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Conferences, Lectures, & Seminars
Events for March

  • ECE Seminar: Protecting User Security and Privacy in Emerging Computing Platforms

    Tue, Mar 01, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Yuan Tian, Assistant Professor, Department of Computer Science, University of Virginia

    Talk Title: Protecting User Security and Privacy in Emerging Computing Platforms

    Abstract: Computing is undergoing a significant shift. First, the explosive growth of the Internet of Things (IoT) enables users to interact with computing systems and physical environments in novel ways through perceptual interfaces (e.g., microphones and cameras). Second, machine learning algorithms collect huge amounts of data and make critical decisions on new computing systems. While these trends bring unprecedented functionality, they also drastically increase the number of untrusted algorithms, implementations, interfaces, and the amount of private data processed by them, endangering user security and privacy. To regulate these security and privacy issues, privacy regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) went into effect. However, there is a huge gap between the desired high-level security/privacy/ethical properties (from regulations, specifications, users' expectations) and low-level real implementations.

    To bridge the gap, my work aims to change how platform architects design secure systems, assist developers by detecting security and privacy violation of implementations and build usable and scalable privacy-preserving systems. In this talk, I will present how my group designs principled solutions to ensure modern and emerging computing platforms' security and privacy. In this talk, I will introduce two developer tools we build to detect security and privacy violations. Using the tools, we found large numbers of policy violations in healthcare voice applications and security property violations in IoT messaging protocol implementations. Additionally, I will discuss our recent work on scalable privacy-preserving machine learning.

    Biography: Yuan Tian is an Assistant Professor of Computer Science at the University of Virginia. Before joining UVA, she obtained her Ph.D. from Carnegie Mellon University in 2017 and interned at Microsoft Research, Facebook, and Samsung Research. Her research interests involve security and privacy and its interactions with computer systems, machine learning, and human-computer interaction. Her current research focuses on developing new computing platforms with strong security and privacy features, particularly in the Internet of Things and mobile systems. Her work has real-world impacts as countermeasures and design changes have been integrated into platforms (such as Android, Chrome, Azure, and iOS), and also impacted the security recommendations of standard organizations such as the Internet Engineering Task Force (IETF). She is a recipient of Google Research Scholar Award 2021, Facebook Research Award 2021, NSF CAREER award 2020, NSF CRII award 2019, Amazon AI Faculty Fellowship 2019, CSAW Best Security Paper Award 2019, and Rising Stars in EECS 2016. Her research has appeared in top-tier venues in security, machine learning, and systems. Her projects have been covered by media outlets such as IEEE Spectrum, Forbes, Fortune, Wired, and Telegraph.

    Host: Host: Dr. Konstantinos Psounis, kpsounis@usc.edu

    Webcast: https://usc.zoom.us/j/97735008231?pwd=WEJCcDJpdnZsaEZxczA0SEtaKzBJdz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/97735008231?pwd=WEJCcDJpdnZsaEZxczA0SEtaKzBJdz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Oded Stein (MIT) - Mathematical Foundations of Robust Geometry and Fabrication

    Tue, Mar 01, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Oded Stein, MIT

    Talk Title: Mathematical Foundations of Robust Geometry and Fabrication

    Series: CS Colloquium

    Abstract: Current geometry methods for creating and manipulating shapes on computers can sometimes be unreliable and fail unpredictably. Such failures make geometry tools hard to use, prevent non-experts from creating geometry on their computers, and limit the use of geometry methods in domains where reliability is critical. We will discuss my recent efforts in proving when existing methods work as intended, my work in making methods more robust to imperfect input, my work in the creation of new reliable tools with mathematical guarantees, and my future efforts towards a reliable geometry pipeline.
    When used for computational fabrication, geometry methods can be expensive, finicky, and require a controlled environment. I will show how simple and economical manufacturing techniques can be used for computational fabrication by exploiting the geometric constraints inherent in specific materials and fabrication methods. We will take a look at how I create geometric tools to design for constrained fabrication techniques, and discuss how computational fabrication can be made both economical as well as accessible in difficult environments.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Oded Stein is a postdoc at MIT at the geometric data processing group. He obtained his MSc from ETH Zurich in 2015, and his PhD from Columbia University in 2020.
    Oded is interested in geometry, computer graphics, and applied mathematics. He works on smoothness energies, partial differential equations, discretization of geometric quantities, and their applications to computer graphics and digital fabrication.


    Host: Jernej Barbic

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Amy Ousterhout (UC Berkeley) - Optimizing CPU Efficiency and Tail Latency in Datacenters

    Tue, Mar 01, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Amy Ousterhout , UC Berkeley

    Talk Title: Optimizing CPU Efficiency and Tail Latency in Datacenters

    Series: CS Colloquium

    Abstract: The slowing of Moore's Law and increased concerns about the environmental impacts of computing are exerting pressure on datacenter operators to use resources such as CPUs and memory more efficiently. However, it is difficult to improve efficiency without degrading the performance of applications.

    In this talk, I will focus on CPU efficiency and how we can increase efficiency while maintaining low tail latency for applications. The key innovation is to reallocate cores between applications on the same server very quickly, every few microseconds. First I will describe Shenango, a system design that makes such frequent core reallocations possible. Then I will show how policy choices for core reallocation and load balancing impact CPU efficiency and tail latency, and present the policies that yield the best combination of both.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Amy is a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She received her PhD in Computer Science from MIT and her BSE in Computer Science from Princeton University. Her research is on operating systems and distributed systems, and focuses on improving the efficiency, performance, and usability of applications in datacenters. She is a recipient of a Jacobs Presidential Fellowship at MIT, an NSF Graduate Research Fellowship, and a Hertz Foundation Fellowship.

    Host: Barath Raghavan

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ISE 651 Epstein Seminar

    Tue, Mar 01, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Albert Shih, Professor, Dept of Mechanical Engineering, University of Michigan

    Talk Title: TBD

    Host: Prof. Yong Chen and Prof. Qiang Huang

    Location: Online/Zoom

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • Mork Family Department Seminar - Thi Vo

    Tue, Mar 01, 2022 @ 04:00 PM - 05:15 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Thi Vo, University of Michigan

    Talk Title: First Principles Driven Materials Design: From Building Blocks to Superlattices

    Host: Professor A.Hodge

    Location: Social Sciences Building (SOS) - B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • CS Colloquium: Dmitry Berenson (University of Michigan) - Learning Where to Trust Unreliable Dynamics Models for Motion Planning and Manipulation

    Tue, Mar 01, 2022 @ 04:15 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Dmitry Berenson, University of Michigan

    Talk Title: Learning Where to Trust Unreliable Dynamics Models for Motion Planning and Manipulation

    Series: Computer Science Colloquium

    Abstract: **New time: 4:15pm-5:20pm, SGM 124**

    The world outside our labs seldom conforms to the assumptions of our models. This is especially true for dynamics models used in control and motion planning for complex high-DOF systems like deformable objects. We must develop better models, but we must also accept that, no matter how powerful our simulators or how big our datasets, our models will sometimes be wrong. This talk will present our recent work on using unreliable dynamics models for motion planning and manipulation. Given a dynamics model, our methods learn where that model can be trusted given either batch data or online experience. These approaches allow imperfect dynamics models to be useful for a wide range of tasks in novel scenarios, while requiring much less data than baseline methods. This data-efficiency is a key requirement for scalable and flexible motion planning and manipulation capabilities.

    Prof. Dmitry Berenson will give his talk in person at SGM 124 and we will also host the talk over Zoom.

    Register in advance for this webinar at:
    https://usc.zoom.us/webinar/register/WN_prfowdXjR7iOn1mPLTnXog

    After registering, attendees will receive a confirmation email containing information about joining the webinar.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Dmitry Berenson is an Associate Professor in Electrical Engineering and Computer Science and the Robotics Institute at the University of Michigan, where he has been since 2016. Before coming to University of Michigan, he was an Assistant Professor at WPI (2012-2016). He received a BS in Electrical Engineering from Cornell University in 2005 and received his Ph.D. degree from the Robotics Institute at Carnegie Mellon University in 2011, where he was supported by an Intel PhD Fellowship. He was also a post-doc at UC Berkeley (2011-2012). He has received the IEEE RAS Early Career Award and the NSF CAREER award. His current research focuses on robotic manipulation, robot learning, and motion planning.


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_prfowdXjR7iOn1mPLTnXog

    Location: Seeley G. Mudd Building (SGM) - 124

    WebCast Link: https://usc.zoom.us/webinar/register/WN_prfowdXjR7iOn1mPLTnXog

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • ECE Seminar: Full Stack Deep Learning at the Edge

    Wed, Mar 02, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Amir Gholami, Research Scientist, RiseLab and BAIR at UC Berkeley

    Talk Title: Full Stack Deep Learning at the Edge

    Abstract: An important next milestone in machine learning is to bring intelligence to the edge without relying on the computational power of the cloud. This could lead to more reliable, lower latency, and privacy preserving AI for a wide range of applications. However, state-of-the-art NN models require prohibitive amounts of compute, memory, and energy resources which is often not available at the edge. Addressing these challenges without compromising on accuracy, requires a multi-faceted approach, including hardware-aware model compression and accelerator co-design.

    In this talk, I will first discuss a novel hardware-aware method for neural network quantization and pruning that achieves optimal trade-off between accuracy, latency, and model size. In particular, I will discuss a new Hessian Aware Quantization (HAWQ) method that relies on second-order information to perform low precision quantization of the model with minimal generalization loss. I will present extensive testing of the method on different learning tasks including various models for image classification, object detection, natural language processing, and speech recognition showing that HAWQ exceeds previous baselines. I will then present a recent extension of this method which allows integer-only inference for the end-to-end computations, enabling efficient deployment on fixed-point hardware. Finally, I will discuss a full-stack hardware-aware neural network architecture and accelerator design, which enables adapting the model architecture and the accelerator parameters to achieve optimal performance.

    Related paper:
    ICML'21: HAWQ-V3: Dyadic Neural Network Quantization
    ICML'21: I-BERT: Integer-only BERT Quantization

    Biography: Amir Gholami is a research scientist in RiseLab and BAIR at UC Berkeley. He received his PhD from UT Austin, working on large scale 3D image segmentation, a research topic which received UT Austin's best doctoral dissertation award in 2018. He is a Melosh Medal finalist, the recipient of best student paper award in SC'17, Gold Medal in the ACM Student Research Competition, best student paper finalist in SC'14, as well as Amazon Machine Learning Research Award in 2020. He was also part of the Nvidia team that for the first time made low precision neural network training possible (FP16), enabling more than 10x increase in compute power through tensor cores. That technology has been widely adopted in GPUs today. Amir's current research focuses on efficient AI, AutoML, and scalable training of Neural Network models.


    Host: Host: Dr. Massoud Pedram, pedram@usc.edu

    Webcast: https://usc.zoom.us/j/95064180366?pwd=SVJ3VzZ3aGNRKzNLdmJQeGRhdzBUZz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/95064180366?pwd=SVJ3VzZ3aGNRKzNLdmJQeGRhdzBUZz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

    Wed, Mar 02, 2022 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dimos V. Dimarogonas, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology

    Talk Title: Multi-robot Task Planning and Control Under Spatiotemporal Specifications

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: Multi-robot task planning and control under temporal logic specifications has been gaining increasing attention in recent years due to its applicability among others in autonomous systems, manufacturing systems, service robotics and intelligent transportation. Initial approaches considered qualitative logics, such as Linear Temporal Logic, whose automata representation facilitates the direct use of model checking tools for correct-by-design control synthesis. In many real world applications however, there is a need to quantify spatial and temporal constraints, e.g., in order to include deadlines and separation assurance bounds. This led to the use of quantitative logics, such as Metric Interval and Signal Temporal Logic, to impose such spatiotemporal constraints. However, the lack of automata representations for such specifications hinders the direct use of model checking tools. Motivated by this, the use of transient control methodologies that fulfil the aforementioned qualitative constraints becomes evident. In this talk, we review some of our recent results in applying transient control techniques, and in particular Model Predictive Control, Barrier Certificates based design and Prescribed Performance Control, to distributed multi-robot task planning under spatiotemporal specifications. The results are supported by relevant experimental validations.

    Biography: Dimos V. Dimarogonas received the Diploma in Electrical and Computer Engineering in 2001 and the Ph.D. in Mechanical Engineering in 2007, both from National Technical University of Athens (NTUA), Greece. Between 2007 and 2010, he held postdoctoral positions at the KTH Royal Institute of Technology, Dept of Automatic Control and MIT, Laboratory for Information and Decision Systems (LIDS). He is currently Professor at the Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, at KTH. His current research interests include multi-agent systems, hybrid systems and control, robot navigation and manipulation, human-robot-interaction and networked control. He serves in the Editorial Board of Automatica and the IEEE Transactions on Control of Network Systems and is a Senior Member of IEEE. He is a recipient of the ERC Starting Grant in 2014, the ERC Consolidator Grant in 2019, and the Knut och Alice Wallenberg Academy Fellowship in 2015.

    Host: Pierluigi Nuzzo, nuzzo@usc.edu

    Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • AME Seminar

    Wed, Mar 02, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Michael Burke, Columbia University

    Talk Title: Non-Equilibrium Behavior in Combustion, Planetary Atmospheres, and Compressible Flows

    Abstract: Chemically reacting flows are often interpreted and computed under the premise that all chemical species have a range of energies in their rotational and vibrational modes that are well described by the Boltzmann or thermal distribution at the local temperature. Of course, breakdown in this premise can occur naturally as a result of chemical reactions, light absorption, and/or shock waves. The manifestations of this breakdown on unimolecular reactions, where non-thermally distributed molecular ensembles dissociate, are well known to give rise to pressure-dependent reactions in combustion, photochemical reactions in the Earth atmosphere, and induction time lags in reactions following shock waves. By contrast, manifestations of non-equilibrium behavior on bimolecular reactions, where non-thermally distributed molecules react with other species, are generally less understood and historically less appreciated. Here, I describe three distinct tales of such non-equilibrium behavior across varied application domains. In particular, I present results from ab initio master equation calculations that shed light on previous hypotheses and experimental observations and reveal new processes involving non-equilibrium induced by chemistry in combustion, photons in the Earth atmosphere, and shock waves in compressible flows. Namely, the rovibrationally excited ephemeral complexes, formed from association of two molecules, with a third molecule give rise to a fourth, long-forgotten type of phenomenological reaction, involving three chemical reactants, that impacts macroscopic combustion behavior; the vibrationally excited complexes, formed upon photon absorption, collide with oxygen to produce radicals even for low photon energies in the Earth troposphere; and the rovibrationally cold molecular ensembles encountered following shock waves not only slow the reaction timescales but also change the main chemical pathways.

    Biography: Michael Burke is an Associate Professor of Mechanical Engineering at Columbia University, where he also holds affiliate appointments in Chemical Engineering and the Data Science Institute. Prior to joining Columbia in 2014, Burke earned his Ph.D. in Mechanical and Aerospace Engineering in 2011 at Princeton University, where he was a Wallace Memorial Honorific Fellow, and he worked as a Directors Postdoctoral Fellow in the Chemical Sciences and Engineering Division at Argonne National Laboratory. Burke is a recipient of the National Science Foundations CAREER award, the Combustion Institutes Research Excellence Award, the Combustion Institutes Hiroshi Tsuji Early Career Researcher Award, and the American Chemical Societys PRF Doctoral New Investigator Award. His publications have been featured in the News and Views section of Nature Chemistry, selected as the Feature Article in Combustion and Flame, and chosen for the Distinguished Paper Award at the 31st International Symposium on Combustion. His research combines physics and data across multiple scales to unravel and predict outcomes of complex reacting systems in varied application domains with major emphases on theoretical chemistry of nonequilibrium processes, multiscale datadriven modeling, and highthroughput experiments selected by optimal design.

    Host: AME Department

    More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • ECE Seminar: Foundations of Trusted AI for Molecular Inference: the Role of Sparsity

    Thu, Mar 03, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Amirali Aghazadeh, Postdoctoral Researcher, EECS Department, University of California, Berkeley

    Talk Title: Foundations of Trusted AI for Molecular Inference: the Role of Sparsity

    Abstract: Recent breakthroughs in artificial intelligence (AI) have enabled accurate prediction of protein structures from their sequences and have opened up new avenues for the engineering of proteins, drugs, and molecules with advanced and novel functional properties. However, despite their high predictive power, AI models do not provide a mechanistic understanding of interactions that give rise to many functional properties. Moreover, their generalization power has remained limited for novel and rapidly evolving molecules for which sufficient sequence data is not available.

    In this talk, I will describe how I developed a foundation for trusted AI in molecular inference. Key to my approach is the observation that the combinatorial landscapes of molecular properties reside in low dimensional subspaces characterized by sparse high order non-linear interactions. I will show how we can leverage this sparsity prior and develop new algorithms rooted in signal processing, coding and graph theory to efficiently explain, regularize, and build molecular AI models. My algorithms have resulted in a drastic reduction in the number of sequences required to infer functional properties in proteins and an improved understanding of high order interactions in the DNA repair process. I will conclude by describing how my works set the computational and statistical foundation for engineering programmable molecular machines.

    Biography: Amirali Aghazadeh is a postdoctoral researcher in the Electrical Engineering and Computer Science department at the University of California, Berkeley, working with Kannan Ramchandran. Prior to that, he was a postdoctoral researcher at Stanford University with David Tse after receiving his PhD degree in Electrical and Computer Engineering from Rice University with Richard Baraniuk. His research interest is at the interface of large-scale machine learning, signal processing, and molecular engineering. He is the recipient of the Hershel M. Rich Invention Award for his thesis on universal molecular diagnostics as well as the Texas Instruments Fellowship. He received his Bachelor's degree in Electrical Engineering from Sharif University of Technology.

    Host: Dr. Sandeep Gupta, sandeep@usc.edu

    Webcast: https://usc.zoom.us/j/98808075733?pwd=MktQYUc0Z2lhZ3NZd09uTURYUFBzUT09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/98808075733?pwd=MktQYUc0Z2lhZ3NZd09uTURYUFBzUT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Saining Xie (Facebook AI Research (FAIR)) - Towards Scalable Representation Learning for Visual Recognition

    Thu, Mar 03, 2022 @ 10:00 AM - 11:00 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Saining Xie, Facebook AI Research (FAIR)

    Talk Title: Towards Scalable Representation Learning for Visual Recognition

    Series: CS Colloquium

    Abstract: A powerful biological and cognitive representation is essential for humans' remarkable visual recognition abilities. Deep learning has achieved unprecedented success in a variety of domains over the last decade. One major driving force is representation learning, which is concerned with learning efficient, accurate, and robust representations from raw data that are useful for a downstream classifier or predictor. A modern deep learning system is composed of two core and often intertwined components: 1) neural network architectures and 2) representation learning algorithms. In this talk, we will present several studies in both directions. On the neural network modeling side, we will examine modern network design principles and how they affect the scaling behavior of ConvNets and recent Vision Transformers. Additionally, we will demonstrate how we can acquire a better understanding of neural network connectivity patterns through the lens of random graphs. In terms of representation learning algorithms, we will discuss our recent efforts to move beyond the traditional supervised learning paradigm and demonstrate how self-supervised visual representation learning, which does not require human annotated labels, can outperform its supervised learning counterpart across a variety of visual recognition tasks. The talk will encompass a variety of vision application domains and modalities (e.g. 2D images and 3D scenes). The goal is to show existing connections between the techniques specialized for different input modalities and provide some insights about diverse challenges that each modality presents. Finally, we will discuss several pressing challenges and opportunities that the "big model era" raises for computer vision research.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Saining Xie is a research scientist at Facebook AI Research (FAIR). He received his Ph.D. and M.S. degrees in computer science from the University of California San Diego, advised by Zhuowen Tu. Prior to that, he received his Bachelor's degree from Shanghai Jiao Tong University. He has broad research interests in deep learning and computer vision, with a focus on developing deep representation learning techniques to push the boundaries of core visual recognition. He is a recipient of the Marr Prize Honorable Mention at ICCV 2015.

    Host: Ramakant Nevatia

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Lars Lindemann (University of Pennsylvania) - Safe AI-Enabled Autonomy

    Thu, Mar 03, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Lars Lindemann, University of Pennsylvania

    Talk Title: Safe AI-Enabled Autonomy

    Series: CS Colloquium

    Abstract: AI-enabled autonomous systems show great promise to enable many future technologies such as autonomous driving, intelligent transportation, and robotics. Over the past years, there has been tremendous success in the development of autonomous systems, which was especially accelerated by the computational advances in machine learning and AI. At the same time, however, new fundamental questions were raised regarding the safety and reliability of these increasingly complex systems that often operate in uncertain and dynamic environments. In this seminar, I will provide new insights and exciting opportunities to address these challenges.

    In the first part of the seminar, I will present a data-driven optimization framework to learn safe control laws for dynamical systems. For most safety-critical systems such as self-driving cars, safe expert demonstrations in the form of system trajectories that show safe system behavior are readily available or can easily be collected. At the same time, accurate models of these systems can be identified from data or obtained from first order modeling principles. To learn safe control laws, I present a constrained optimization problem with constraints on the expert demonstrations and the system model. Safety guarantees are provided in terms of the density of the data and the smoothness of the system model. Two case studies on a self-driving car and a bipedal walking robot illustrate the presented method. In the past years, it was shown that neural networks are fragile and that their use in AI-enabled systems has resulted in systems taking excessive risk. The second part of the seminar is motivated by this fact and presents a data-driven verification framework to quantify and assess the risk of AI-enabled systems. I particularly show how risk measures, classically used in finance, can be used to quantify the risk of not being robust to failure, and how we can estimate this risk from data. We will compare and verify four different neural network controllers in terms of their risk for a self-driving car. I will conclude by sharing exciting research directions in this area.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Lars Lindemann is currently a Postdoctoral Researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.Sc. degrees in Electrical and Information Engineering and his B.Sc. degree in Engineering Management in 2014 from the Christian-Albrechts-University (CAU), Kiel, Germany. He received his M.Sc. degree in Systems, Control and Robotics in 2016 and his Ph.D. degree in Electrical Engineering in 2020, both from KTH Royal Institute of Technology, Stockholm, Sweden. His current research interests include systems and control theory, formal methods, data-driven control, and autonomous systems. Lars received the Outstanding Student Paper Award at the 58th IEEE Conference on Decision and Control and was a Best Student Paper Award Finalist at the 2018 American Control Conference. He also received the Student Best Paper Award as a co-author at the 60th IEEE Conference on Decision and Control.

    Host: Jyo Deshmukh

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • Astani Civil and Environmental Engineering Seminar

    Thu, Mar 03, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Joshua Jack, Postdoctoral Research Scholar, Princeton University

    Talk Title: Engineering a New Circular Economy: Waste CO2 valorization and resource recovery towards an improved water-energy-climate nexus

    Abstract: Understanding and advancing the water-energy-climate nexus is key to mitigating the immense threats of climate change and solving many of the related environmental issues we face today. Due to the rapid decrease in the cost of renewable energy, it is now practical to design devices that use renewable electrons to drive the transformation of CO2 and other waste feedstock (wastewater, food waste, biomass) into high-value products while also recovering important resources such as water, nutrients, and energy. Overall, these new sustainable technologies can help us decarbonize various sectors and enable a new circular economy. This presentation will discuss opportunities to leverage cutting-edge hybrid electrochemical-biological technologies in diverse environmental applications including wastewater reclamation, water reuse, remediation, desalination, and CO2 capture and conversion. Current lab scale experiments have demonstrated excellent production rates, titer,and energy efficiencies. Efforts towards improving scalability, expanding the portfolio of products, and implementing new types of waste streams are on going.

    Biography: Joshua Jack is a postdoctoral research scholar in the Andlinger Center for Energy and Environment and the Civil and Environmental Engineering department at Princeton University. Jack previously earned a bachelor degree in Civil and Environmental Engineering from the University of Massachusetts, Amherst and holds a doctoral degree in Environmental Engineering from the University of Colorado, Boulder. During his graduate studies, Jack obtained extensive interdisciplinary research experience at both the DOE-National Renewable Energy Laboratory and NASA Langley Research Center, and has received numerous awards including a NASA Outstanding Research Award and NSF Fellowship. Jack current research focuses on energy and resource recovery as part of a sustainable water-energy-climate nexus with a special focus on process design of bioelectrochemical technologies toward scalable CO2 valorization and water treatment. Jack collaborates with many researchers from the Department of Chemical and Biological Engineering as well as various DOE laboratories and private companies such as Shell Energy. Jack has recently published in many highly cited journals including Applied Energy and Green Chemistry and plans to begin a tenure-track academic position in the near future.

    Host: D. Amy Childress

    Webcast: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659 Passcode: 975701

    Location: Ronald Tutor Hall of Engineering (RTH) - 526

    WebCast Link: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659 Passcode: 975701

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • ECE Seminar: New Generation Photoacoustic Imaging: From benchtop wholebody imagers to wearable sensors

    Fri, Mar 04, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Lei Li, Postdoctoral Scholar, Department of Medical Engineering, California Institute of Technology

    Talk Title: New Generation Photoacoustic Imaging: From benchtop wholebody imagers to wearable sensors

    Abstract: Whole-body imaging has played an indispensable role in preclinical research by providing high-dimensional physiological, pathological, and phenotypic insights with clinical relevance. Yet, pure optical imaging suffers from either shallow penetration or a poor depth-to-resolution ratio, and non-optical techniques for whole-body imaging of small animals lack either spatiotemporal resolution or functional contrast. We have developed a dream machine, demonstrating that a stand-alone single-impulse panoramic photoacoustic computed tomography (SIP-PACT) mitigates these limitations by combining high spatiotemporal resolution, deep penetration, anatomical, dynamical and functional contrasts, and full-view fidelity. SIP-PACT has imaged in vivo whole-body dynamics of small animals in real time, mapped whole-brain functional connectivity, and tracked circulation tumor cells without labeling. It also has been scaled up for human breast cancer diagnosis. SIP-PACT opens a new window for medical researchers to test drugs and monitor longitudinal therapy without the harm from ionizing radiation associated with X-ray CT, PET, or SPECT. Genetically encoded photochromic proteins benefit photoacoustic computed tomography (PACT) in detection sensitivity and specificity, allowing monitoring of tumor growth and metastasis, multiplexed imaging of multiple tumor types at depths, and real-time visualization of protein-protein interactions in deep-seated tumors. Integrating the newly developed microrobotic system with PACT permits deep imaging and precise control of the micromotors in vivo and promises practical biomedical applications, such as drug delivery. In addition, to shape the benchtop PACT systems toward portable and wearable devices with low cost without compromising the imaging performance, we recently have developed photoacoustic topography through an ergodic relay, a high-throughput imaging system with significantly reduced system size, complexity, and cost, enabling wearable applications. As a rapidly evolving imaging technique, photoacoustic imaging promises preclinical applications and clinical translation.

    Biography: Lei Li obtained his Ph.D. degree from the Department of Electrical Engineering at California Institute of Technology (Caltech) in 2019. He received his MS degrees at Washington University in St. Louis in 2016. He is currently a postdoctoral scholar in the Department of Medical Engineering at Caltech. His research focuses on developing next-generation medical imaging technology for understanding the brain better, diagnosing early-stage cancer, and wearable monitoring of human vital signs. He was selected as a TED fellow in 2021 and a rising star in Engineering in Health by Columbia University and Johns Hopkins University (2021). He received the Charles and Ellen Wilts Prize from Caltech in 2020 and was selected as one of the Innovators Under 35 by MIT Technology Review in 2019. He is also a two-time winner of the Seno Medical Best Paper Award granted by SPIE (2017 and 2020, San Francisco).

    Host: Dr. Justin Haldar, jhaldar@usc.edu

    Webcast: https://usc.zoom.us/j/97334155702?pwd=SFlvZ2Y0b3pHMEFxalhNdmxvdU5odz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/97334155702?pwd=SFlvZ2Y0b3pHMEFxalhNdmxvdU5odz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Sai-Kit Yeung (Hong Kong University of Science and Technology (HKUST).) - Computer Vision and Graphics for Real-World Challenges

    Fri, Mar 04, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Sai-Kit Yeung, Hong Kong University of Science and Technology (HKUST)

    Talk Title: Computer Vision and Graphics for Real-World Challenges

    Series: CS Colloquium

    Abstract: With the recent advancements in sensing technology and pervasive computing devices, the fields of computer vision and graphics are witnessing renewed importance in addressing real-world problems. In this talk, I will be discussing my research relating to 3D reconstruction, scene understanding, content generation, and fabrication. My talk will also overview ways this core research can be used in multidisciplinary projects involving city planning, seafloor surveying, and fishery design. I will conclude my talk by discussing potential collaborative projects between computer vision, graphics, and other disciplines to address challenging issues related to human empowerment and the building of sustainable environments.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Dr. Sai-Kit Yeung is an Associate Professor at the Division of Integrative Systems and Design (ISD) and the Department of Computer Science and Engineering (CSE) at the Hong Kong University of Science and Technology (HKUST). His research interests include 3D vision and graphics, content generation, fabrication, and novel computational techniques and integrative systems for marine-related problems.

    Dr. Yeung has published extensively in premiere computer vision and graphics venues including numerous full oral papers in CVPR, ICCV, and AAAI. His work has received best paper honorable mention awards at ICCP 2015 and 3DV 2016. He has served as a Senior Program Committee member in IJCAI and AAAI, and as a Course Chair for SIGGRAPH Asia 2019. In addition, he regularly serves as a Technical Papers Committee member for SIGGPAPH & SIGGRAPH Asia and is currently an Associate Editor of the ACM Transactions on Graphics (TOG).


    Host: Jernej Barbic

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • CAIS Seminar: Patrick Fowler - Equity in Data-Driven Policies

    Mon, Mar 07, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Patrick Fowler,

    Talk Title: Equity in Data-Driven Policies

    Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series

    Abstract: Dr. Fowler will present work investigating group fairness in algorithmic decision-making. The team uses administrative records on homeless service delivery to demonstrate inherent tradeoffs in fairness that depend on the operationalization of equity. The findings inform data-driven policy-making in homelessness and social services broadly.

    Register in advance for this webinar at:

    https://usc.zoom.us/webinar/register/WN_b3XxZm-rRfibw54-40F-UA

    After registering, attendees will receive a confirmation email containing information about joining the webinar.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Dr. Patrick Fowler's research centers on homelessness prevention and the negative consequences of homelessness on youth, families, and communities. His recent research focuses on homelessness among youth aging out of foster care and child maltreatment prevention among families experiencing homelessness.

    Using linked administrative data, Dr. Fowler designs and tests big data applications to improve the fairness and efficiency of homelessness services delivery. He employs a complex systems approach to create developmentally appropriate and culturally tailored responses to homelessness.


    Host: USC Center for Artificial Intelligence in Society (CAIS)

    Webcast: https://usc.zoom.us/webinar/register/WN_b3XxZm-rRfibw54-40F-UA

    Location: Online - Zoom Webinar

    WebCast Link: https://usc.zoom.us/webinar/register/WN_b3XxZm-rRfibw54-40F-UA

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • ECE Seminar: Rethinking Hardware for Cryptography, Security, and Privacy

    Tue, Mar 08, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Daniel Moghimi, Computer Science and Engineering, UC San Diego

    Talk Title: Rethinking Hardware for Cryptography, Security, and Privacy

    Abstract: Modern computers run on top of complex processors, but complexity is the worst enemy of security. Scientists and engineers have consistently tried to develop secure software systems for decades. However, my work shows that new classes of vulnerabilities in complicated processors can break the security guarantees provided by software systems, cryptographic protocols, and privacy technologies. In this talk, I will give an overview of my work on discovering, evaluating, and mitigating such vulnerabilities. First, I will talk about side-channel attacks on cryptographic implementations. Second, I will discuss vulnerabilities at the microarchitecture level. Finally, I highlight my future work on improving security and privacy through automated testing for hardware vulnerabilities and hardware-software co-design.

    Biography: Daniel Moghimi (https://moghimi.org) is a postdoctoral fellow in Computer Science and Engineering at UCSD. Previously, he received his MS.c in CS and Ph.D. in ECE from Worcester Polytechnic Institute. He develops new techniques and tools to discover new classes of vulnerabilities in hardware, evaluate their impact on software, particularly cryptography, and defend against these vulnerabilities. His work has improved the security of commodity processors and cryptographic products used by billions of users worldwide. Several of his publications have been covered by the news media such as Forbes, Wired, and The Register.

    Host: Dr. Salman Avestimehr, avestime@usc.edu

    Webcast: https://usc.zoom.us/j/96000769674?pwd=ZzJXNmgyNTY1dmo4c21sWXZpSjFuQT09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/96000769674?pwd=ZzJXNmgyNTY1dmo4c21sWXZpSjFuQT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Fei Miao (University of Connecticut) - Learning, Optimization and Control for Efficiency and Security of Cyber-Physical Systems

    Tue, Mar 08, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Fei Miao, University of Connecticut

    Talk Title: Learning, Optimization and Control for Efficiency and Security of Cyber-Physical Systems

    Series: CS Colloquium

    Abstract: Ubiquitous sensing enables large-scale multi-source data of cyber-physical systems (CPS) collected in real-time and poses both challenges and opportunities for a paradigm-shift to data-driven CPS. For instance, how to capture the complexity and analyze the dynamical state information from data, and make decisions to improve safety, efficiency and security of the networked CPS is still challenging. In this talk, we present our research that integrates optimization, machine learning, control, and game theory to address these challenges, including theoretical contributions, algorithmic design, and experimental validations. We first present data-driven distributionally robust optimization (DRO) methods for CPS efficiency, with application on smart city resource allocation. We design algorithms to construct the uncertainty sets of the model prediction based on spatial temporal data. We prove the computationally tractable forms or equivalent convex optimization forms of the DRO problems to guarantee the worst-case expected cost of real-time decisions. We show the improvement of autonomous mobility-on-demand (AMoD) service fairness and efficiency based on large-scale dataset. Second, we summarize our research contribution for CPS security. We mainly present a hybrid state stochastic game model to guarantee the worst-case cost of the system, and a proved suboptimal algorithm to calculate the mixed policies. Finally, based on our active awarded projects, we briefly discuss future research directions on robust multi-agent reinforcement learning and data-driven robust optimization based decision-making, to address CPS safety, efficiency, and security challenges.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Fei Miao is an Assistant Professor of the Department of Computer Science & Engineering, a Courtesy Faculty of the Department of Electrical & Computer Engineering, University of Connecticut since 2017. She is also affiliated to the Institute of Advanced Systems Engineering and Eversource Energy Center. Her research interests lie in the optimization, machine learning, control, and game theory, to address safety, efficiency, and security challenges of cyber-physical systems. She received the Ph.D. degree and the Best Doctoral Dissertation Award in Electrical and Systems Engineering from the University of Pennsylvania in 2016. She received the B.S. degree majoring in Automation from Shanghai Jiao Tong University. She was a postdoc researcher at the GRASP Lab and the PRECISE Lab of Upenn from 2016 to 2017. Dr. Miao is a receipt of the NSF CAREER Award, the title of the project is "Distributionally Robust Learning, Control, and Benefits Analysis of Information Sharing for Connected and Autonomous Vehicles". Dr. Miao has also received a couple of other awards from NSF, including awards from the Smart & Autonomous Systems, the Cyber-Physical Systems, and the Smart & Connected Communities programs. She received Best Paper Award and Best Paper Award Finalist at the ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) in 2021 and 2015, respectively.

    Host: Jyo Deshmukh

    Location: 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ISE 651 Epstein Seminar

    Tue, Mar 08, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Adam Elmachtoub, Associate Professor, Dept. of Industrial Engineering & Ops Research, Columbia University

    Talk Title: Contextual Optimization: Bridging Machine Learning and Operations

    Host: Dr. Phebe Vayanos

    More Information: March 8, 2022.pdf

    Location: Online/Zoom

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • Mork Family Department Seminar - Salva Salmani-Rezaie

    Tue, Mar 08, 2022 @ 04:00 PM - 05:15 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Salva Salmani-Rezaie, University of California, Santa Barbara

    Talk Title: Atomic Scale Understanding of Ferroelectricity and Superconductivity in SrTiO3

    Host: Professor A.Hodge

    Location: Social Sciences Building (SOS) - B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • CS Colloquium: Christoforos Mavrogiannis (University of Washington) - Building Robots that Humans Accept

    Wed, Mar 09, 2022 @ 10:00 AM - 11:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Christoforos Mavrogiannis, University of Washington

    Talk Title: Building Robots that Humans Accept

    Series: CS Colloquium

    Abstract: Robotics has transformed sectors like manufacturing and fulfillment which now rely on robots to meet their goals. Conventionally, these robots operate in isolation from humans due to safety and efficiency considerations. Lately, there have been efforts towards bringing robots closer to humans to assist in everyday-life tasks, enhance productivity, and augment human capabilities. Despite these efforts, robotic technology has not reached widespread acceptance outside of factories; robot autonomy is often not robust, producing new problems that outweigh its benefits for users. Inspired by theories of technology acceptance, my research strives to develop highly functional, safe, and comfortable robots that humans accept. In this talk, I argue that the path towards acceptance requires imbuing robots with a deeper understanding of how users perceive and react to them. To motivate this perspective, I will share insights on robot navigation in dynamic environments, a fundamental task with many crucial applications ranging from collaborative manufacturing to warehouse automation and healthcare. I will describe a human-inspired algorithmic framework for crowd navigation, highlighting how mathematical abstractions of multiagent behavior enable safe, efficient, and positively perceived robot motion across a series of extensive empirical studies involving real robots and human subjects. Inspired by field-deployment challenges, I will then present a data-driven framework that enables robots to recover from failure via bystander help without overloading users. I will conclude with future directions on the development of shared and full robot autonomy that explicitly reasons about human perceptions to produce safe, trustworthy, and comfortable robot behavior.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Christoforos Mavrogiannis is a postdoctoral Research Associate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working with Prof. Siddhartha Srinivasa. His interests lie at the intersection of robotics, human-robot interaction, and artificial intelligence. His research often draws insights from algebraic topology and dynamical systems, tools from machine learning, planning and control, and inspiration from social sciences. He is a full-stack roboticist, passionate about real-world deployment of robot systems, and extensive benchmarking with users. He has been a best-paper award finalist at the ACM/IEEE International Conference on Human-Robot Interaction (HRI), and selected as a Pioneer at the HRI and RSS conferences. He has also led open-source initiatives (Openbionics, MuSHR), for which he has been a finalist for the Hackaday Prize and a winner of the Robotdalen International Innovation Award. His work has received coverage from many media outlets including Wired, IEEE Spectrum, GeekWire, RoboHub, and the Hellenic Broadcasting Corporation. Christoforos holds M.S. and Ph.D. degrees from Cornell University, and a Diploma in mechanical engineering from the National Technical University of Athens.


    Host: Jesse Thomason

    Location: Ronald Tutor Hall of Engineering (RTH) - 109

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ECE Seminar: Algebraic Neural Networks: Stability to Deformations

    Wed, Mar 09, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Alejandro Parada-Mayorga, Postdoctoral Researcher, Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia

    Talk Title: Algebraic Neural Networks: Stability to Deformations

    Abstract: Convolutional architectures play a central role on countless scenarios in machine learning, and the numerical evidence that proves the advantages of using them is overwhelming. Theoretical insights have provided solid explanations about why such architectures work well. These analysis apparently different in nature, have been performed considering signals defined on different domains and with different notions of convolution, but with remarkable similarities in the final results, posing then the question of whether there exists an explanation for this at a more structural level. In this talk we provide an affirmative answer to this question with a first principles analysis introducing algebraic neural networks (AlgNNs), which rely on algebraic signal processing and algebraic signal models. In particular, we study the stability properties of algebraic neural networks showing that stability results for traditional CNNs, graph neural networks (GNNs), group neural networks, graphon neural networks, or any formal convolutional architecture, can be derived as particular cases of our results. This shows that stability is a universal property - at an algebraic level - of convolutional architectures, and this also explains why the remarkable similarities we find when analyzing stability for each particular type of architecture.

    Biography: Alejandro Parada-Mayorga (alejopm@seas.upenn.edu) received his B.Sc. and M.Sc. degrees in electrical engineering from Universidad Industrial de Santander, Colombia, in 2009 and 2012, respectively, and his Ph.D. degree in electrical engineering from the University of Delaware, Newark, 2019. Currently, he is a postdoctoral researcher at the University of Pennsylvania, Philadelphia, under the supervision of Prof. Alejandro Ribeiro. His research interests include algebraic signal processing, algebraic neural networks, graph neural networks, graph signal processing, and applications of representation theory of algebras and category theory.

    Host: Dr. Shri Narayanan, shri@ee.usc.edu

    Webcast: https://usc.zoom.us/j/92088625170?pwd=enhYNUpicEYvS0R5SEViVVBobjQ1dz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/92088625170?pwd=enhYNUpicEYvS0R5SEViVVBobjQ1dz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

    Wed, Mar 09, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Swarat Chaudhuri, Computer Science Department, The University of Texas at Austin

    Talk Title: Neurosymbolic Programming

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: I will speak about Neurosymbolic programming, an emerging research area that bridges the fields of deep learning and program synthesis. Like in classic machine learning, the goal here is to learn functions from data. However, these functions are represented as programs that can use neural modules in addition to symbolic primitives and are induced using a combination of symbolic search and gradient-based optimization. Neurosymbolic programming can offer multiple advantages over end-to-end deep learning. Programs can sometimes naturally represent long-horizon, procedural tasks that are difficult to perform using deep networks. Neurosymbolic representations are also, commonly, easier to interpret and formally verify than neural networks. The restrictions of a programming language can serve as a form of regularization and lead to more generalizable and data-efficient learning. Compositional programming abstractions can also be a natural way of reusing learned modules across learning tasks.

    In the talk, I will illustrate some of the potential benefits of research in this area. I will also categorize the main ways in which symbolic and neural learning techniques come together here. I will conclude with a discussion of the open technical challenges in the field.


    Biography: Swarat Chaudhuri (http://www.cs.utexas.edu/~swarat) is an Associate Professor of Computer Science and the director of the Trishul laboratory at UT Austin. His research lies at the interface of programming languages, logic, and machine learning. Through a synthesis of ideas from these areas, he seeks to develop a new generation of intelligent systems that are designed to be reliable, transparent, secure, and that can solve complex procedural tasks beyond the scope of contemporary AI.

    Host: Pierluigi Nuzzo

    Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • AME Seminar

    Wed, Mar 09, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Talk Title: Data-driven discovery of governing equations with deep learning and sparse identification techniques

    Abstract: Machine learning techniques promise to offer the ultimate form of automation, particularly when applied to computational modeling and simulation. As a consequence, the computational scientist's narrative now revolves around discovering physics directly from data, with as little assumptions about the underlying physical system as possible. I briefly go over the latest attempts to accomplish this goal and focus on my recent work in combining deep learning with sparse identification of differential equations. First, I show how probability distribution function (PDF) equations can be inferred from Monte Carlo simulations for coarse-graining and closure approximations. Second, I present our latest results on discovering dimensionless groups from data, using the Buckingham Pi theorem as a constraint. And third, I go over the deep delay autoencoder algorithm that reconstructs high dimensional models from partial measurements as motivated by Takens' embedding theorem. I finally highlight the limitations of these methods and propose a few directions for future research.

    Biography: Joseph Bakarji is currently a postdoctoral fellow in the department of mechanical engineering at the University of Washington, working with Steven Brunton and Nathan Kutz. He received his PhD in 2020 from Stanford University where he developed multiscale stochastic models for granular materials and data-driven closure models for uncertainty quantification. Joseph received the Henry J. Ramey, Jr. and the Frank G. Miller fellowship awards in 2018 and 2020 respectively. His current research focuses on combining deep learning and sparse identification methods, to discover interpretable physical models in complex systems from data.

    More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • ECE Seminar: A Variegated Study of 5G Services: Challenges, Opportunities, and Application Innovations

    Thu, Mar 10, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Feng Qian, Associate Professor, Department of Computer Science and Engineering, University of Minnesota - Twin Cities

    Talk Title: A Variegated Study of 5G Services: Challenges, Opportunities, and Application Innovations

    Abstract: 5G is expected to support sub-millisecond latency as well as throughput of up to 20 Gbps -- a 100x improvement compared to 4G/LTE. However, there exists a vacuum in understanding how 5G performs "in the wild" and whether it can fulfill its promises. In this talk, I will describe our research thrust of 5G networks since early 2019, when Minneapolis became one of the first two U.S. cities that received commercial 5G deployment. Over the past 3 years, we have experimented with more than 100 TB of 5G data and traveled more than 8,000 km for drive tests. Our studies revealed a complete landscape of 5G across several key dimensions -- network performance, power characteristics, mobility management, application quality-of-experience (QoE), to name a few, with their critical tradeoffs quantitatively revealed. I will then talk about our development of a learning-based framework for accurate 5G performance prediction, and how we innovate emerging applications such as virtual/mixed reality (VR/MR) to improve their QoE on 5G networks.

    Biography: As an experimental networking and system researcher, I design, engineer, deploy, evaluate real network systems, and make them yield real-world impact. I am particularly interested in mobile systems, AR/VR, mobile networking, wearable computing, real-world system measurements.

    I received my Ph.D. from EECS at University of Michigan in 2012. I am honored to receive several awards including the AT&T Key Contributor Award (2014), NSF CRII Award (2016), Google Faculty Award (2016), ACM CoNEXT Best Paper Award (2016,2018), AT&T VURI Award (2017), NSF CAREER Award (2018), Trustees Teaching Award (2018), DASH-IF Excellence Award (2019), Cisco Research Award (2021), and ACM SIGCOMM Best Student Paper Award (2021). Some of my research prototypes such as mobile Application Resource Optimizer (ARO) have been commercialized and are widely used in academia and industry.

    Host: Dr. Konstantinos Psounis, kpsounis@usc.edu

    Webcast: https://usc.zoom.us/j/93770414634?pwd=SlBFL0JwL3QwR0RjK1p5bVMyM3duQT09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/93770414634?pwd=SlBFL0JwL3QwR0RjK1p5bVMyM3duQT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Astani Department of Civil and Environmental Engineering Seminar

    Thu, Mar 10, 2022 @ 01:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Michael Gomez, Ph.D., University of Washington

    Talk Title: Bio-cementation Soil Improvement for the Mitigation of Earthquake-induced Soil Liquefaction

    Abstract: Recent advances in bio-mediated soil improvement technologies have highlighted the potential of natural biological-chemical reactions in the soil subsurface to enable mitigation of infrastructure damage resulting from natural hazards such as earthquakes. Bio-mediated geotechnical solutions leverage the capabilities of microorganisms already existing in the geotechnical subsurface to generate a diverse range of products, which can dramatically improve the engineering behavior of soils. One such technology, Microbially Induced Calcite Precipitation (MICP), is an environmentally conscious soil improvement technique that can improve the geotechnical properties of granular soils through the precipitation of calcite. The biogeochemical process offers an environmentally-conscious alternative to traditional brute-force mechanical and Portland cement based ground improvement methods, by utilizing natural microbial enzymatic activity to induce calcite precipitation on soil particle surfaces and at particle contacts. The resulting bio-cementation affords improvements in soil shear strength, initial shear stiffness, and liquefaction resistance, while reducing soil hydraulic conductivity and porosity. Although MICP has been demonstrated extensively at the laboratory scale, critical gaps remain in our understanding of this technology with respect to up-scaling the process to field-scale, understanding the engineering behavior of (bio-)cemented geomaterials, and evaluating material permanence. This presentation will provide a brief introduction to MICP and highlight results from several recent experiments completed at centimeter- and meter- scales aimed at: (1) developing the MICP process for field-scale deployment including techniques for the stimulation of indigenous microorganisms, management of ammonium by-products, and improvement of cementation spatial uniformity and extent, (2) characterizing the liquefaction resistance of bio-cemented geomaterials including triggering and post-triggering responses, and (3) systematically exploring the effect of treatment conditions and environmental factors on resulting material mineralogy and long-term

    Biography: Mike Gomez is an Assistant Professor in the department of Civil and Environmental Engineering at the University of Washington. Mike joined UW in March 2017 after completing his Ph.D. at the University of California, Davis. His research focuses on leveraging natural chemical and biological processes in soils to develop sustainable bio-mediated geotechnical ground improvement technologies. In particular, Mike research has focused on the strengthening of loose and weak granular soils through a bio-mediated calcite precipitation process known as Microbially Induced Calcite Precipitation (MICP). Mike additional research interests include advanced laboratory and in-situ testing, naturally cemented and aged geomaterials, reactive transport modeling, clay surface chemistry, and non-destructive measurements for site characterization and subsurface reaction monitoring, among other topics.



    Host: Dr. Chukwuebuka Nweke

    Webcast: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659 Pass: 975701

    Location: ZOOM MEETING

    WebCast Link: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659 Pass: 975701

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • Mork Family Department Seminar - Miaofang Chi

    Thu, Mar 10, 2022 @ 04:00 PM - 05:15 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Miaofang Chi, Center for Nanophase Materials Sciences at Oak Ridge National Laboratory

    Talk Title: Emerging Scanning Transmission Electron Microscopy (STEM) for Energy Materials Research

    Host: Professor A.Hodge

    Location: Kaprielian Hall (KAP) - 147

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • CS Colloquium: David Held (Carnegie Mellon University) - Perceptual Robot Learning

    Thu, Mar 10, 2022 @ 05:00 PM - 06:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: David Held, Carnegie Mellon University

    Talk Title: Perceptual Robot Learning

    Series: Computer Science Colloquium

    Abstract: *New time: 5:00-6:20PM*

    Robots today are typically confined to interact with rigid, opaque objects with known object models. However, the objects in our daily lives are often non-rigid, can be transparent or reflective, and are diverse in shape and appearance. One reason for the limitations of current methods is that computer vision and robot planning are often considered separate fields. I argue that, to enhance the capabilities of robots, we should design state representations that consider both the perception and planning algorithms needed for the robotics task. I will show how we can develop novel perception and planning algorithms to assist with the tasks of manipulating cloth, articulated objects, and transparent and reflective objects. By thinking about the downstream task while jointly developing perception and planning algorithms, we can significantly improve our progress on difficult robots tasks.

    Register in advance for this webinar at:
    https://usc.zoom.us/webinar/register/WN_X9bmT5afSU2gjC03nttQHg

    After registering, attendees will receive a confirmation email containing information about joining the webinar.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: David Held is an assistant professor at Carnegie Mellon University in the Robotics Institute and is the director of the RPAD lab: Robots Perceiving And Doing. His research focuses on perceptual robot learning, i.e. developing new methods at the intersection of robot perception and planning for robots to learn to interact with novel, perceptually challenging, and deformable objects. David has applied these ideas to robot manipulation and autonomous driving. Prior to coming to CMU, David was a post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in Computer Science at Stanford University. David also has a B.S. and M.S. in Mechanical Engineering at MIT. David is a recipient of the Google Faculty Research Award in 2017 and the NSF CAREER Award in 2021.


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_X9bmT5afSU2gjC03nttQHg

    Location: Online - Zoom Webinar

    WebCast Link: https://usc.zoom.us/webinar/register/WN_X9bmT5afSU2gjC03nttQHg

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • ECE Seminar: Optics, Sensors & AI: Next-Generation Computational Imaging

    Fri, Mar 11, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Vivek Boominathan, Postdoctoral Research Associate, Department of Electrical and Computer Engineering, Rice University

    Talk Title: Optics, Sensors & AI: Next-Generation Computational Imaging

    Abstract: Rapidly growing machine learning techniques such as deep learning have produced powerful computer vision algorithms. However, these algorithms usually apply to images and videos captured with traditional camera designs that have been principally unchanged for decades. Furthermore, real-world applications such as robotics, autonomous navigation, augmented/virtual reality, human-computer interaction, biomedical, and IoT need systems that adhere to fundamental constraints such as size, weight, power, and privacy. These fundamental constraints cannot be addressed by a software-only solution but demand a joint hardware-software solution. In my talk, I will present end-to-end computational imaging systems that execute "computation" at all stages of a physical vision system, from optics to sensors to algorithms. Novel optics such as diffractive and metamaterial optics provide new dimensions of light manipulation, while novel sensors such as SPADs offer new dimensions in light transduction. I will highlight algorithms and AI to explore these new dimensions and accessible nanofabrication techniques to realize novel optics and sensors. I will show applications from photographic 3D imaging to in vivo 3D imaging, achieved using compact coded aperture systems and ultraminiature lensless imaging systems. I will conclude by describing how my works set the stage for designing next-generation imaging systems for various future applications such as biomedical imaging, robotics, IoT, and human-computer interaction.

    Biography: Dr. Vivek Boominathan is a postdoctoral research associate in the Department of Electrical and Computer Engineering at Rice University. He received his Ph.D. in 2019, advised by Prof. Ashok Veeraraghavan, and co-advised by Prof. Jacob Robinson and Prof. Richard Baraniuk. His research interests lie at the intersection of computer vision, machine learning, applied optics, and nanofabrication. His contributions have appeared in a broad spectrum of venues such as Science Advances, Nature BME, IEEE journals, optics journals, vision conferences, and circuits conferences. He has also published a review article, in Optica, around his Ph.D. topic of lensless imaging. His work has been covered by news media such as EurekAlert, NPR, Phys.org, and NDTV India. He has co-organized a tutorial on Computational Imaging and Machine Learning in CVPR 2019 and has served as the publication co-chair for ICCP since 2020. More details can be found at https://vivekboominathan.com/.


    Host: Dr. Shri Narayanan, shri@ee.usc.edu

    Webcast: https://usc.zoom.us/j/96039656028?pwd=RnVxeGx3aEZ3RTNsTW5PajFWakN2Zz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/96039656028?pwd=RnVxeGx3aEZ3RTNsTW5PajFWakN2Zz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Harsha V. Madhyastha (University of Michigan) - Inter-connecting society across space and time

    Fri, Mar 11, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Harsha V. Madhyastha, University of Michigan

    Talk Title: Inter-connecting society across space and time

    Series: CS Colloquium

    Abstract: Thanks to the Internet and a range of services that have been developed to take advantage of it -- web, email, social media, instant messaging, etc. -- being in the same place at the same time is no longer a requirement for all of us to share information with each other. Instead, we are able to store our ideas, opinions, and observations on services which enable others to access this information later from anywhere in the world.

    In this talk, I will discuss my group/s work over the past several years to address some of the fundamental challenges faced by the providers of such global-scale services. I will provide examples of two broad research thrusts: 1) enabling cost-effective development and deployment of geo-distributed services, and 2) optimizing the availability and performance of client-service interactions. I will also briefly discuss my ongoing research in facilitating
    information exchange in domains such as web archival, federated learning, and 3D printing.


    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Harsha V. Madhyastha is an Associate Professor in CSE at the University of Michigan. His research broadly spans the areas of distributed systems and
    networking. Two of his papers have received the IRTF's Applied Networking Research Prize, and he has also co-authored award papers at OSDI, NSDI, and IMC. He has received multiple Google Faculty Research awards, a NetApp Faculty Fellowship, a Facebook Faculty Award, and an NSF CAREER award.


    Host: Barath Raghavan

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ECE-EP Seminar - Quntao Zhuang, Friday, March 11th at 2pm in EEB 248

    Fri, Mar 11, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Quntao Zhuang, University of Arizona

    Talk Title: Quantum Information Processing: From Fundamentals to Applications

    Abstract: Quantum physics has changed the way we understand nature, and also the way we process information. Starting from the fundamental questions raised a century ago, we have now entered an era of quantum engineering. In this talk, I will introduce our recent results on quantum sensing and communication. Quantum sensing utilizes quantum effects such as coherence, squeezing and entanglement to boost measurement sensitivity. I will summarize the paradigm of distributed quantum sensing, which utilizes multi-partite entanglement to boost the measurement of an arbitrary function of local network parameters, generalizing the famous Heisenberg limit of quantum sensing; distributed quantum sensing has a wide range of applications, including dark matter search in different platforms and quantum machine learning. Then, I will briefly present our recent results on quantum radar and quantum spectroscopy. Finally, I will introduce our works on quantum communication. Claude Shannon established the famous classical capacity of communication channels---the ultimate rate at which classical physics allows us to communicate. Quantum physics has made things more interesting. To begin with, I will introduce our recent works in breaking the Shannon capacity for the first time, by utilizing quantum entanglement; Next, I will briefly summarize works on quantum information transmission, including quantum transduction and quantum repeaters.

    Biography: Quntao Zhuang is an assistant professor in ECE and Optical Sciences at University of Arizona. He joined university of Arizona in 2019 after a brief postdoc at University of California, Berkeley. He got his PHD in physics from MIT in 2018. He received the NSF CAREER award in 2022, DARPA Young Faculty Award and Craig M. Berge Dean's Fellow in 2020.

    Host: ECE-Electrophysics

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • ECE Seminar: The Role of Machine Learning in Electronic Design Automation

    Mon, Mar 14, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Vidya A. Chhabria, Ph.D. Candidate, Electrical and Computer Engineering Department, University of Minnesota

    Talk Title: The Role of Machine Learning in Electronic Design Automation

    Abstract: For several decades, advances in hardware, accelerated by Moore's law and enabled by electronic design automation (EDA) tools, have sustainably met the demands for high computation at low energy and cost. However, emerging applications demand computing power far beyond today's system capabilities. Rapid advances in high-performance computing address the problem by using accelerators for specialized tasks such as machine learning (ML), increasing design diversity and system complexity. With Moore's law running out of steam, EDA tools now play a crucial role in meeting these computational demands. EDA tools are challenged to build chips that not only compensate for slow down in scaling, but also provide high performance for both ML and non-ML applications, which use a variety of new architectural techniques and operate under stringent performance constraints. Conventional EDA tools involve computationally expensive analysis and optimizations and are suboptimal as they often tradeoff speed for accuracy. ML promises to address these challenges as it has found tremendous success in solving these problems in classification, detection, and design space exploration problems in several different applications.

    In this talk, I will show how leveraging ML techniques can revolutionize EDA tools by addressing the existing challenges. In particular, the talk will focus on tools that aid designers in (i) delivering power inside the chip without significant losses to meet power demands and (ii) sending the heat outside the chip to avoid high temperatures. The first section of the talk will show how a fast ML inference brings down several hours of runtime to a few milliseconds on industry-scale designs for these tasks. The second section will demonstrate how ML enables high-quality solutions through rapid optimizations. A key challenge with the proposed ML-based methods is the limited availability of open-source data and benchmarks for training and evaluation. The third section will show how ML can generate synthetic training sets and benchmarks for evaluating novel EDA solutions to these tasks. I will conclude by presenting avenues for future research in ML and EDA.

    Biography: Vidya A. Chhabria is a Ph.D. candidate in the Electrical and Computer Engineering department at the University of Minnesota. She received her B.E. in Electronics and Communication from M. S. Ramaiah Institute of Technology, India, in 2016, and her M.S. in Electrical Engineering from the University of Minnesota in 2018. Her research interests are in the areas of electronic design automation, IC design, and machine learning. She has interned at Qualcomm Technologies, Inc. in the summer of 2017 and NVIDIA Corporation during the summers of 2020 and 2021. She received the ICCAD Best Paper Award in 2021, the University of Minnesota Doctoral Dissertation Fellowship in 2021, Louise Dosdall Fellowship in 2020, and Cadence Women in Technology Scholarship in 2020.


    Host: Dr. Pierluigi Nuzzo, nuzzo@usc.edu

    Webcast: https://usc.zoom.us/j/91321182725?pwd=ZDl0Qzc0b0F3cVRlZE1ORE11VHdCQT09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/91321182725?pwd=ZDl0Qzc0b0F3cVRlZE1ORE11VHdCQT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • ***NO ISE 651, Epstein Seminar - Spring Recess***

    Tue, Mar 15, 2022

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • Mork Family Department Seminar - Juan Restrepo-Florez

    Tue, Mar 15, 2022 @ 04:00 PM - 05:20 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Juan Restrepo-Florez, University of Wisconsin-Madison

    Talk Title: A road toward sustainability -from materials to processes-

    Host: Professor A.Hodge

    Location: Social Sciences Building (SOS) - B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • ECE Seminar: Data efficient high-dimensional machine learning

    Wed, Mar 16, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Kamyar Azizzadenesheli, Assistant Professor, Department of Computer Science, Purdue University

    Talk Title: Data efficient high-dimensional machine learning

    Abstract: Traditional deep neural networks are maps between finite dimension spaces, and hence, are not suitable for modeling phenomena such as those arising from the solution of partial differential equations (PDE). In the first part of the talk, I introduce a new deep learning paradigm, called neural operators, that learns operators which are maps between infinite dimension spaces. I show that neural operators are universal approximators of operators and demonstrate a series of empirical successes of neural operators in natural sciences.

    In the second part, I talk about the intersection of control theory and reinforcement learning and establish data-efficient learning and decision-making methods for generic dynamical systems. I conclude the talk by presenting empirical successes of these principled methods.

    Biography: Kamyar Azizzadenesheli is an assistant professor at Purdue University, department of computer science, since Fall 2020. Prior to his faculty position, he was at the California Institute of Technology (Caltech) as a Postdoctoral Scholar at the Department of Computing + Mathematical Sciences. Before his postdoctoral position, he was appointed as a special student researcher at Caltech, working with ML and Control researchers at the CMS department and the Center for Autonomous Systems and Technologies. He is also a former visiting student researcher at Caltech. Kamyar Azizzadenesheli is a former visiting student researcher at Stanford University, and researcher at Simons Institute, UC. Berkeley. In addition, he is a former guest researcher at INRIA France (SequeL team), as well as a visitor at Microsoft Research Lab, New England, and New York. He received his Ph.D. at the University of California, Irvine.

    Host: Dr. Salman Avestimehr, avestime@usc.edu

    Webcast: https://usc.zoom.us/j/93153496285?pwd=SmE3clJMSm9OVmVoNWdhMW1SVlk4QT09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/93153496285?pwd=SmE3clJMSm9OVmVoNWdhMW1SVlk4QT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Mork Family Department Seminar - Vida Jamali

    Thu, Mar 17, 2022 @ 10:00 AM - 11:20 AM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Vida Jamali, University of California-Berkeley

    Talk Title: Imaging, Learning, and Engineering of Soft Matter Systems at the Nanoscale

    Host: Professor A.Hodge

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • ECE Seminar: Machine Learning for Precision Health: A Holistic Approach

    Thu, Mar 17, 2022 @ 10:00 AM - 11:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Ahmed Alaa, Postdoctoral Associate, Broad Institute of MIT & Harvard, MIT CSAIL

    Talk Title: Machine Learning for Precision Health: A Holistic Approach

    Abstract: Machine learning (ML) methods, combined with large-scale electronic health databases, could enable a personalized approach to healthcare by improving patient-specific diagnosis, prognostic predictions, and treatment decisions. If successful, this approach would be transformative for clinical research and practice. In this talk, I will describe a holistic approach to ML for precision health that comprises a three-step procedure: (1) data characterization and understanding, (2) model development and (3) model deployment. Next, I will demonstrate one instantiation of this approach in the context of developing ML models for predicting patient-level response to therapies using observational data. I will focus on a multi-task learning model that uses Gaussian processes to estimate the causal effects of a treatment on individual patients and discuss its application in various disease areas. Finally, I will discuss exciting avenues for future work, including ML methods for learning from unannotated clinical data, generating synthetic data and integrating clinical knowledge into data-driven modeling.


    Biography: Dr. Ahmed Alaa is a postdoctoral associate at Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard University. Previously, he was a joint postdoctoral scholar at Cambridge University, Cambridge Center for AI in Medicine and the University of California, Los Angeles (UCLA). He obtained his Ph.D. in Electrical and Computer Engineering from UCLA, where he was also a recognized (visiting) Ph.D. student at Oxford University. His research focuses on developing machine learning (ML) methods that can leverage healthcare data to enable a patient-centric approach to medicine, whereby ML models can inform disease diagnosis, prognosis and treatment decisions based on the characteristics of individual patients. He is the recipient of the (school-wide) 2021 Edward K. Rice Outstanding Doctoral Student Award at UCLA.

    Host: Dr. Ashutosh Nayyar, ashutosn@usc.edu

    Webcast: https://usc.zoom.us/j/94383946134?pwd=U1N4emFRaDBnc0pTd2VXUHMwSkVidz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/94383946134?pwd=U1N4emFRaDBnc0pTd2VXUHMwSkVidz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Matthew Mirman (ETH Zürich) - Trustworthy Deep Learning: methods, systems and theory

    Thu, Mar 17, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Matthew Mirman , ETH Zürich

    Talk Title: Trustworthy Deep Learning: methods, systems and theory

    Series: CS Colloquium

    Abstract: Deep learning models are quickly becoming an integral part of a plethora of high stakes applications, including autonomous driving and health care. As the discovery of vulnerabilities and flaws in these models has become frequent, so has the interest in ensuring their safety, robustness and reliability. My research addresses this need by introducing new core methods and systems that can establish desirable mathematical guarantees of deep learning models.

    In the first part of my talk I will describe how we leverage abstract interpretation to scale verification to orders of magnitude larger deep neural networks than prior work, at the same time demonstrating the correctness of significantly more properties. I will then show how these techniques can be extended to ensure, for the first time, formal guarantees of probabilistic semantic specifications using generative models.

    In the second part, I will show how to fuse abstract interpretation with the training phase so as to improve a model's amenability to certification, allowing us to guarantee orders of magnitude more properties than possible with prior work. Finally, I will discuss exciting theoretical advances which address fundamental questions on the very existence of certified deep learning.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Matthew Mirman is a final-year PhD student at ETH Zürich, supervised by Martin Vechev. His main research interests sit at the intersection of programming languages, machine learning, and theory with applications to creating safe and reliable artificial intelligence systems. Prior to ETH, he completed his B.Sc. and M.Sc. at Carnegie-Mellon University supervised by Frank Pfenning.

    Host: Mukund Raghothaman

    Location: 115

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Amir Houmansadr (UMass Amherst) - Communication Secrecy in the Age of AI

    Thu, Mar 17, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Amir Houmansadr, UMass Amherst

    Talk Title: Communication Secrecy in the Age of AI

    Series: CS Colloquium

    Abstract: Internet users face constant threats to the secrecy of their communications: repressive regimes deprive them of open access to the Internet, corporations and surveillance organizations monitor their online behavior, advertising companies and social networks collect and share their private information, and cybercriminals hurt them financially by stealing their private information. In this talk, I will present the key research challenges facing communication secrecy in a world overtaken by the AI. In particular, I will introduce new ML-specific mechanisms to defeat AI-enabled surveillance. I will also discuss crucial AI trustworthiness research problems that are essential to the secrecy of Internet communications in the age of AI.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Amir Houmansadr is an associate professor of computer science at UMass Amherst. He received his Ph.D. from the University of Illinois at Urbana-Champaign in 2012, and spent two years at the University of Texas at Austin as a postdoctoral scholar. Amir is broadly interested in the security and privacy of networked systems. To that end, he designs and deploys privacy-enhancing technologies, analyzes network protocols and services (e.g., messaging apps and machine learning APIs) for privacy leakage, and performs theoretical analysis to derive bounds on privacy (e.g., using game theory and information theory). Amir has received several awards and recognitions including the 2013 IEEE S&P Best Practical Paper Award, a 2015 Google Faculty Research Award, an NSF CAREER Award in 2016, a CSAW 2019 Applied Research Competition Finalist, an IMC 2020 Best Paper Award Runner-up, and a Facebook 2021 Privacy Enhancing Technologies Award Finalist. He is an Associate Editor of the IEEE TDSC and frequently serves on the program committees of major security conferences.

    Host: Barath Raghavan

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ECE-EP Seminar - Mehdi Kiani, Thursday, March 17 at 2pm in EEB 248

    Thu, Mar 17, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Mehdi Kiani, Pennsylvania State University

    Talk Title: Wireless Hybrid Electrical-Acoustic Systems for Body-Machine Interface

    Abstract: We have already witnessed significant efforts towards the research and development of neurotechnologies to radically enhance our understanding of the extremely complex central and peripheral nervous systems (CNS and PNS) by modulating and imaging their activities. These technologies can eventually be utilized in establishing body-machine interfaces (BMIs) with the CNS and PNS to offer effective, minimally invasive, and long-term solutions for neurological disorders and chronic disabilities such as spinal cord and brain injuries, stroke, Parkinson's disease, epilepsy, rheumatoid arthritis, and diabetes, to name a few. Despite all the developments over the past decade, closed-loop BMIs with minimally invasive high-spatiotemporal-resolution recording and stimulation capabilities from the large-scale distributed CNS/PNS circuits is still one of the grand challenges of the neuroscience research in the 21st century. In this talk, I will present our recent efforts (and future work) towards the development of advanced minimally invasive BMIs for modulating and sensing neural and electrophysiological activities with high spatiotemporal resolution at large scale. These BMIs are enabled by innovative integrated circuits, ultrasound, and wireless power/data (with different modalities such as ultrasound and magnetoelectric) technologies. I will particularly present two projects that leverage ultrasound beam focusing and steering with electronic beamforming to enable wireless implantable technologies for high-resolution, large-scale brain neuromodulation and gastric electrical-wave mapping.

    Biography: Dr. Kiani received his Ph.D. degree in Electrical and Computer Engineering from the Georgia Institute of Technology in 2014. He joined the faculty of the School of Electrical Engineering and Computer Science at the Pennsylvania State University in August 2014 where he is currently an Associate Professor. His research interests are in the multidisciplinary areas of analog, mixed-signal, and power-management integrated circuits; ultrasound; and wireless power/data transfer and energy harvesting for wireless implantable medical devices and neural interfaces. He was a recipient of the 2020 NSF CAREER Award. He is currently an Associate Editor of the IEEE Transactions on Biomedical Circuits and Systems and IEEE Transactions on Biomedical Engineering. He also serves as a Technical Program Committee member of the IEEE International Solid-State Circuits Conference (ISSCC) in the IMMD subcommittee.

    Host: ECE-Electrophysics

    More Information: Mehdi Kiani Flyer.pdf

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • ECE-EP Seminar - Najme Ebrahimi, Friday, March 18th at 10am in EEB 248

    Fri, Mar 18, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Najme Ebrahimi, University of Florida

    Talk Title: Next Generation Intelligent and Secured Wireless World: From IoT and Sensors to Wideband and Multi-band Scalable Circuit and System

    Abstract: The future intelligent and secured wireless world needs connectivity at any time anywhere and under extreme conditions with over one trillion sensors and Internet-of-Things (IoT) devices connected to the network. To this end, the autonomous, and yet connected, wireless world is envisioned to provide sensing and high-data-rate communications, accurate localization and ranging, and resiliency. The major challenges to attain these goals are latency and energy efficiency requirements, that are largely affected by interference, multi-path, and channel fading. To tackle these challenges, wideband high frequency scalable arrays are desired to provide high data-rate communications and directional beams for interference cancellation. Furthermore, wideband/multiband circuits and systems are needed for accurate localization in the presence of severe multipath and fading in ultra-dense environments in IoT networks.

    In this talk, firstly, I will present novel techniques to overcome the challenges for future wideband/multiband scalable transceiver arrays, including power-efficient local oscillator distribution and phase shifting, image selection architecture, and novel compact antenna-IC integration. I will then discuss our ongoing work towards the wideband/multiband signal generation and modulation for 6G and beyond as well as heterogonous integration of different technologies and modules for extending the Moore's law. Secondly, I will present multi-band circuit generation for IoT and sensor nodes to be employed in dense wireless networks. More specifically, I will present the first bidirectional circuitry for IoT transponder that reciprocally generates harmonics and subharmonics, covering two communication frequency bands interchangeably, which makes it a premier tool for localization and sensing protocols. I will also discuss future directions on advanced multi-band reconfigurable architecture for wireless sensors and IoTs compatible with network and physical layer protocols for security, communications, and localization.

    Biography: Najme Ebrahimi is an Assistant Professor of Electrical and Computer Engineering at the University of Florida. Her research focuses on Mm-Wave/THz Scalable Array for high data rate communications and sensing as well as the security and connectivity of the next generation of distributed Internet-of-Things (IoT) networks. She was a post-doctoral research fellow at the University of Michigan- Ann Arbor from 2017 to 2020 under the departmental fellowship and earned her Ph.D. from the University of California, San Diego in June 2017. She was selected as a Rising Star by MIT EECS Rising Star program in 2019 and by ISSCC Rising Star program of the IEEE Solid-State Circuits Society in 2020. She is a member of the Microwave and Mm-Wave Integrated Circuits committee (MTT-14) and serves in the IMS2022 Technical Paper Review Committee (TPRC). She is the recipient of the 2021 DARPA Young Faculty Award (YFA).

    Host: ECE-Electrophysics

    More Information: Najme Ebrahimi Flyer.pdf

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • CS Colloquium: Jieyu Zhao (UMD) - Building Accountable NLP Models: on Social Bias Detection and Mitigation

    Fri, Mar 18, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Jieyu Zhao , UMD

    Talk Title: Building Accountable NLP Models: on Social Bias Detection and Mitigation

    Series: CS Colloquium

    Abstract: Natural Language Processing (NLP) plays an important role in many applications, including resume filtering, text analysis, and information retrieval. Despite the remarkable accuracy enabled by the advances in machine learning used in many applications, the technique may discover and generalize the societal biases implicit in the data. For example, an automatic resume filtering system may unconsciously select candidates based on their gender and race due to implicit associations between applicant names and job titles, causing the societal disparity discovered by researchers. Various laws and policies have been designed to ensure social equality and diversity. However, there is no such mechanism for a machine learning model for sensitive applications. My research analyzes the potential stereotypes in various machine learning models and develops computational approaches to enhance fairness in a wide range of NLP applications. The broader impact of my research aligns with one the concerns of machine learning community: how can we do AI for social good.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Jieyu Zhao is a postdoctoral research at UMD, working together with Prof. Hal Daumé III. She obtained her PhD from the department of Computer Science at UCLA where she was advised by Prof. Kai-Wei Chang. Her research interest lies in fairness of ML/NLP models. Her paper got the EMNLP Best Long Paper Award (2017). She was one of the recipients of 2020 Microsoft PhD Fellowship and has been selected to participate in 2021 Rising Stars in EECS workshop. Her research has been covered by news media such as Wires, The Daily Mail and South China Morning Post. She was invited by UN-WOMEN Beijing on a panel discussion about gender equality and social responsibility. More detail can be found at https://jyzhao.net/.

    Host: Xiang Ren

    Location: Ronald Tutor Hall of Engineering (RTH) - 105

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Yue Wang (MIT) - Learning 3D representations with minimal supervision

    Mon, Mar 21, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Yue Wang , MIT

    Talk Title: Learning 3D representations with minimal supervision

    Series: CS Colloquium

    Abstract: Deep learning has demonstrated considerable success embedding images and more general 2D representations into compact feature spaces for downstream tasks like recognition, registration, and generation. Learning on 3D data, however, is the missing piece needed for embodied agents to perceive their surrounding environments. To bridge the gap between 3D perception and robotic intelligence, my present efforts focus on learning 3D representations with minimal supervision from a geometry perspective.

    In this talk, I will discuss two key aspects to reduce the amount of human supervision in current 3D deep learning algorithms. First, I will talk about how to leverage geometry of point clouds and incorporate such inductive bias into point cloud learning pipelines. These learning models can be used to tackle object recognition problems and point cloud registration problems. Second, I will present our works on leveraging natural supervision in point clouds to perform self-supervised learning. In addition, I will discuss how these 3D learning algorithms enable human-level perception for robotic applications such as self-driving cars. Finally, the talk will conclude with a discussion about future inquiries to design complete and active 3D learning systems.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Yue Wang is a final year PhD student with Prof. Justin Solomon at MIT. His research interests lie in the intersection of computer vision, computer graphics, and machine learning. His major field is learning from point clouds. His paper "Dynamic Graph CNN" has been widely adopted in 3D visual computing and other fields. He is a recipient of the Nvidia Fellowship and is named the first place recipient of the William A. Martin Master's Thesis Award for 2021. Yue received his BEng from Zhejiang University and MS from University of California, San Diego. He has spent time at Nvidia Research, Google Research and Salesforce Research.

    Host: Ramakant Nevatia

    Location: online only

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Erdem Bıyık (Stanford University) - Learning Preferences for Interactive Autonomy

    Mon, Mar 21, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Erdem Bıyık , Stanford University

    Talk Title: Learning Preferences for Interactive Autonomy

    Series: CS Colloquium

    Abstract: In human-robot interaction or more generally multi-agent systems, we often have decentralized agents that need to perform a task together. In such settings, it is crucial to have the ability to anticipate the actions of other agents. Without this ability, the agents are often doomed to perform very poorly. Humans are usually good at this, and it is mostly because we can have good estimates of what other agents are trying to do. We want to give such an ability to robots through reward learning and partner modeling. In this talk, I am going to talk about active learning approaches to this problem and how we can leverage preference data to learn objectives. I am going to show how preferences can help reward learning in the settings where demonstration data may fail, and how partner-modeling enables decentralized agents to cooperate efficiently.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Erdem Bıyık is a fifth-year Ph.D. candidate in the Electrical Engineering department at Stanford. He has received his B.Sc. degree from Bilkent University, Turkey, in 2017; and M.Sc. degree from Stanford University in 2019. He is interested in enabling robots to actively learn from various forms of human feedback and designing altruistic robot policies to improve the efficiency of multi-agent systems both in cooperative and competitive settings. He also worked at Google as a research intern in 2021 where he adapted his active robot learning algorithms to recommender systems.

    Host: Heather Culbertson

    Location: Ronald Tutor Hall of Engineering (RTH) - 105

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ECE-EP Seminar - Volker Sorger, Monday, March 21st @ 2pm in EEB 248

    Mon, Mar 21, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Volker Sorger, George Washington University

    Talk Title: Devices & ASICs for Machine Intelligence and Post-Quantum Cryptography

    Abstract: The high demand for AI services in conjunction with a dramatic chip shortage along with technology leaps such as 5/6G networks, cybersecurity threats, and quantum algorithms have resurrected a R&D push for advanced devices, information processing, and computing capability. To address this demand and explore novel technology, unique opportunities exist, for example, given by algorithmic parallelism of mixed-signal non-van Neuman accelerators. Especially electronic-photonic ASIC compute paradigms hold promise to enable non-iterative O(1) runtime complexity, ps-short latency, and TOPS/W throughputs. This opens prospects for next-generation hardware both for AI cloud services but also for accelerating edge computing such as enabled by compact and efficient PIC-CMOS co-designs pushing the SWAP envelope. As both a professor and a co-founder of a venture, in this seminar I will share my latest insights on fundamental complexity scaling and algorithm-hardware homomorphism on the one hand, and device- circuit- and system-level demonstrations on the other. I will introduce a novel memristive photonic RAM capable of zero-static power consumption suitable for AI edge applications and highlight our photonic tensor core ASIC demonstration leveraging parallelism including a software stack. Beyond matrix-matrix multiplication acceleration, I will show our Convolution Theorem-based accelerator enabling 1000x1000 matrix-size convolutions at 100us latency, or about 10x faster than today's GPUs. At the device level I will share advanced optoelectronics and quantum matter including a 50Gbps ITO-based modulator being 1,000x more compact than Silicon PDK solutions, discuss strainoptronic detectors with high gain-bandwidth-product, a 100GHz fast VCSEL, and share a path for an electrically-driven quantum source. Finally, having solved the complex-signal convolution I will show a Montgomery Multiplier for a data-center RSA public-key cryptosystem, and conclude by highlighting our recent post-quantum secure-hash-algorithm (SHA) system accelerating blockchain operations. I will conclude with an R&D outlook for the next decade and share examples of my passion supporting values and programs on diversity & inclusion.

    Biography: Volker J. Sorger is an Associate Professor in the Department of Electrical and Computer Engineering and the Director of the Institute on AI & Photonics, the Head of the Devices & Intelligent Systems Laboratory at the George Washington University. His research areas include devices & optoelectronics, AI/ML accelerators, mixed-signal ASICs, quantum matter & processors, and cryptography. For his work, Dr. Sorger received multiple awards including the Presidential PECASE Award, the AFOSR YIP Award, the Emil Wolf Prize, and the National Academy of Sciences award of the year. Dr. Sorger is an Associate editor for OPTICA, serves on the board of Chip, and was the former editor-in-chief of Nanophotonics. He is a Fellow of Optica (former OSA), a Fellow of SPIE, a Fellow of the German National Academic Foundation, and a Senior Member of IEEE. He is a co-founder of Optelligence Company.

    Host: ECE-Electrophysics

    More Information: Volker Sorger Flyer.pdf

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • CS Colloquium: Rachee Singh (Microsoft Azure for Operators) - Leveraging over-provisioned cloud networks for next-generation services

    Tue, Mar 22, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Rachee Singh , Microsoft Azure for Operators

    Talk Title: Leveraging over-provisioned cloud networks for next-generation services

    Series: CS Colloquium

    Abstract: The last decade has seen a large-scale commercialization of cloud computing and the emergence of global cloud providers. Cloud providers are expanding their datacenter deployments and backbone capacity, preparing their infrastructure to meet the challenges of rapidly evolving workloads in the cloud. In this talk, I will re-examine the design and operation choices made by cloud providers during this phase of exponential growth using a cross-layer empirical analysis of the wide-area network (WAN) of a large commercial cloud provider. Despite their crucial role in enabling high performance cloud applications and expensive infrastructure, there are several inefficiencies in both the design and operation of cloud WANs. In this talk, I will focus on improving the performance and cost efficiency of the fiber optical network underpinning cloud WANs. First, I will demonstrate how rate-adaptive physical links can harness 75% more capacity from 80% of the optical wavelengths in a cloud WAN, leading to a gain of over 100 terabits of network capacity with 25% fewer link failures. Second, I will show how to achieve a 40% reduction in the hardware costs of provisioning long-haul WAN capacity by optically bypassing network hops where conversion of light signals from optical to electrical domain is unnecessary and uneconomical. I will show that identifying and fixing these inefficiencies in today's cloud networks is crucial for enabling next-generation cloud services.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Rachee Singh is a senior researcher in the office of the CTO of Microsoft Azure for Operators. Before this, she was a researcher in the Mobility and Networking group of Microsoft Research, Redmond. Her research interests are in computer networking with a focus on wide area network performance and monitoring. Her PhD dissertation was supported by a Google PhD Fellowship in Systems and Networking and it received the CICS Outstanding dissertation award at the University of Massachusetts, Amherst. Recently, she was named a rising star in computer networking by N2Women and a rising star in EECS by UC Berkeley. In a previous life, she developed routing protocol features for Ethernet switches at Arista Networks.

    Host: Ramesh Govindan

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Ariel Barel (Technion, Israeli Institute of Technology) - Applied Deep-Learning methods for Expediting Path Selection in Real-Time MAP

    Tue, Mar 22, 2022 @ 01:00 PM - 02:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Ariel Barel, Technion, Israeli Institute of Technology

    Talk Title: Applied Deep-Learning methods for Expediting Path Selection in Real-Time MAP

    Series: Computer Science Colloquium

    Abstract: Multi-Agent Path Finding (MAPF) is an NP-hard problem that plays a key role in numerous domains ranging from warehouse automation to computer games. In this research we are given a large pre-calculated set of legal paths from all possible sources to all possible destinations. The aim is to select paths from this set such that they do not collide with static obstacles nor with each other and minimize the maximal execution time of all tasks (Makespan). This selection should be calculated in near real-time, i.e., extremely fast compared to classic MAPF algorithms.

    We investigate how Deep-Learning methods may speed up the search process, as trained Neural Networks have potential to make computations extremely fast. Training dataset may be generated by solving the "online" problem offline. The idea is to train the network to recognize patterns in the training examples and apply them to new, previously unseen, settings of the problem, i.e., new pairs of sources and destinations. The main challenges are definition of NN architecture and input representation.

    This work addresses well-formed environments where agents may wait indefinitely at their sources but must follow a wait-free path once deployed. Moreover, our framework allows assignment of multiple agents per source and requires that all calculations complete before the first deployment, making scheduling a key component of the solution.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

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    Biography: Dr. Ariel Barel is an academic visitor at the Technion, Israeli Institute of Technology. He received the PhD degrees in Computer Science from the Technion in the field of Distributed Control of Multi-Agent Systems. His current interest also includes Machine Learning implementations to expedite traditional planning algorithms. For more info and publications visit his personal web page https://arielba.cswp.cs.technion.ac.il/


    Host: Christopher Leet (cjleet@usc.edu), Sven Koenig (skoenig@usc.edu)

    Webcast: https://usc.zoom.us/j/98857434920

    Location: Online - Zoom

    WebCast Link: https://usc.zoom.us/j/98857434920

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • CS Colloquium: Natasha Jacques (Google Brain / UC Berkeley) - Social Reinforcement Learning

    Tue, Mar 22, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Natasha Jacques, Google Brain / UC Berkeley

    Talk Title: Social Reinforcement Learning

    Series: CS Colloquium

    Abstract: Social learning helps humans and animals rapidly adapt to new circumstances, coordinate with others, and drives the emergence of complex learned behaviors. What if it could do the same for AI? This talk describes how Social Reinforcement Learning in multi-agent and human-AI interactions can address fundamental issues in AI such as learning and generalization, while improving social abilities like coordination. I propose a unified method for improving coordination and communication based on causal social influence. I then demonstrate that multi-agent training can be a useful tool for improving learning and generalization. I present PAIRED, in which an adversary learns to construct training environments to maximize regret between a pair of learners, leading to the generation of a complex curriculum of environments. Agents trained with PAIRED generalize more than 20x better to unknown test environments. Ultimately, the goal of my research is to create intelligent agents that can assist humans with everyday tasks; this means leveraging social learning to interact effectively with humans. I show that learning from human social and affective cues scales more effectively than learning from manual feedback. However, it depends on accurate recognition of such cues. Therefore I discuss how to dramatically enhance the accuracy of affect detection models using personalized multi-task learning to account for inter-individual variability. Together, this work argues that Social RL is a valuable approach for developing more general, sophisticated, and cooperative AI, which is ultimately better able to serve human needs.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Natasha Jaques holds a joint position as a Senior Research Scientist at Google Brain and Postdoctoral Fellow at UC Berkeley. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. Natasha completed her PhD at MIT, where her thesis received the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing. Her work has also received Best Demo at NeurIPS, an honourable mention for Best Paper at ICML, Best of Collection in the IEEE Transactions on Affective Computing, and Best Paper at the NeurIPS workshops on ML for Healthcare and Cooperative AI. She has interned at DeepMind, Google Brain, and was an OpenAI Scholars mentor. Her work has been featured in Science Magazine, Quartz, MIT Technology Review, Boston Magazine, and on CBC radio. Natasha earned her Masters degree from the University of British Columbia, and undergraduate degrees in Computer Science and Psychology from the University of Regina.

    Host: Jesse Thomason

    Location: Ronald Tutor Hall of Engineering (RTH) - 105

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ISE 651 Epstein Seminar

    Tue, Mar 22, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Alain Rossier, Visiting guest/3rd year PhD student, Oxford University

    Talk Title: Asymptotic Analysis of Deep Residual Networks

    Host: Dr. Renyuan Xu

    More Information: March 22, 2022.pdf

    Location: Online/Zoom

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • Mork Family Department Seminar - Aditya Sood

    Tue, Mar 22, 2022 @ 04:00 PM - 05:20 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Aditya Sood, Stanford University

    Talk Title: Engineering functionality through dynamic visualization and control of atomic motions

    Host: Professor A.Hodge

    Location: Social Sciences Building (SOS) - B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • CS Colloquium: Ram Alagappan (VMware Research Group) - Co-designing Distributed Systems and Storage Stacks for Improved Reliability

    Wed, Mar 23, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Ram Alagappan , VMware Research Group

    Talk Title: Co-designing Distributed Systems and Storage Stacks for Improved Reliability

    Series: CS Colloquium

    Abstract: Distributed storage systems form the core of modern cloud services. Like many systems software, these systems are built using layering: designers layer distributed protocols (e.g., Paxos, 2PC) upon local storage stacks. Such layering abstracts details about the local storage stack to the layers above, easing development. I will show that such black-box layering, unfortunately, masks vital information, resulting in poor reliability. I will then demonstrate that reliability can be significantly improved by co-designing these layers.
    In the first half of the talk, I will show how local storage-layer faults in one node can lead to serious vulnerabilities such as global data loss, corruption, and unavailability in many widely used systems. I then present CTRL, a new foundation that uses the co-design approach to avoid such problems, improving reliability. I implement CTRL in two practical systems and show that CTRL greatly improves resiliency to storage faults while incurring little performance overhead.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Ram Alagappan is a postdoctoral researcher at the VMware Research Group. He earned his Ph.D., working with Professors Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau at the University of Wisconsin - Madison. His work has been published at top systems venues and has won three best paper awards (FAST 17, 18, and 20). His dissertation also won an honorable mention for the UW CS Best Dissertation. His open-source frameworks have had a practical impact: these tools have exposed more than 80 severe vulnerabilities across 20 widely used systems. Ideas from his work have been adopted by a financial database to make it more robust.

    Host: Ramesh Govindan

    Location: online only

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • AME Seminar

    Wed, Mar 23, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: George Tynan, UC San Diego

    Talk Title: Status and Outlook for Controlled Fusion as a Firm Zero-Carbon Energy Source

    Abstract: Controlled fusion research has been pursued since the 1950s by most of the world's developed economies due to many attractive characteristics of this seemingly elusive technology. In 2021, inertial confinement fusion experiments at LLNL reached the threshold of fusion ignition while magnetic confinement experiments in the UK demonstrated that the ITER device nearing completion in France should, for the first time, produce a burning plasma in which fusion heating dominates the system. In parallel, a rapidly developing industry with $4B of private-sector funding has emerged and is pursuing a wide variety of approaches for controlled fusion. This talk will summarize the key elements of these developments, and sketch out the characteristics that fusion-based energy systems will need to demonstrate if they are to compete economically in the emerging zero-carbon energy system of the mid-century.

    Biography: George Tynan studies the fundamental physics of turbulent transport in hot confined plasmas using both smaller scaled laboratory plasma devices as well as large scale fusion experiments located around the world. In addition, he is investigating how solid material surfaces interact with the boundary region of fusion plasmas, and how the materials are modified by that interaction. He is also interested in the larger issue of transitioning to a sustainable energy economy based upon a mixture of efficient end use technologies, large scale deployment of renewable energy sources, and incorporation of a new generation of nuclear technologies such as advanced fission and fusion reactor systems. He received his Ph.D. in 1991 from the Department of Mechanical, Aerospace, and Nuclear Engineering at the University of California, Los Angeles. He then spent several years studying the effect of sheared flows on plasma turbulence on experiments located in the Federal Republic of Germany and at Princeton Plasma Physics Laboratory, and worked in industry developing plasma sources for use in investigating the creation of submicron-scale semiconductor circuits. He joined the UCSD faculty in 1999 where he worked to establish a graduate program in plasma physics within the School of Engineering. He has served as Associate Vice Chancellor for Research, Associate Dean of Engineering, is co-founding Director of the UC San Diego Deep Decarbonization Initiative, and is currently Department Chair of Mechanical and Aerospace Engineering at the UC San Diego Jacobs School of Engineering.

    Host: AME Department

    More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • ECE-EP Seminar - Dejan Markovic, Thursday, March 24th at 10am in EEB 248 & via Zoom

    Thu, Mar 24, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dejan Markovic, UCLA

    Talk Title: The Future of Computing and Neuromodulation

    Abstract: This talk will discuss future technologies addressing unmet needs in science, medicine, and engineering. Data-driven attentive computing requires runtime flexible and efficient hardware and software. Simple hardware leads to complex software (e.g. FPGA) and simple software leads to complex hardware (e.g. CPU). Runtime reconfigurable arrays (RTRAs) balance hardware and software to enable spatial and temporal flexibility for dynamic or uncertain environments. RTRA features multi-program tenancy, multi-size compile, and priority handling for >100x compute capacity gains over FPGA, and within 5x of (inflexible) hardware accelerators, as shown on a blind signal classification use case. Medical implants also require efficiency and flexibility, with heavily constrained size, weight and power, for novel clinical research and therapeutic systems. Despite notable clinical successes (e.g. Parkinson's disease), limitations in existing devices prevent them from expanding to other indications such as mental health or Alzheimer's disease. I will discuss the Neuro-stack, a versatile closed-loop system, verified in human subject experiments, towards miniaturized neural duplex of the future. These applications also reveal opportunities in system-level design automation to address design productivity and system assembly challenges.

    Biography: Dejan Marković is a Professor of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA). He is also affiliated with UCLA Bioengineering Department, Neuroengineering field. He completed the Ph.D. degree in 2006 at the University of California, Berkeley, for which he was awarded 2007 David J. Sakrison Memorial Prize. His current research is focused on implantable neuromodulation systems, domain-specific compute architectures, and design methodologies. Dr. Marković co-founded Flex Logix Technologies, a semiconductor IP startup, in 2014, and helped build foundational technology of Ceribell, a medical device startup. He received an NSF CAREER Award in 2009. In 2010, he was a co-recipient of ISSCC Jack Raper Award for Outstanding Technology Directions. He also received 2014 ISSCC Lewis Winner Award for Outstanding Paper. Prof. Markovic is a Fellow of the IEEE.

    Host: ECE-Electrophysics

    More Information: Dejan Markovic Flyer.pdf

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • CS Colloquium: Tesca Fitzgerald (Carnegie Mellon University) - Learning to address novel situations through human-robot collaboration

    Thu, Mar 24, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Tesca Fitzgerald, Carnegie Mellon University

    Talk Title: Learning to address novel situations through human-robot collaboration

    Series: CS Colloquium

    Abstract: As our expectations for robots' adaptive capacities grow, it will be increasingly important for them to reason about the novel objects, tasks, and interactions inherent to everyday life. Rather than attempt to pre-train a robot for all potential task variations it may encounter, we can develop more capable and robust robots by assuming they will inevitably encounter situations that they are initially unprepared to address. My work enables a robot to address these novel situations by learning from a human teacher's domain knowledge of the task, such as the contextual use of an object or tool. Meeting this challenge requires robots to be flexible not only to novelty, but to different forms of novelty and their varying effects on the robot's task completion. In this talk, I will focus on (1) the implications of novelty, and its various causes, on the robot's learning goals, (2) methods for structuring its interaction with the human teacher in order to meet those learning goals, and (3) modeling and learning from interaction-derived training data to address novelty.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Dr. Tesca Fitzgerald is a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University. Her research is centered around interactive robot learning, with the aim of developing robots that are adaptive, robust, and collaborative when faced with novel situations. Before joining Carnegie Mellon, Dr. Fitzgerald received her PhD in Computer Science at Georgia Tech and completed her B.Sc at Portland State University. She is an NSF Graduate Research Fellow (2014), Microsoft Graduate Women Scholar (2014), and IBM Ph.D. Fellow (2017).


    www.tescafitzgerald.com


    Host: Heather Culbertson

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Seo Jin Park (MIT CSAIL) - Towards Interactive Big Data Processing Through Flash Burst Parallel Systems

    Thu, Mar 24, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Seo Jin Park , MIT CSAIL

    Talk Title: Towards Interactive Big Data Processing Through Flash Burst Parallel Systems

    Series: CS Colloquium

    Abstract: Today, many organizations store big data on the cloud and lease relatively small clusters of instances to run analytics queries, train machine learning models, and more. However, the exponential data growth, combined with the slowdown of Moore's law, makes it challenging (if not impossible) to run such big data processing tasks in real-time. Most applications run big data workloads on timescales of several minutes or hours and resort to complex, application-specific optimizations to reduce the amount of data processing required for interactive queries. This design pattern hurts developer productivity and restricts the scope of applications that can use big data. However, as we have many servers in a cloud datacenter, a natural question is "can we borrow thousands of servers briefly to accelerate big data processing enough to be interactive?"

    In this talk, I'll share my vision to enable massively parallel data processing even for very short-duration (1-10 ms), which I call "flash bursts." This will empower interactive, real-time applications (e.g., cyber security attack defense, self-driving cars or drones, etc) to utilize much larger data than before. For this moonshot, I take a two-pronged approach. First, I restructure important big data applications (analytics and DNN training) so that they can run efficiently in a flash burst fashion. On this prong, the talk will focus on how I efficiently scaled distributed sorting to 100+ servers even for a 1-millisecond time budget. Second, I rebuild various layers in distributed systems to reduce overheads of flash burst scaling. On this prong, I will focus on how I removed the overheads of consistent replication.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Seo Jin Park is a postdoctoral researcher at MIT CSAIL. He received a Ph.D. in Computer Science from Stanford University in 2019, advised by John Ousterhout. He is broadly interested in distributed systems, focusing on low-latency systems: scaling low-latency data processing, optimizing consensus protocols (both standard and byzantine), suppressing tail-latencies, and building efficient performance debugging tools. His Ph.D. study was supported by Samsung Scholarship.

    Host: Barath Raghavan

    Location: Michelson Center for Convergent Bioscience (MCB) - 101

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • Mork Family Department Seminar - Jason Bates

    Thu, Mar 24, 2022 @ 04:00 PM - 05:20 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Jason Bates, University of Wisconsin-Madison

    Talk Title: Catalysis beyond the binding site: reactions on crowded surfaces and in packed pores

    Host: Professor A.Hodge

    Location: Kaprielian Hall (KAP) - 147

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • CS Colloquium: Feras Saad (MIT) - Scalable Structure Learning and Inference via Probabilistic Programming

    Thu, Mar 24, 2022 @ 04:00 PM - 05:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Feras Saad, MIT

    Talk Title: Scalable Structure Learning and Inference via Probabilistic Programming

    Series: CS Colloquium

    Abstract: Probabilistic programming supports probabilistic modeling, learning, and inference by representing sophisticated probabilistic models as computer programs in new programming languages. This talk presents efficient probabilistic programming-based techniques that address two fundamental challenges in scaling and automating structure learning and inference over complex data.

    First, I will describe scalable structure learning methods that make it possible to automatically synthesize probabilistic programs in an online setting by performing Bayesian inference over hierarchies of flexibly structured symbolic program representations, for discovering models of time series data, tabular data, and relational data. Second, I will present fast compilers and symbolic analyses that compute exact answers to a broad range of inference queries about these learned programs, which lets us extract interpretable patterns and make accurate predictions in real time.

    I will demonstrate how these techniques deliver state-of-the-art performance in terms of runtime, accuracy, robustness, and programmability by drawing on several examples from real-world applications, which include adapting to extreme novelty in economic time series, online forecasting of flu rates given sparse multivariate observations, discovering stochastic motion models of zebrafish hunting, and verifying the fairness of machine learning classifiers.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Feras Saad is a PhD candidate in Computer Science at MIT working at the intersection of programming languages, probabilistic machine learning, and computational statistics. His research is accompanied with a collection of popular open-source probabilistic programming systems used by collaborators at Intel, Takeda, Liberty Mutual, IBM, and the Bill & Melinda Gates Foundation for practical applications of structure learning and probabilistic inference. Feras' MEng thesis on probabilistic programming and data science has been recognized with the 1st Place Computer Science Thesis Award at MIT.

    Host: Mukund Raghothaman

    Location: Ronald Tutor Hall of Engineering (RTH) - 105

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Chuang Gan (MIT-IBM Watson AI Lab) - Neuro-Symbolic AI for Machine Intelligence

    Fri, Mar 25, 2022 @ 10:00 AM - 11:00 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Chuang Gan, MIT-IBM Watson AI Lab

    Talk Title: Neuro-Symbolic AI for Machine Intelligence

    Series: CS Colloquium

    Abstract: Machine intelligence is characterized by the ability to understand and reason about the world around us. While deep learning has excelled at pattern recognition tasks such as image classification and object recognition, it falls short of deriving the true understanding necessary for complex reasoning and physical interaction. In this talk, I will introduce a framework, neuro-symbolic AI, to reduce the gap between machine and human intelligence in terms of data efficiency, flexibility, and generalization. Our approach combines the ability of neural networks to extract patterns from data, symbolic programs to represent and reason from prior knowledge, and physics engines for inference and planning. Together, they form the basis of enabling machines to effectively reason about underlying objects and their associated dynamics as well as master new skills efficiently and flexibly.

    This lecture satisfies requirements for CSCI 591: Research Colloquium



    Biography: Chuang Gan is a principal research staff member at MIT-IBM Watson AI Lab. He is also a visiting research scientist at MIT, working closely with Prof. Antonio Torralba and Prof. Josh Tenenbaum. Before that, he completed his Ph.D. with the highest honor at Tsinghua University, supervised by Prof. Andrew Chi-Chih Yao. His research interests sit at the intersection of computer vision, machine learning, and cognitive science. His research works have been recognized by Microsoft Fellowship, Baidu Fellowship, and media coverage from BBC, WIRED, Forbes, and MIT Tech Review. He has served as an area chair of CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, ACL, and an associate editor of IEEE Transactions on Image Processing.

    Host: Ram Nevatia

    Location: online only

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ECE Seminar: Distributed Systems: Rigorous Theoretical Foundations Unlock Promising Gains

    Fri, Mar 25, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Mohammad Ali Maddah-Ali, Research Scientist, Department of Electrical Engineering, Stanford University

    Talk Title: Distributed Systems: Rigorous Theoretical Foundations Unlock Promising Gains

    Abstract: Over the last twenty years, we have witnessed several revolutionary technologies, from communication networks to learning platforms to blockchains, that have profoundly changed our daily lives. Often, these platforms are modeled, designed, and operated based on intuition and folk wisdom. In this talk, we challenge some of those common beliefs. We show that by meticulously elaborating the key performance bottlenecks from first principles, we can propose counterintuitive solutions grounded in rigorous analysis that unlock considerable scaling gains in several areas:

    1) In wireless communications, the delay in acquiring channel information is a significant bottleneck in supporting multiple users at a time. Contrary to popular belief, we demonstrate that even completely outdated channel information can be used for interference management and enabling simultaneous communications, thus alleviating the bottleneck of channel training.

    2) In content delivery networks, folk wisdom design is to maximize the likelihood of serving a request from the local cache (hit rate); thus, the performance is bottlenecked by the size of an individual cache. We propose a fundamentally new approach with a gain that scales with the sum of the cache sizes in the network, rather than an individual cache size.

    3) In distributed learning, we demonstrate that training with combined data samples (i.e., erasure-coded samples), rather than raw samples, can significantly improve the reliability and convergence rate. Moreover, we highlight the surprising role of approximation theory in circumventing a major bottleneck in designing practical coded training procedures.

    We conclude with promising directions for further investigation: in particular, the challenges in adding decentralized trust and accountability to these systems, to place control over them back in the hands of individuals rather than big corporations.

    Biography: Mohammad Ali Maddah-Ali received the B.Sc. degree from the Isfahan University of Technology, the M.Sc. degree from the University of Tehran, and the Ph.D. degree from the Department of Electrical and Computer Engineering, University of Waterloo, Canada. From 2008 to 2010, he was a Postdoctoral Fellow in the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley. From 2010 to 2020, he was working at Bell Labs, Holmdel, NJ, as a communication network research scientist. He also worked as a faculty member at the Department of Electrical Engineering, Sharif University of Technology. Currently, he is a research scientist at the Department of Electrical Engineering, Stanford University.

    Dr. Maddah-Ali is a recipient of several awards including the IEEE International Conference on Communications (ICC) Best Paper Award in 2014, the IEEE Communications Society and IEEE Information Theory Society Joint Paper Award in 2015, and the IEEE Information Theory Society Paper Award in 2016. He is currently serving as an associate editor for the IEEE Transactions on Information Theory and a lead editor for The IEEE Journal on Selected Areas in Information Theory.

    Host: Dr. Keith Chugg, chugg@usc.edu

    Webcast: https://usc.zoom.us/j/98149159985?pwd=cWFsVnRkZXRKcTlWYllMcy9Rempmdz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/98149159985?pwd=cWFsVnRkZXRKcTlWYllMcy9Rempmdz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Advanced Manufacturing Seminar

    Fri, Mar 25, 2022 @ 10:00 AM - 11:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Mostafa Bedewy, University of Pittsburgh

    Talk Title: Manufacturing for the Future: Carbon-Based Flexible Neural Interfaces

    Abstract: Abstract: Nanocarbons like graphene, carbon nanotubes (CNTs), and nanofibers are promising for various applications including advanced electronic devices, novel energy systems, and next-generation healthcare diagnostics. This is owing to the excellent physical, chemical and electrochemical properties arising from the ordered atomic structure, the hierarchical nanoscale morphology, and tunable chemistry of nanocarbons. In particular, high surface area carbon electrodes for biosensors and neural interfaces have consistently been shown to have superior performance when compared to state-of-the-art metal electrodes. Nevertheless, major manufacturing challenges still hinder our ability to scalably produce nanocarbon-based electrodes with tailored morphology and surface chemistry, especially on flexible substrates. Unlike different transfer technique of CVD-grown nanocarbons, this talk will focus on a unique bottom-up approach for directly growing different types of graphenic nanocarbons on polymer films by laser irradiation. The speaker will show how this direct-write process, often referred to as laser-induced graphene (LIG), can be controlled to produce spatially-varying morphologies and chemical compositions of LIG electrodes, by leveraging gradients of laser fluence. Moreover, a method will be introduced to control the heteroatom doping of these LIG electrodes based on controlling the molecular structure of the polymer being lased. Finally, a demonstration of these functional LIG electrodes as electrochemical biosensors will be presented for the detection of the neurotransmitter dopamine with nanomolar sensitivity.

    Biography: Dr. Mostafa Bedewy leads the NanoProduct Lab at the University of Pittsburgh. His research interests include carbon nanomaterials, laser processing, nanomanufacturing and micromanufactuing, chemical vapor deposition (CVD), and biology-assisted manufacturing. Dr. Bedewy received the Frontiers of Materials Award from the Minerals, Metals and Materials Society (TMS) in 2022, Outstanding Young Investigator Award from the Institute of Industrial and Systems Engineers Manufacturing and Design (IISE M&D) Division in 2020, Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers (SME) in 2018, the Ralph E. Powe Junior Faculty Enhancement Award from the Oak Ridge Associated Universities (ORAU) in 2017, the Robert A. Meyer Award from the American Carbon Society in 2016, and many other prestigious awards.

    Host: Center for Advanced Manufacturing

    More Info: https://usc.zoom.us/webinar/register/WN_OMywkH2iRSmzYMtYVM-frQ

    Webcast: https://usc.zoom.us/webinar/register/WN_OMywkH2iRSmzYMtYVM-frQ

    WebCast Link: https://usc.zoom.us/webinar/register/WN_OMywkH2iRSmzYMtYVM-frQ

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • Astani Civil and Environmental Engineering Seminar

    Fri, Mar 25, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Kristopher McNeill, Department of Environmental Systems Science, ETH Zurich

    Talk Title: An Environmental Chemist View of Biodegradable Plastics

    Abstract: Contamination of the environment with plastic is a long-recognized problem, but in recent years, there has been a remarkable increase in public attention and outcry regarding plastic pollution. The low cost and durability of plastic materials, which make them desirable for many applications, are the same factors that contribute to their accumulation. The low cost lowers the barrier to short- term and single use applications and the durability means that, once in the environment, these materials are highly persistent. On this latter point, there is a growing interest and market for non- persistent, biodegradable plastic materials, which could help the problem of accumulation of plastic in the environment. This presentation will focus on several key questions about these alternative biodegradable materials: How do we know that a material is really biodegrading instead of just breaking down into microplastic? How does the receiving environment affect biodegradability? Are there applications where biodegradable plastics are viable alternatives to conventional plastics? What are the challenges that we face from an environmental chemistry perspective?

    Biography: Prof. Kris McNeill received his B.A. in Chemistry from Reed College (Portland, Oregon) in 1992 and his Ph.D. in Chemistry from the University of California, Berkeley in 1997. At Berkeley, he was co-advised by Professors Robert Bergman and Richard Andersen. Following his PhD, he switched his research focus from organometallic chemistry to environmental chemistry. He was a postdoctoral researcher at MIT from 1997 to 1999 with Prof. Philip Gschwend in the department of Civil and Environmental Engineering. McNeill began his independent career as a faculty member at the University of Minnesota in the Department of Chemistry, holding ranks of Assistant Professor (2000-2006) and Associate Professor (2007-2009). In 2009, Kris McNeill joined the faculty of ETH Zurich, where he continues to apply physical organic chemistry to the study of environmental processes.

    Host: Dr. Daniel McCurry

    More Info: https://usc.zoom.us/j/93390473354 Meeting ID: 933 9047 3354 Passcode: 527888

    Location: Ray R. Irani Hall (RRI) - 421

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • ECE Seminar Announcement: Accelerating Chip-Building Design Cycles for Future Generations of Computing

    Mon, Mar 28, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Christopher Torng, Postdoctoral Researcher, Stanford University

    Talk Title: Accelerating Chip-Building Design Cycles for Future Generations of Computing

    Abstract: The chip building industry is a major cornerstone of the global economy. As a result, addressing the causes behind a multi-year global chip shortage is important for both near and long term futures. Unfortunately, one major challenge is that it is difficult to produce high-quality designs quickly and at low cost using traditional hardware design flows. This means that the industry wastes valuable fabrication slots learning painful design lessons rather than meeting economic demands.

    My research focuses on building new architectures, systems, and design tools to accelerate chip building design cycles for future generations of computing systems. To support this goal, my research spans across the computing stack, ranging from applications, compilers, architectures, and down to chip implementation. In this talk, I will first present a set of vertically integrated techniques (compiler, architecture, and VLSI) that significantly reduces the design effort for extremely fine-grain power control in spatial architectures. Next, I will introduce my work on a new generation of open and agile hardware flow tools that leverage modern programming language features to increase code reuse in physical design. Finally, I will discuss recent work on Amber SoC, a coarse-grained reconfigurable array designed with an end-to-end agile accelerator-compiler co-designed flow. I will conclude with my future directions in supporting chip building for the next generation of computing.

    Biography: Christopher Torng is a postdoctoral researcher at Stanford University. He received his Ph.D. degree, M.S. degree, and B.S degree (2019, 2016, 2012) in Electrical and Computer Engineering from Cornell University. His projects target the development of architectures and tools to accelerate building chips and complex hardware systems. His tools have achieved use across multiple universities to support over ten academic tapeouts in technologies ranging from 180nm to 16nm. His activities have resulted in a selection as a Rising Star in Computer Architecture (2018) by Georgia Tech and an IEEE MICRO Top Pick from Hot Chips (2018).

    Host: Dr. Peter Beerel, pabeerel@usc.edu

    Webcast: https://usc.zoom.us/j/99531222900?pwd=S1VDR2pRU2lyZ2hORmtObE1PcFh6Zz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/99531222900?pwd=S1VDR2pRU2lyZ2hORmtObE1PcFh6Zz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Aishwarya Ganesan (VMware Research) - Consistency and Performance in Distributed Storage Systems

    Mon, Mar 28, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Aishwarya Ganesan , VMware Research

    Talk Title: Consistency and Performance in Distributed Storage Systems

    Series: CS Colloquium

    Abstract: Talk abstract: Computer systems underpin every modern application that we interact with today. When designing systems, one must often tradeoff strong guarantees for performance or vice-versa. The same tradeoff exists in distributed storage systems as well; designers must often choose consistency or performance. In this talk, I will show how we can build distributed storage systems that provide strong consistency yet also perform well. My key insight to achieving this goal is to defer enforcing consistency until state is externally visible. Based on this insight, I design two novel distributed storage systems.

    First, I present Skyros, a new replication protocol that exploits storage-interface properties to defer expensive coordination. Skyros realizes that many update interfaces are nil-externalizing: they do not expose system state immediately. By taking advantage of nil-externality, Skyros offers significantly lower latencies than traditional replication protocols while still providing strong consistency.

    Second, I present consistency-aware durability (CAD), a new durability primitive that enables stronger consistency. CAD shifts the point of durability from writes to reads. By delaying writes, CAD enables high performance; however, by ensuring durability before serving reads, CAD enables the construction of stronger consistency models.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Aishwarya Ganesan is a postdoctoral researcher at VMware Research. She completed her PhD from the University of Wisconsin - Madison in Computer Sciences, advised by Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau. She is broadly interested in distributed systems and storage systems. Her work has been recognized with best-paper awards at FAST 20 and FAST 18 and a best paper award nomination at FAST 17. She was selected for the Rising Stars in EECS workshop and a recipient of Facebook 2019 PhD Fellowship. She also received the graduate student instructor award for teaching graduate-level distributed systems at UW Madison.

    Host: Ramesh Govindan

    Location: online only

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Souti Chattopadhyay (Oregon State University) - When cognition works against us! Transforming Software to reduce the cost of cognitive processes.

    Mon, Mar 28, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Souti Chattopadhyay, Oregon State University

    Talk Title: When cognition works against us! Transforming Software to reduce the cost of cognitive processes.

    Series: CS Colloquium

    Abstract: 86 billion neurons make up our brains! Naturally, these 100 trillion neural connections give rise to a complex process of making decisions, interpreting information, and taking intended actions. This is especially true when programming, whether to build software systems or analyze data. Cognitive processes like selective interpretation and biases affect these programming decisions and actions frequently and significantly. In a recent study, we found that biases are associated with 45.7% of actions that developers take (like editing a line or navigating to a part of code). Eventually, developers reversed or undid 70% of the actions associated with biases which made up 25% of their entire worktime [1]. Similarly, data scientists report spending a lot of time in a "tortuous, multi-step adventure" for getting the data set up for analysis based on familiarity and preferences [2]. Programmers pay the necessary price of being human when working with tools without support for the negative impacts of cognitive processes. In this talk, I will present findings on how some cognitive processes affect programming. To reduce the friction between software and cognition, we will discuss how to design tools to be vigilant and provide desired support using automated and empirical approaches.


    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Souti Chattopadhyay (Rini) is a Ph.D. candidate at Oregon State University in the Department of EECS. She works at the intersection of Human-Computer Interaction, Software Engineering, and Cognitive Science, focusing on assisting software engineers and data scientists.

    Her research is on human-centered tools and interfaces that align with the human cognitive processes when solving problems. Her work is focused on understanding how humans make decisions when interacting with interfaces, specifically programming interfaces. She studies developers, data scientists, and end-user programmers to identify the process behind their technical decisions and social interactions.

    During her internship at Microsoft Research, she worked on a project related to the next generation of developers, specifically how they express their identity on social media platforms like YouTube. Some of her works were awarded best papers and honorable mentions by ACM and IEEE, including understanding cognitive biases in programmers and exploring a plethora of challenges data scientists face. Her work on cognitive biases was also recognized as research highlights by CACM and that on data scientists was featured on Nature articles.


    Host: Chao Wang

    Location: Ronald Tutor Hall of Engineering (RTH) - 105

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • Astani Department of Civil and Environmental Engineering Ph.D. Dissertation

    Mon, Mar 28, 2022 @ 02:00 PM - 04:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Maria Morvillo, Ph.D. Candidate, Viterbi School of Engineering- Astani Department of Civil and Environmental Engineering

    Talk Title: Reproducible and Rapid Computational Approaches for Assessing Contamination in Natural Aquifers

    Abstract: The ubiquitous presence of multi-scale heterogeneity in hydrological properties is the cause of complex subsurface flow patterns that impact the transport behavior of a solute plume. Fluctuations in the velocity field lead to increased solute spreading which enhances mass transfer mechanisms and impact solute arrival times. This thesis proposes a series of methods which accounts for the effects of aquifer heterogeneity on transport observables which are essential for risk analysis, performance assessment of waste disposal facilities and the selection of optimal remediation cleanup strategies. The approaches proposed in this dissertation are computationally rapid and reproducible. The first contribution of this thesis consists of the development of a novel aquifer connectivity-ranked Monte Carlo method that accelerates the statistical convergence of the statistics of the first arrival times of a solute body in an environmentally sensitive location. Secondly, I propose an innovative kernel-based reactive random walk particle tracking method to improve the computational efficiency associated with reactive transport in spatially variable groundwater flows. Finally, we present a computational package that links the various components relevant for the estimation of the concentration of a pollutant at an environmentally sensitive target and its uncertainty to support decision making in risk analysis.



    Host: Advisor, Dr. Felipe de Barros

    More Info: https://usc.zoom.us/j/96445541938

    Location: Zoom Meeting

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • CS Colloquium: Lingjie Liu (Max Planck Institute for Informatics) - Neural Representation and Rendering of 3D Real-world Scenes

    Tue, Mar 29, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Lingjie Liu , Max Planck Institute for Informatics

    Talk Title: Neural Representation and Rendering of 3D Real-world Scenes

    Series: CS Colloquium

    Abstract: High-quality reconstruction and photo-realistic rendering of real-world scenes are two important tasks that have a wide range of applications in AR/VR, movie production, games, and robotics. These tasks are challenging because real-world scenes contain complex phenomena, such as occlusions, motions and interactions. Approaching these tasks using classical computer graphics techniques is a highly difficult and time-consuming process, which requires complicated capture procedures, manual intervention, and a sophisticated global illumination rendering process. In this talk, I will introduce our recent work that integrates deep learning techniques into the classical graphics pipeline for modelling humans and static scenes in an automatic way. Specifically, I will talk about creating photo-realistic animatable human characters from only RGB videos, high-quality reconstruction and fast novel view synthesis of general static scenes from RGB image inputs, and scene generation with a 3D generative model. Finally, I will discuss challenges and opportunities in this area for future work.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Lingjie Liu is Lise Meitner Postdoctoral Research Fellow working with Prof. Christian Theobalt in the Visual Computing and AI Department at the Max Planck Institute for Informatics. She received her Ph.D. degree at the University of Hong Kong in 2019. Before that, she got her B.Sc. degree in Computer Science at Huazhong University of Science and Technology in 2014. Her research interests include neural scene representations, neural rendering, human performance modeling and capture, and 3D reconstruction. Webpage: https://lingjie0206.github.io/.

    Host: Jernej Barbic

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Roopsha Samanta (Purdue University) - Semantics-Guided Inductive Program Synthesis

    Tue, Mar 29, 2022 @ 01:00 PM - 02:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Roopsha Samanta, Purdue University

    Talk Title: Semantics-Guided Inductive Program Synthesis

    Series: CS Colloquium

    Abstract: The dream of program synthesis seeks to automatically develop programs that conform to a user's intent. Classically, program synthesis has been framed as a problem of generation of correct-by-construction programs from complete, formal specifications of their expected behavior. An increasingly promising and more tractable paradigm of program synthesis, however, is inductive program synthesis. Broadly construed, inductive program synthesis can be framed as a problem of program discovery from partial specifications such as input-output examples, program traces, and natural language descriptions. While the last decade has witnessed several breakthroughs in improving the scalability and applicability of inductive program synthesis, the true potential of this synthesis paradigm remains to be unleashed.

    In this talk, I will describe my group's ongoing endeavors to advance the frontiers of inductive program synthesis. Further, I will emphasize the need to tackle a fundamental, yet often neglected, challenge of inductive synthesis-”reliability. Because inductive synthesizers generalize from partial observations, they often suffer from overfitting, ambiguity, and brittleness-”the synthesized program may indeed conform to its partial specification, but it may not exhibit the intended behavior on all inputs. I will present my group's novel semantics-guided approach-”based on surprising notions of program semantics-”to improve the reliability of inductive program synthesis.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Roopsha Samanta is an Assistant Professor in the Department of Computer Science at Purdue University. Before joining Purdue in 2016, she completed her PhD at UT Austin in 2013, advised by E. Allen Emerson and Vijay K. Garg, and was a postdoctoral researcher at IST Austria from 2014-2016 with Thomas A. Henzinger. She is a recipient of 2019 NSF CAREER award and 2021 Amazon Research Award. Roopsha's research seeks to help programmers write programs that conform to their intent. She develop tools and techniques for algorithmic program verification, synthesis, and repair for a spectrum of application domains, correctness specifications, and programmer expertise. Her current research agenda is centered around two themes-”semantics-guided inductive program synthesis and repair and modular, bounded verification of unbounded distributed systems

    Host: Mukund Raghothaman

    Location: Ronald Tutor Hall of Engineering (RTH) - 105

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Marco Pavone (Stanford University) - Towards safe, data-driven autonomy

    Tue, Mar 29, 2022 @ 02:30 PM - 03:50 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Marco Pavone, Stanford University

    Talk Title: Towards safe, data-driven autonomy

    Series: Computer Science Colloquium

    Abstract: *New time: 2:30 - 3:50 PM PT*

    AI-powered autonomous vehicles that can learn, reason, and interact with people are no longer science fiction. Self-driving cars, unmanned aerial vehicles, and autonomous spacecraft, among others, are continually increasing in capability and seeing incremental deployment in more and more domains. However, fundamental research questions still need to be addressed in order to achieve full and widespread vehicle autonomy. In this talk, I will discuss our work on addressing key open problems in the field of vehicle autonomy, particularly in pursuit of safe, data-driven autonomy stacks. Specifically, I will discuss (1) robust human prediction models for both simulation and real-time decision making, (2) AI safety frameworks for autonomous systems, and (3) novel, highly integrated autonomy architectures that are amenable to end-to-end training while retaining a modular, interpretable structure. The discussion will be grounded in autonomous driving and aerospace robotics applications.

    **Dr. Marco Pavone will give his talk in person at SGM 124 and we will also host the talk over Zoom.**

    Register in advance for this webinar at:

    https://usc.zoom.us/webinar/register/WN_tR3q2DuTRwulhaNsexKXAw

    After registering, attendees will receive a confirmation email containing information about joining the webinar.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he is the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford. He is currently on a partial leave of absence at NVIDIA serving as Director of Autonomous Vehicle Research. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He is a recipient of a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. He was identified by the American Society for Engineering Education (ASEE) as one of America's 20 most highly promising investigators under the age of 40.


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_tR3q2DuTRwulhaNsexKXAw

    Location: Seeley G. Mudd Building (SGM) - 124

    WebCast Link: https://usc.zoom.us/webinar/register/WN_tR3q2DuTRwulhaNsexKXAw

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • ISE 651 Epstein Seminar

    Tue, Mar 29, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Ignacio Grossmann, R.R. Dean University Professor, Dept. of Chemical Engineering, Carnegie Mellon

    Talk Title: Global Optimization of Nonconvex Nonlinear Generalized Disjunctive Programs

    Host: Dr. Phebe Vayanos

    More Information: March 29, 2022.pdf

    Location: Online/Zoom

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • Mork Family Department Seminar - Julie Rorrer

    Tue, Mar 29, 2022 @ 04:00 PM - 05:20 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Julie Rorrer, Massachusetts Institute of Technology

    Talk Title: From Trash to Treasure: Advancing the Catalytic Upcycling of Waste Plastics and Renewable Feedstocks

    Host: Professor A.Hodge

    Location: Social Sciences Building (SOS) - B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • CS Colloquium: Nengkun Yu (University of Technology Sydney) - Efficient verification and testing of quantum programs

    Wed, Mar 30, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Nengkun Yu , University of Technology Sydney

    Talk Title: Efficient verification and testing of quantum programs

    Series: CS Colloquium

    Abstract: Quantum can solve complex problems that classical computers will never be able to. In recent years, significant efforts have been devoted to building quantum computers to solve real-world problems. To ensure the correctness of quantum programs, we develop verification techniques and testing algorithms for quantum programs. In the first part of this talk, I will overview efficient reasoning about quantum programs by developing verification techniques and tools that leverage the power of Birkhoff & von Neumann quantum logic. In the second part, I will review my work on quantum state tomography, i.e., learning the classical description of quantum states, which closes a long-standing gap between the upper and lower bounds for dynamic testing the properties of a quantum program's output.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Nengkun Yu is an associate professor in the Centre for Quantum Software and Information, the University of Technology Sydney. He received his B.S. and PhD degrees from the Department of Computer Science and Technology, Tsinghua University, Beijing, China, in July of 2008 and 2013. He won a distinguished paper award at OOPSLA 2020 and a distinguished paper award at PLDI 2021. His research interest focuses on quantum computing.


    Host: Todd Brun / Jyo Deshmukh

    Location: online only

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

    Wed, Mar 30, 2022 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Nir Piterman, Department of Computer Science and Engineering, University of Gothenburg, Sweden

    Talk Title: Synthesis From Temporal Specifications

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: In this talk I will present the GR[1] approach to synthesis, the automatic production of designs from their temporal logic specifications. We are interested in reactive systems, systems that continuously interact with other programs, users, or their environment and specifications in linear temporal logic. Classical solutions to synthesis use either two player games or tree automata. I will give a short introduction to the technique of using two player games for synthesis.

    The classical solution to synthesis requires the usage of deterministic automata. This solution is 2EXPTIME-complete, is quite complicated, and does not work well in practice. I will present a syntactic approach that restricts the kind of properties users are allowed to write. It turns out that this approach is general enough and can be extended to cover many properties written in practice.

    Time permitting, I will present results that support the usage of synthesis in model-driven development and robot control.


    Biography: Nir Piterman is a professor of computer science at the University of Gothenburg in Sweden. Before that he was an associate professor at the University of Leicester, held postdoctoral research positions at Imperial College London and the Ecole Polytechnique Federal de Lausanne, and completed his PhD at the Weizmann Institute of Science. His research interests include formal verification and automata theory. Particularly, he has worked on model checking, temporal logic, reactive synthesis, and game solving. His current research is funded by the European Research Council (ERC), the Swedish Research Council (VR), and the Wallenberg Autonomous Systems Program (WASP(. He is currently the editor in chief of the journal Formal Methods in System Design.

    Host: Pierluigi Nuzzo, nuzzo@usc.edu

    Location: Online

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • AME Seminar

    Wed, Mar 30, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Shawn Shadden, University of California, Berkeley

    Talk Title: Computational models of cardiovascular function

    Abstract: Combining medical imaging and other forms of clinical data with first principles-, phenomenological- and/or statistical-based computational modeling has become an important avenue in cardiovascular research, including for disease diagnosis, treatment planning and scientific discovery. In this talk, I will provide some background on the field of computational modeling of cardiovascular biomechanics and will discuss some of our recent work focused on methods to improve personalization and efficiency of this modeling process. Namely, I will discuss developments on machine learning approaches to facilitate image-based model construction and parameterization, some of our work on reduced order modeling to facilitate efficient computation of common physical quantities of clinical importance, and where we might be headed.

    Biography: Shawn Shadden is a Professor and Vice Chair of Mechanical Engineering at the University of California, Berkeley and a core member of the UCSF-UC Berkeley Graduate Program in Bioengineering. His research focuses on the computational modeling of cardiovascular biomechanics and the advancement of theoretical and numerical methods to quantify complex fluid flow. He is recipient of an NSF CAREER Award, a Bakar Faculty Fellow Award, Hellman Faculty Fellow Award, and the American Heart Association Established Investigator Award. His lab helps develop the SimVascular software platform, which is broadly used in the field of computational cardiovascular research.

    Host: AME Department

    More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • CS Colloquium: Weihang Wang (State University of New York at Buffalo) - Understanding WebAssembly via Program Transformation

    Thu, Mar 31, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Weihang Wang , State University of New York at Buffalo

    Talk Title: Understanding WebAssembly via Program Transformation

    Series: CS Colloquium

    Abstract: WebAssembly is the newest language for the web, aiming to enable high-performance applications and provide languages such as C/C++ a compilation target so that they can be run on the web. WebAssembly defines a portable binary instruction set, as well as a corresponding textual assembly format. However, WebAssembly's syntax is difficult to interpret for human readers because of the stack machine-based implementation. As a result, distributed third-party WebAssembly modules need to be implicitly trusted by developers as verifying the functionality requires significant effort.

    In this talk, I will describe my work towards building analysis tools for developers to understand WebAssembly programs. The first section of the talk will focus on identifying limitations of current analysis tools: I will introduce a code obfuscation technique for obfuscating JavaScript malware by translating parts of the computation into WebAssembly. By pinpointing limitations of current malware detectors, my work motivates future efforts on detecting multi-language malware on the web that uses WebAssembly. The second section of the talk will focus on a set of abstraction rules for WebAssembly instructions, which can be used to lift WebAssembly to a high-level representation that abstracts the underlying semantics of the code. I have applied the abstraction rules in detecting WebAssembly-based cryptomining malware. My detection relies on program semantics unique to cryptomining, which is resilient to variants.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Weihang Wang is an Assistant Professor at the State University of New York at Buffalo. She received her Ph.D. degree in Computer Science from Purdue University in 2018. Weihang's interests are in Software Engineering, with a focus on building tools for improving the reliability and security of software systems. She was awarded an NSF CAREER Award in 2021, a Facebook Testing and Verification Research Award in 2019, a Mozilla Research Award in 2019, and a Maurice H. Halstead Memorial Research Award in 2018.

    Host: Chao Wang

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • Astani Civil and Environmental Engineering Seminar

    Thu, Mar 31, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Michael Shields, John Hopkins University

    Talk Title: Manifold Learning for High Dimensional Uncertainty Quantification

    Abstract:
    Uncertainty Quantification (UQ), the systematic and rigorous accounting of uncertainties, has become widely accepted as an essential component of any proper scientific investigation -“ whether computational, experimental, or otherwise. In computational science and engineering, as well as in experimental investigations, we often encounter problems that are parameterized by very high-dimensional quantities and/or result in very high-dimensional quantities of interest. Thanks to the curse of dimensionality, the challenge of solving these problems grows exponentially with the problem dimensions. This explosive growth in complexity has been widely known for decades and may never be truly resolved. However, all hope is not lost. In this presentation, we offer some strategies for addressing high dimensional UQ problems whose uncertainties can be expressed in lower-dimensional latent spaces or on manifolds whose geometry is not necessarily Euclidean. We begin by introducing some concepts in Reimannian geometry and nonlinear dimension reduction, specifically reviewing Grassmann manifolds and diffusion maps, and show how UQ problems with high dimensional solutions can be solved by projecting solution snapshots onto the Grassmann manifold, performing diffusion maps on the manifold, and constructing surrogate models on the resulting low-dimensional space using standard machine learning methods such as Gaussian process regression, polynomial chaos expansions (PCE), or deep neural networks. Next, we consider problems with very high dimensional inputs and present a survey of 13 different unsupervised learning methods for dimension reduction, which are used to identify low-dimensional latent spaces on which PCE surrogates are constructed. Some takeaways from this general approach, termed manifold-PCE, are presented. Finally, we bring the two components together to propose a general framework for UQ in high dimensions that is widely applicable and very flexible.




    Biography: Michael D. Shields is an Associate Professor in the Dept. of Civil & Systems Engineering at Johns Hopkins University and holds a secondary appointment in the Dept. of Materials Science and Engineering. Prof. Shields conducts methodological research in uncertainty quantification and stochastic simulation for problems in mechanics, materials science, and physics with applications ranging from multi-scale material modeling to assessing the reliability and safety of large-scale structures. He received his Ph.D. in Civil Engineering and Engineering Mechanics from Columbia University in 2010, after which he was employed as a Research Engineer in applied computational mechanics at Weidlinger Associates, Inc. He joined the faculty at Johns Hopkins in 2013. For his work in UQ, Prof. Shields has been awarded the ONR Young Investigator Award, the NSF CAREER Award, the DOE Early Career Award, and the Johns Hopkins University Catalyst Award. Prof. Shields and his group also develop the open-source UQpy (Uncertainty Quantification with Python) software, which is a general toolbox for UQ in computational, mathematical, and physical systems.


    Host: Dr. Roger Ghanem

    Webcast: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659 Pass: 975701

    Location: Ronald Tutor Hall of Engineering (RTH) - 526

    WebCast Link: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659 Pass: 975701

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • McKinsey & Company Info Session, hosted by Affinity Networks (Virtual)

    Thu, Mar 31, 2022 @ 07:00 PM - 08:00 PM

    Viterbi School of Engineering Career Connections

    Conferences, Lectures, & Seminars


    Abstract: Our global All In, Diversity & Inclusion initiatives engage colleagues around the world to develop and share innovative ways of working that advance inclusivity. To ensure diversity in gender, ethnicity, background, education, orientation, etc., we constantly look for new ways to reach people who might otherwise not be considering consulting as a career option.
    For this session, we will be talking about our global culture of inclusion and our Affinity Networks, and also sharing a few details about the application process.
    Please register for the event here: https://mckinsey.avature.net/events/Rsvp/?folderId=62467
    External employer-hosted events and activities are not affiliated with the USC Viterbi Career Connections Office. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participant's responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.edu

    More Info: https://mckinsey.avature.net/events/Rsvp/?folderId=62467

    Location: RSVP in Viterbi Career Gateway

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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