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Conferences, Lectures, & Seminars
Events for April

  • CS Colloquium: Paul Schmitt (USC ISI) - Networked Systems for a Modern, Private Internet

    Mon, Apr 04, 2022 @ 10:00 AM - 11:00 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Paul Schmitt, USC ISI

    Talk Title: Networked Systems for a Modern, Private Internet

    Abstract: Users expect that the networks and protocols they use protect their privacy. Unfortunately, many ubiquitous legacy systems have significant privacy flaws. Network operators face a different challenge: widespread adoption of encryption, while a clear benefit to users, reduces operator visibility into traffic flowing through their networks. In this talk, I discuss networked systems to enhance user privacy and systems and techniques for privacy-preserving network traffic analysis. I describe my research that leverages key architectural points of decoupling to enhance privacy in the global DNS ecosystem and in mobile networks. I then discuss systems I have built for privacy-preserving network analysis for use by network operators to gain insight into network usage and performance, all without breaking encryption.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Paul Schmitt is a research computer scientist at ISI. His research areas include networked systems, privacy, network traffic inference and analysis, and scalable Internet measurement. His work takes a dirty-slate approach to networked systems research, allowing for compatibility and immediate deployability in current environments. He previously received his PhD from UC Santa Barbara in 2017 and was an associate research scholar at Princeton University.

    Host: John Heidemann

    Audiences: Everyone Is Invited

    Contact: Cherie Carter

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  • CAIS Seminar: David Eddie (Massachusetts General Hospital) - Towards a biosensor-driven, just-in-time relapse prevention tool for substance use disorder: Identifying neurocardiac biomarkers of stress and relapse risk

    Mon, Apr 04, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: David Eddie, Massachusetts General Hospital

    Talk Title: Towards a biosensor-driven, just-in-time relapse prevention tool for substance use disorder: Identifying neurocardiac biomarkers of stress and relapse risk

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

    Abstract: Substance use disorders carry tremendous personal and societal costs, and despite best patient and clinical efforts, relapse is common. Much research has sought to identify psychosocial risk factors for addiction relapse, but much less attention has been paid to how psychophysiological impairment may confer risk. In this talk, I will highlight how stress and central autonomic network dysregulation reflected by reduced heart rate variability (HRV) may heighten risk for individuals in early alcohol use disorder (AUD) recovery, showing that HRV can be used to predict subsequent alcohol use. I will also show preliminary findings from a study that aims to use smartwatches and machine learning to identify stress states, with the goal of developing a just-in-time relapse prevention tool for individuals in early recovery from substance use disorder.

    Register in advance for this webinar at:
    https://usc.zoom.us/webinar/register/WN_Hcft0t87RQqrca66W5c8ug

    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 Eddie, Ph.D. is a research scientist at Massachusetts General Hospital's Recovery Research Institute and Center for Addiction Medicine, a clinical psychologist in Massachusetts General Hospital's Department of Psychiatry, and an assistant professor at Harvard Medical School. His current projects include an NIAAA supported study developing a biosensor driven just-in-time intervention for substance use disorders, and a NIDA supported project assessing the efficacy of a novel mutual-help addiction recovery program based on physical activity.


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

    Webcast: https://usc.zoom.us/webinar/register/WN_Hcft0t87RQqrca66W5c8ug

    Location: Online - Zoom Webinar

    WebCast Link: https://usc.zoom.us/webinar/register/WN_Hcft0t87RQqrca66W5c8ug

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • CS Colloquium: Suguman Bansal (University of Pennsylvania) - Specification-Guided Policy Synthesis

    Tue, Apr 05, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Suguman Bansal , University of Pennsylvania

    Talk Title: Specification-Guided Policy Synthesis

    Series: CS Colloquium

    Abstract: Policy synthesis or algorithms to design policies for computational systems is one of the fundamental problems in computer science. Standing on the shoulders of simplified yet concise task-specification using high-level logical specification languages, this talk will cover synthesis algorithms using two contrasting approaches. First, the classical logic-based approach of reactive synthesis; Second, the modern learning-based approach of reinforcement learning. This talk will cover our scalable and efficient state-of-the-art algorithms for synthesis from high-level specifications using both these approaches, and investigate whether formal guarantees are possible. We will conclude with a forward-looking view of these contributions to trustworthy AI.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Suguman Bansal is an NSF/CRA Computing Innovation Postdoctoral Fellow at the University of Pennsylvania, mentored by Prof. Rajeev Alur. Her primary area of research is Formal Methods and Programming Langauge, and her secondary area of research is Artificial Intelligence.

    https://suguman.github.io/

    Host: Mukund Raghothaman

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Daniel Grier (University of Waterloo) - The Complexity of Near-Term Quantum Computers

    Tue, Apr 05, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Daniel Grier, University of Waterloo

    Talk Title: The Complexity of Near-Term Quantum Computers

    Series: CS Colloquium

    Abstract: Quantum computing is at an exciting moment in its history, with some high-profile experimental successes in building programmable quantum devices. That said, these quantum devices (at least in the near term) will be restricted in several ways, raising questions about their power relative to classical computers. In this talk, I will present three results which give us a better understanding of these near-term quantum devices, revealing key features which make them superior to their classical counterparts.

    First, I will show that constant-depth quantum circuits can solve problems that cannot be solved by any constant-depth classical circuit consisting of AND, OR, NOT, and PARITY gates---giving the largest-known unconditional separation between natural classes of quantum and classical circuits. Second, I will show that these quantum circuits can nevertheless be simulated quickly by classical algorithms that have no depth restriction, emphasizing the role that depth plays in provable quantum advantage. Finally, I will address some of the experimental challenges in implementing linear optical quantum computers, and I will prove that they outperform classical computers using standard conjectures but in more practical experimental regimes.


    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Daniel is a postdoctoral researcher at the Institute for Quantum Computing at the University of Waterloo. He received his PhD in Computer Science at MIT, where he was advised by Scott Aaronson and was supported by an NSF Graduate Research Fellowship. His research lies at the intersection of complexity theory and quantum computing, with a particular focus on the power of near-term quantum computing devices.

    Host: Ramesh Govindan

    Location: online only

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ISE 651 Epstein Seminar

    Tue, Apr 05, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Timothy C. Y. Chan, Professor, Dept. of Mechanical & Industrial Engineering, University of Toronto

    Talk Title: An Inverse Optimization Approach to Measuring Clinical Pathway Concordance

    Host: Prof. Suvrajeet Sen

    More Information: April 5, 2022.pdf

    Location: Online/Zoom

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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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, Apr 06, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Aaron Johnson, Mechanical Engineering at Carnegie Mellon University

    Talk Title: The Trouble with Contact: State Estimation and Control Generation for Discontinuous Systems

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

    Abstract: Contact with the outside world is challenging for robots due to its inherently discontinuous nature -- when a foot or hand is touching a surface the forces are completely different than if it is just above the surface. However, most of our computational and analytic tools for planning, learning, and control assume continuous (if not smooth or even linear) systems. Simple models of contact make assumptions (like plasticity and coulomb friction) that are known to not only be wrong physically but also inconsistent. In this talk I will present techniques for overcoming these challenges in order to adapt smooth methods to systems that have changing contact conditions. In particular I will focus on two topics: First, I will present the "Salted Kalman Filter" for state estimation over hybrid systems. Second, I will show a few techniques for generating new controllers with changing contact conditions, using both higher-order direct collocation and hybrid iLQR.

    Biography: Prof. Johnson is an Assistant Professor in Mechanical Engineering at Carnegie Mellon University, working on legged robots, adaptive controls, contact-rich manipulation, physics based planning & learning, and terrain manipulation as director of the Robomechanics Lab. Previously, his postdoc focused on convergent manipulation planning algorithms in the Personal Robotics Lab at Carnegie Mellon University. He received his PhD in 2014 on self-manipulation and dynamic behaviors on legged robots (among other things) in Kod*lab at the University of Pennsylvania. He is the recipient of the NSF Career award, the ARO Young Investigator Award, and the CMU George Tallman Ladd Research Award.

    Host: Pierluigi Nuzzo and Feifei Qian

    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, Apr 06, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Shreyas Mandre, University of Warwick, UK

    Talk Title: Functional interpretation for transverse arches of human foot

    Abstract: Fossil record indicates that the emergence of arches in human ancestral feet coincided with a transition from an arboreal to a terrestrial lifestyle. Propulsive forces exerted during walking and running load the foot under bending, which is distinct from those experienced during arboreal locomotion. I will present mathematical models with varying levels of detail to illustrate a simple function of the transverse arch. Just as we curve a dollar bill in the transverse direction to stiffen it while inserting it in a vending machine, the transverse arch of the human foot stiffens it for bending deformations. A fundamental interplay of geometry and mechanics underlies this stiffening -- curvature couples the soft out-of-plane bending mode to the stiff in-plane stretching deformation. In addition to presenting a functional interpretation of the transverse arch of the foot, this study also indicates a classification of flat feet based on the skeletal geometry and mechanics.

    Biography: Mandre is an applied mathematician, an engineer, and a scientist. Before moving to Warwick, he served as an Assistant Professor in the School of Engineering at Brown University from 2010 to 2019. He was also a Lecturer in Applied Mathematics at Harvard University. He received my Ph.D. in Mathematics from the University of British Columbia in 2006. His undergraduate education was in Mechanical Engineering from the Indian Institute of Technology Bombay followed by an M.S. from Northwestern University in the same subject. His research spans continuum mechanics, biomechanics, and applied mathematics, with applications to biology and 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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  • CS Colloquium: Geoff Pleiss (Columbia University) - Bridging the Gap Between Deep Learning and Probabilistic Modeling

    Thu, Apr 07, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Geoff Pleiss , Columbia University

    Talk Title: Bridging the Gap Between Deep Learning and Probabilistic Modeling

    Series: CS Colloquium

    Abstract: Deep learning excels with large-scale unstructured data - common across many modern application domains - while probabilistic modeling offers the ability to encode prior knowledge and quantify uncertainty - necessary for safety-critical applications and downstream decision-making tasks. I will discuss examples from my research that bridge the gap between these two learning paradigms. The first half will show that insights from deep learning can improve the practicality of probabilistic models. I will discuss work that scales Gaussian process regression, a common probabilistic model, to datasets two orders of magnitude larger than previously reported. The second half will show that probabilistic methods can improve our understanding of deep learning. I will demonstrate that Gaussian process theory uncovers new insights about the effects of width and depth in neural networks. I will conclude with ongoing efforts to quantify neural network uncertainty, develop new inductive biases, and other work at the intersection of deep learning and probabilistic modeling.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Geoff Pleiss is a postdoctoral researcher at Columbia University, hosted by John Cunningham, with affiliations in the Department of Statistics and the Zuckerman Institute. He obtained his Ph.D. in Computer Science from Cornell University, advised by Kilian Weinberger, and his B.Sc. from Olin College of Engineering. His research interests are broadly situated in machine learning, including neural networks, Gaussian processes, uncertainty quantification, and scalability. Geoff is also the co-founder and maintainer of the GPyTorch software framework.

    Host: Robin Jia

    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, Apr 07, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Jiaqi Ma, Assoc Director, UCLA Institute of Transportation Studies, University of California, Los Angeles

    Talk Title: OpenCDA: An Open Cooperative Driving Automation Research Framework

    Abstract: This presentation introduces OpenCDA, an open co-simulation-based research engineering framework integrated with prototype cooperative driving automation (CDA, SAE J3216) pipelines that contains perception, localization, planning, control, and V2X communication modules. The purpose of the framework is to take an integrated approach to CDA research that considers closed-loop autonomy to investigate the performance of automated driving components driven by conventional and AI algorithms and their impacts on resultant vehicular and traffic behavior under various environments. It not only enables CDA studies in a CARLA -SUMO co-simulation environment but also provides rich research pipelines (i.e., open-source codes for basic and advanced CDA modules) and training-testing datasets. It supports various levels and categories of information sharing and cooperation between automated vehicles in simulation testing. OpenCDA also offers benchmark testing scenarios, baseline maps, state-of-the-art benchmark algorithms, and selected evaluation metrics. Two recent research on cooperative perception (i.e., OpenCOOD) and cooperative vehicle platooning are discussed to show how OpenCDA enables cutting-edge CDA research.




    Biography: Dr. Jiaqi Ma is an Associate Professor at the UCLA Samueli School of Engineering and Associate Director UCLA Institute of Transportation Studies. He has led and managed many research projects funded by U.S. DOT, NSF, state DOTs, and other federal/state/local programs covering areas of smart transportation systems, such as vehicle-highway automation, Intelligent Transportation Systems (ITS), connected vehicles, shared mobility, and large-scale smart system modeling and simulation, and artificial intelligence and advanced computing applications in transportation. He is an Associate Editor of the IEEE Open Journal of Intelligent Transportation Systems and Journal of Intelligent Transportation Systems. He is Member of the Transportation Research Board (TRB) Standing Committee on Vehicle-Highway Automation, Member of TRB Standing Committee on Artificial Intelligence and Advanced Computing Applications, Member of American Society of Civil Engineers (ASCE) Connected & Autonomous Vehicles Impacts Committee, Co-Chair of the IEEE ITS Society Technical Committee on Smart Mobility and Transportation 5.0.


    Host: Dr. Jim Moore and Dr. Ketan Savla

    More Info: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659

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

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

    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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  • CS Colloquium: Hussein Sibai (UC Berkeley) - Towards Physics-aware Trustworthy Autonomy

    Thu, Apr 07, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Hussein Sibai , UC Berkeley

    Talk Title: Towards Physics-aware Trustworthy Autonomy

    Series: CS Colloquium

    Abstract: Designing trustworthy autonomous systems is a looming challenge in several domains. Symbolic reasoning and verification can complement purely data-driven approaches by exploiting knowledge of structure and code, providing rigorous safety assurances, explaining why designs work, and helping find edge-cases quickly. In this talk, I will discuss recent results that use knowledge about physical laws, such as symmetries, to boost the scalability of formal verification of autonomous systems. The boosting benefits both data-driven and model-based analysis. My tool SceneChecker embodies these algorithms and data structures that use knowledge of symmetries to save verification algorithms from repeating expensive reachability computations. It implements a counterexample-guided abstraction-refinement (CEGAR) verification algorithm that compresses models by combining symmetric states. SceneChecker has been successful in verifying complex scenarios involving ground and aerial vehicles. In the second half, I will present results developed using notions from topological entropy to relate knowledge of physical laws governing a system with data requirements in solving estimation and verification problems. These results can give physics-aware lower-bounds that can guide future autonomy design processes.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Hussein Sibai is a Postdoctoral Scholar at UC Berkeley, advised by Murat Arcak and Sanjit Seshia. He obtained his Ph.D. in Electrical and Computer Engineering from the University of Illinois Urbana-Champaign (UIUC) in December 2021, advised by Sayan Mitra. He received his bachelor's degree in Computer and Communication Engineering from the American University of Beirut and a master's degree in Electrical and Computer Engineering from UIUC. His research interests are in formal methods, control theory, and machine learning. Hussein has won the best poster award in HSCC 2018 and best paper nominations at HSCC 2017 and ATVA 2019. His work has been recognized by the Rambus fellowship, the Ernest A. Reid fellowship, the MAVIS Future Faculty fellowship, and the ACM SIGBED gold medal for the graduate category in the student research competition in CPS Week 21.

    Host: Jyo Deshmukh

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

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ECE-EP Seminar - Jae-sun Seo, Friday, April 8th at 10am via Zoom

    Fri, Apr 08, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Jae-sun Seo, Arizona State University

    Talk Title: Energy-Efficient AI Chip Designs with Digital and Analog Circuits

    Abstract: AI algorithms have been widespread across many practical applications, e.g. convolutional neural networks (CNNs) for computer vision, long short-term memory (LSTM) for speech recognition, etc., but state-of-the-art algorithms are compute-/memory-intensive, posing challenges for AI hardware to perform inference and training tasks with high throughput and low power consumption, especially on area-/energy-constrained edge devices.
    In this talk, I will present our recent research of several energy-efficient AI ASIC accelerators on both all-digital chips and analog/mixed-signal circuit based chips. These include (1) a 40nm CNN inference accelerator with conditional computing and low external memory access, (2) a 28nm CNN training accelerator exploiting dynamic activation/weight sparsity, and (3) a 28nm programmable in-memory computing (IMC) inference accelerator integrating 108 capacitive-coupling-based IMC SRAM macros. We will discuss the digital/analog circuits and architecture design, as well as hardware-aware algorithms employed for the proposed energy-efficient AI accelerators. Based on the demonstrated advantages and challenges of digital and analog AI chip designs, emerging research directions for new AI hardware with new device/circuit/architecture/algorithm design considerations will be discussed.

    Biography: Jae-sun Seo received the Ph.D. degree from the University of Michigan, Ann Arbor in 2010. From 2010 to 2013, he was with IBM T. J. Watson Research Center, working on the DARPA SyNAPSE project and next-generation processor designs. Since 2014, he has been with Arizona State University, where he is currently an Associate Professor in the School of ECEE. He was a visiting faculty at Intel Circuits Research Lab in 2015. His research interests include efficient hardware design of machine learning algorithms and neuromorphic computing. Dr. Seo was a recipient of IBM Outstanding Technical Achievement Award (2012), NSF CAREER Award (2017), and Intel Outstanding Researcher Award (2021). He has served on the technical program committees for ISSCC, MLSys, DAC, DATE, ICCAD, etc.

    Host: ECE-Electrophysics

    More Information: Jae-sun Seo Flyer.pdf

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • Advanced Manufacturing Seminar

    Fri, Apr 08, 2022 @ 10:00 AM - 11:30 AM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: John Hart, Massachusetts Institute of Technology

    Talk Title: The Trajectory of Metal Additive Manufacturing

    Abstract: Manufacturing of metal components is essential to every major industry, and involves complex supply chains, consumes significant natural resources, and sometimes still uses ancient techniques. Conversely, additive manufacturing (AM) promises to, ultimately, digitize the shaping of components and enable distributed production. I will highlight recent work from my research group at MIT and collaborators on metal AM including discrete element simulation of powder spreading coupled with X-ray microscopy for layer quality control; a new concept for drop-on-demand metal printing; and physics-based cost modeling to guide the deployment of AM at scale. I will also discuss our efforts in manufacturing education and workforce training.

    Biography: John Hart is Professor of Mechanical Engineering, Director of the Center for Additive and Digital Advanced Production Technologies, and Director of the Laboratory for Manufacturing and Productivity, at MIT. His research group at MIT, the Mechanosynthesis Group focuses on science and technology of production, including research in additive manufacturing, nanostructured materials, and precision machine design. In 2017 and 2018, respectively, he received the MIT Ruth and Joel Spira Award for Distinguished Teaching in Mechanical Engineering and the MIT Keenan Award for Innovation in Undergraduate Education. He is a co-founder of Desktop Metal and VulcanForms, and a Board Member of Carpenter Technology Corporation.

    Host: Center for Advanced Manufacturing

    More Info: https://usc.zoom.us/webinar/register/WN_lp3nfkY6TQ6brG0kB-c2Ag

    Webcast: https://usc.zoom.us/webinar/register/WN_lp3nfkY6TQ6brG0kB-c2Ag

    WebCast Link: https://usc.zoom.us/webinar/register/WN_lp3nfkY6TQ6brG0kB-c2Ag

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • CS Colloquium: Priya Donti (Carnegie Mellon University) - Optimization-in-the-loop AI for energy and climate

    Fri, Apr 08, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Priya Donti , Carnegie Mellon University

    Talk Title: Optimization-in-the-loop AI for energy and climate

    Series: CS Colloquium

    Abstract: Addressing climate change will require concerted action across society, including the development of innovative technologies. While methods from artificial intelligence (AI) and machine learning (ML) have the potential to play an important role, these methods often struggle to contend with the physics, hard constraints, and complex decision-making processes that are inherent to many climate and energy problems. To address these limitations, I present the framework of "optimization-in-the-loop AI," and show how it can enable the design of AI models that explicitly capture relevant constraints and decision-making processes. For instance, this framework can be used to design learning-based controllers that provably enforce the stability criteria or operational constraints associated with the systems in which they operate. It can also enable the design of task-based learning procedures that are cognizant of the downstream decision-making processes for which a model's outputs will be used. By significantly improving performance and preventing critical failures, such techniques can unlock the potential of AI and ML for operating low-carbon power grids, improving energy efficiency in buildings, and addressing other high-impact problems of relevance to climate action.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Priya Donti is a Ph.D. Candidate in Computer Science and Public Policy at Carnegie Mellon University. Her research explores methods to incorporate physics and hard constraints into deep learning models, in order to enable their use for forecasting, optimization, and control in high-renewables power grids. She is also a co-founder and chair of Climate Change AI, an initiative to catalyze impactful work in climate change and machine learning. Priya is a recipient of the MIT Technology Review's 2021 "35 Innovators Under 35" award, the Siebel Scholarship, the U.S. Department of Energy Computational Science Graduate Fellowship, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good.

    Host: Bistra Dilkina

    Location: Ronald Tutor Hall of Engineering (RTH) - 115

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CILQ Internal Seminar

    Fri, Apr 08, 2022 @ 12:00 PM - 01:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Keith Chugg, Professor, USC

    Talk Title: Co-Design of Algorithms and Hardware for Deep Neural Networks

    Abstract: Neural networks are in wide use in cloud computing platforms. This includes inference and training with the latter typically performed on programmable processors with multiply-accumulate (MAC) accelerator arrays (e.g., GPUs). In many applications, it can be describable to train on an edge device or using energy efficient application specific circuits. In this talk I will present some research results on application specific hardware acceleration methods for neural networks. Pre-defined sparsity is a method to reduce the complexity of training and inference. In contrast to pruning approaches which remove edges/weights during or after training, this approach sets a pre-defined pattern of sparse connection prior to training and holds this pattern fixed during training and inference. This allows one to design the pattern of sparsity to match a specific hardware acceleration architecture. We also consider Logarithmic Number Systems (LNS) for implementation of training. With LNS, operations are performed on the log of the quantities and therefore multiplies are simplified to addition while additions are more complex in the log domain. We present some preliminary results for LNS training and highlight ongoing challenges in applying this to larger, more complex networks. In many of these approaches we borrow from the design and implementation of iterative decoders for digital communication systems.

    Host: CILQ

    Webcast: https://usc.zoom.us/j/92417517950?pwd=WUkycy90cndVQko5R3RhQ1U3STBDdz09

    More Information: ChuggSeminar-Apr8-2022.pdf

    Location: via zoom

    WebCast Link: https://usc.zoom.us/j/92417517950?pwd=WUkycy90cndVQko5R3RhQ1U3STBDdz09

    Audiences: Everyone Is Invited

    Contact: Corine Wong

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  • ECE-EP Seminar - Jie Gu, Monday, April 11th at 9am via Zoom

    Mon, Apr 11, 2022 @ 09:00 AM - 10:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Jie Gu, Northwestern University

    Talk Title: Efficient On-chip Neural Architecture and Data Processing in the Era of Domain-specific Computing and AI

    Abstract: In this new era of data-driven domain-specific computing, the integrated circuits, serving as the cornerstones of modern electronic devices, are facing tremendous challenges in meeting the ever-growing data processing demand under staggering technology improvement. It is clear that conventional Von-Neumann architecture is no longer sufficient for the ubiquitous AI and many newly-arrived complex computing tasks. As a result, it is critical to look for new computing architecture that delivers the most efficient computing and data processing solutions. In this talk, I will first discuss our recent developments of a special "neural CPU" processor at the conjunction of Von-Neumann and deep learning architectures to establish a new computing platform where general-purpose computing is incorporated into the framework of deep learning accelerators achieving significant end-to-end performance enhancement and data movement reduction. Second, I will discuss efficient data processing solutions for domain-specific computing using examples of a sparse convolutional neural network accelerator for 3D/4D point-cloud image classification and efficient data processing for wirelessly powered human machine interface System-on-Chip (SoC) with embedded machine learning capabilities. Demonstrations of test chips using standard CMOS process will be used to show the benefits of the proposed solutions in comparison with the conventional implementations.

    Biography: Jie Gu is currently an associate professor in Northwestern University. He received his B.S. degree from Tsinghua University, M.S. degree from Texas A&M University and Ph.D. degree from University of Minnesota. From 2008 to 2010, he was with Texas Instruments, Dallas, TX on research and developments of ultra-low voltage mobile processors for smartphones. From 2011 to 2014, he was with Maxlinear leading developments of home multi-media broadband SoC chips. He joined ECE department in Northwestern University from 2015 working on novel circuit and architecture for low power microprocessors and machine learning accelerators. He is a recent recipient of NSF CAREER award.

    Host: ECE-Electrophysics

    More Info: https://usc.zoom.us/j/93576256328?pwd=OUlzMFYxVzVTY1cwNit5NFR6Nmdmdz09

    More Information: Jie Gu Flyer.pdf

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • CAIS Seminar: Rayid Ghani (Carnegie Mellon University) - Practical Lessons and Challenges in Building Fair and Equitable AI/ML Systems

    Mon, Apr 11, 2022 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Rayid Ghani, Carnegie Mellon University

    Talk Title: Practical Lessons and Challenges in Building Fair and Equitable AI/ML Systems

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

    Abstract: As organizations become more aware of the need to build ML/AI systems that result in fair and equitable outcomes, they have started to struggle with operationalizing that need. In this talk, I'll discuss lessons learned over the past few years working with various government agencies and non-profits across health, criminal justice, social services, education, and economic & workforce development on how those organizations view this challenge, how they're attempting to design ML/AI systems, and what gaps exist in the work that Fair ML researchers have been producing. I'll also discuss some examples of methods and tools that were useful in those collaborations and resulted in more equitable impact through the use of ML.

    Register in advance for this webinar at:

    https://usc.zoom.us/webinar/register/WN_zr43DpG2SKaIj-rspOahZA

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

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Rayid Ghani is a Professor in Machine Learning and Public Policy at Carnegie Mellon University focused on developing and using AI/Machine Learning/Data Science to help tackle large public policy and societal challenges in a fair and equitable manner. Among other areas, Rayid works with governments and non-profits in policy areas such as health, criminal justice, education, public safety, economic development, and urban infrastructure. Before joining Carnegie Mellon University, Rayid was the Founding Director of the Center for Data Science & Public Policy, Research Associate Professor in Computer Science, and a Senior Fellow at the Harris School of Public Policy at the University of Chicago. Previously, Rayid was the Chief Scientist of the Obama 2012 Election Campaign.


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

    Webcast: https://usc.zoom.us/webinar/register/WN_zr43DpG2SKaIj-rspOahZA

    Location: Online - Zoom Webinar

    WebCast Link: https://usc.zoom.us/webinar/register/WN_zr43DpG2SKaIj-rspOahZA

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Remarkable Trajectory Lecture: Paul S. Rosenbloom (USC) - From Designing Minds to Mapping Disciplines

    Tue, Apr 12, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Paul S. Rosenbloom, University of Southern California

    Talk Title: From Designing Minds to Mapping Disciplines

    Series: Remarkable Trajectory Lecture Series

    Abstract: Designing minds involves understanding the fixed mechanisms that combine to yield a mind as a basis for building both integrated models of human cognition and general AI systems. My trajectory here began in the mid-to-late 1970s with rule-based systems, and evolved through a sequence of more elaborate cognitive architectures -“ Xaps, Soar, and Sigma. It has also included recent efforts to understand minds more abstractly, in terms of a Common Model of Cognition and dichotomic maps of architectural mechanisms. Mapping disciplines involves understanding their essences and systematically structuring their compositions. My trajectory here began with a relational map of computing as a great scientific domain and continued with recent work on dichotomic maps of the technologies underlying AI and cognitive science. Following a dab of personal background, I will overview these two trajectories, and then wrap up with a bit of speculation on their affinity and a sampling of maxims extracted from my career as a whole.

    Register in advance for this online event at:

    https://usc.zoom.us/webinar/register/WN__4hJussyRBus_HIFLcgigQ

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

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Paul S. Rosenbloom recently retired as a Professor of Computer Science in the Viterbi School of Engineering at the University of Southern California and Director for Cognitive Architecture Research at the Institute for Creative Technologies. He also was a member of USC's Information Sciences Institute for two decades, ending as its deputy director, and earlier was faculty at Carnegie Mellon University and Stanford University (with a joint appointment in Computer Science and Psychology). His research has focused on cognitive architectures (models of the fixed structures and processes that together yield a mind), the Common Model of Cognition (a partial consensus about the structure of a human-like mind), dichotomic maps (structuring the space of technologies underlying AI and cognitive science), and the relational model of computing as a great scientific domain (akin to the physical, life and social sciences). He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the American Association for the Advancement of Science (AAAS), and the Cognitive Science Society; and with John E. Laird was awarded the Herbert A. Simon Prize for Advances in Cognitive Systems. He has served as Councilor and Conference Chair for AAAI; Chair of ACM SIGART (now SIGAI); Chair of the Viterbi Engineering Faculty Council; and President of the USC Faculty.


    Host: USC Viterbi School of Engineering Department of Computer Science

    Webcast: https://usc.zoom.us/webinar/register/WN__4hJussyRBus_HIFLcgigQ

    Location: Online - Zoom Webinar

    WebCast Link: https://usc.zoom.us/webinar/register/WN__4hJussyRBus_HIFLcgigQ

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • ISE 651 Epstein Seminar

    Tue, Apr 12, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Daniel Apley, Professor, Dept. of Industrial Engineering and Management Sciences, Northwestern University

    Talk Title: Interpreting Black-Box Supervised Learning Models Via Accumulated Local Effects

    Host: Prof. Qiang Huang

    More Information: April 12, 2022.pdf

    Location: Online/Zoom

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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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 (Part 1)

    Wed, Apr 13, 2022 @ 02:00 PM - 02:30 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Prakash Sarathy, Northrop Grumman Global Products (1st Speaker)

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

    Abstract: Increasing tempo and complexity of missions for aircraft and space vehicles has driven the design of such systems to higher levels of autonomous operations. Consequently, the challenges of ensuring a safe and secure operational regime have been rapidly escalating. While a number of techniques, new and old, are available to address different facets of these challenges, what seems to be missing is some cohesive approach or framework that can support the total design lifecycle while lowering the cost and risk of a successful design and deployment of such cyber-physical systems.

    This talk will focus on some of these challenges and where overlaps exist in the safety and security needs and in its eventual resolution within a software and hardware design. Some notable approaches and methodologies will be discussed briefly to highlight the potential of such convergence as well as some mention of current state-of-practice in software and hardware verification, validation, and accreditations (VV&A).

    Biography: Dr. Prakash Sarathy is the Chief Engineer for common mission processing subsystem for Northrop Grumman Global Products center. He earned his doctoral degree in Aerospace Engineering from Syracuse University. He has held positions as post-doctoral fellow, research engineer and tenured engineering faculty in his prior career. He has over 30 years of experience in the area of software applied to aerospace engineering, providing technical and project/program management and oversight for advanced technology programs requiring accelerated risk burn down and rapid maturation. His technical expertise areas include cooperative distributed architectures for multi-agent systems, cooperative decision frameworks, agent architectures for mission management, aggregate control of distributed assets, user interface design for aggregate level situational awareness. Insertion of neural networks, evolutionary computing and emergent behavior to decision making paradigms. This software engineering expertise coupled with his in-depth experience in linear and nonlinear dynamics of vehicle systems, applied to guidance, navigation and control of aircraft, spacecraft, and robots as well as of real-time and embedded simulations, high fidelity modeling, implementation, VV&A, formal methods, and testing, provide an excellent framework for the challenges of next generation autonomous aircraft. He has spearheaded an effort to assemble a feasible set of methodologies to establish bounded behavior assurance for advanced autonomous missions under contested operating conditions.

    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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  • Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series (Part 2)

    Wed, Apr 13, 2022 @ 02:30 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Marlon Marquez, Northrop Grumman Space Systems (2nd Speaker)

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

    Abstract: Increasing tempo and complexity of missions for aircraft and space vehicles has driven the design of such systems to higher levels of autonomous operations. Consequently, the challenges of ensuring a safe and secure operational regime have been rapidly escalating. While a number of techniques, new and old, are available to address different facets of these challenges, what seems to be missing is some cohesive approach or framework that can support the total design lifecycle while lowering the cost and risk of a successful design and deployment of such cyber-physical systems.

    This talk will focus on some of these challenges and where overlaps exist in the safety and security needs and in its eventual resolution within a software and hardware design. Some notable approaches and methodologies will be discussed briefly to highlight the potential of such convergence as well as some mention of current state-of-practice in software and hardware verification, validation, and accreditations (VV&A).

    Biography: Marlon Marquez is a Consulting Engineer at NG Space Systems. Mr. Marquez has prior experience designing with Intel, Power PC, and ARM microprocessor technologies. He has domain knowledge with state-of-the- art cyber security and anti-tamper infrastructures including TPMs, secure hypervisor technologies and multi-level security concepts. He has experience with Operating System technologies, Pub/Sub application development, and Kernel development. He is an FPGA subject matter expert and has extensive experience with FPGA and Processor interfaces. He holds two USPTO patents and presented ASIC technology at IEEE. He has a BSEE from UCLA, was an MS candidate at Cal State Northridge in Electrical and Computing Engineering and has an MBA from Pepperdine University.

    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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  • CS Colloquium: Bradley Hayes (University of Colorado Boulder) - Human-robot teaming is a lot less dangerous with communication: Improving Human-Robot Teaming Performance in Partially Observable Environments with Augmented Reality

    Wed, Apr 13, 2022 @ 04:30 PM - 05:50 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Bradley Hayes, University of Colorado Boulder

    Talk Title: Human-robot teaming is a lot less dangerous with communication: Improving Human-Robot Teaming Performance in Partially Observable Environments with Augmented Reality

    Series: Computer Science Colloquium

    Abstract: Clear and frequent communication is a foundational aspect of collaboration. Effective communication not only enables and sustains the shared situational awareness necessary for adaptation and coordination during human-robot teaming, but is often a requirement given the opaque nature of decision-making in autonomous systems. In this talk I will share some of our recent work using augmented reality as a mode of visual communication to improve both human and robot safety and capability when working together, introducing insights into human behavior and compliance in safety-critical situations as well as novel algorithms for autonomous communication and collaboration in partially observable environments. The talk will conclude with a presentation of our ongoing work at the intersection of fast constrained motion planning for sequential manifold planning problems and augmented reality-assisted learning from demonstration.

    Prof. Bradley Hayes will give his talk in person at GFS 106 and we will also host the talk over Zoom.

    Register in advance for this webinar at:

    https://usc.zoom.us/webinar/register/WN_HgvCIbb7TDS6aOU1ksSI0A

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

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Bradley Hayes is an Assistant Professor of Computer Science at the University of Colorado Boulder, where he runs the Collaborative AI and Robotics (CAIRO) Lab and serves as co-director of the university's Autonomous Systems Interdisciplinary Research Theme. Brad's research develops techniques to create and validate autonomous systems that learn from, teach, and collaborate with humans to improve efficiency, safety, and capability at scale. His work primarily leverages novel approaches at the intersection of human-robot interaction and explainable artificial intelligence, providing autonomous systems with the ability to generalize skills with limited risk, to act safely and productively around humans, and to make human-autonomy teams more powerful than the sums of their parts. His continual efforts to systematically put humans and autonomous systems into often entertaining and occasionally productive situations has been featured by TEDx, Popular Science, Wired, and MIT Technology review, and has been recognized with best paper nominations from HRI, AAMAS, and RO-MAN. Brad also serves as CTO at Circadence, building high-fidelity simulation, test, and evaluation environments for cyber-physical systems at nation-state scale.


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_HgvCIbb7TDS6aOU1ksSI0A

    Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 106

    WebCast Link: https://usc.zoom.us/webinar/register/WN_HgvCIbb7TDS6aOU1ksSI0A

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Astani Civil and Environmental Engineering Seminar

    Thu, Apr 14, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Yue Yue Fan, University of California, Davis

    Talk Title: Physics-informed data analytics approaches using constrained optimization - exploiting domain knowledge and hard information in a transportation network

    Abstract: Please see attached abstract and bio.

    Host: Dr. Jim Moore

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

    More Information: YueYue Fan-Abstract_ Bio.pdf

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

    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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  • CS Colloquium: Joydeep Biswas (University of Texas at Austin) - Deploying Autonomous Service Mobile Robots, And Keeping Them Autonomous

    Thu, Apr 14, 2022 @ 04:10 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Joydeep Biswas, University of Texas at Austin

    Talk Title: Deploying Autonomous Service Mobile Robots, And Keeping Them Autonomous

    Series: Computer Science Colloquium

    Abstract: *New start time: 4:10 PM PT*

    Why is it so hard to deploy autonomous service mobile robots in unstructured human environments, and to keep them autonomous? In this talk, I will explain three key challenges, and our recent research in overcoming them: 1) ensuring robustness to environmental changes; 2) anticipating and overcoming failures; and 3) efficiently adapting to user needs.
    To remain robust to environmental changes, we build probabilistic perception models to explicitly reason about object permanence and distributions of semantically meaningful movable objects. By anticipating and accounting for changes in the environment, we are able to robustly deploy robots in challenging frequently changing environments.
    To anticipate and overcome failures, we introduce introspective perception to learn to predict and overcome perception errors. Introspective perception allows a robot to autonomously learn to identify causes of perception failure, how to avoid them, and how to learn context-aware noise models to overcome such failures.
    To adapt and correct behaviors of robots based on user preferences, or to handle unforeseen circumstances, we leverage representation learning and program synthesis. We introduce visual representation learning for preference-aware planning to identify and reason about novel terrain types from unlabelled human demonstrations. We further introduce physics-informed program synthesis to synthesize and repair programmatic action selection policies (ASPs) in a human-interpretable domain-specific language with several orders of magnitude fewer demonstrations than necessary for neural network ASPs of comparable performance.
    The combination of these research advances allows us to deploy a varied fleet of wheeled and legged autonomous mobile robots on the campus scale at UT Austin, performing tasks that require robust mobility both indoors and outdoors.

    ***Dr. Joydeep Biswas will give the 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_lWf_mXH3Qr2qtbHg1kbOYQ

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

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Joydeep Biswas is an assistant professor in the department of computer science at the University of Texas at Austin. He earned his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008, and M.S. and PhD in Robotics from Carnegie Mellon University in 2010 and 2014 respectively. From 2015 to 2019, he was assistant professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. His research spans perception and planning for long-term autonomy, with the ultimate goal of having service mobile robots deployed in human environments for years at a time, without the need for expert corrections or supervision. Prof. Biswas received the NSF CAREER award in 2021, an Amazon Research Award in 2018, and a JP Morgan Faculty Research Award in 2018.


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_lWf_mXH3Qr2qtbHg1kbOYQ

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

    WebCast Link: https://usc.zoom.us/webinar/register/WN_lWf_mXH3Qr2qtbHg1kbOYQ

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Astani Civil and Environmental Engineering Seminar

    Fri, Apr 15, 2022 @ 10:00 AM - 12:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. YueYue Fan, and Dr. Daan Liang, National Science Foundation

    Talk Title: Funding opportunities for research related to infrastructure systems and smart and connected communities at National Science Foundation

    Abstract: In this meeting, Dr. Yueyue Fan and Dr. Daan Liang from the Civil, Mechanical, & Manufacturing Innovation CMMI Division at NSF will discuss funding opportunities related to infrastructure systems, disasters and resilience, and smart connected communities at the National Science Foundation. Challenges brought by problems in these areas often require cross-disciplinary efforts from engineering, mathematics, and physical and social sciences. Therefore, we would welcome audience from broad academic communities. Specifically, We will focus on four NSF programs: Civil Infrastructure Systems CIS, Human, Disaster, and Built Environment HDBE, Cyber Physical Systems CPS, and Smart and Connected Communities S&CC, including their scopes, review criteria, and different expectations. Solicitations supporting large- scale research infrastructure development, including MRI and CDS&E, will also be discussed. We also hope to use this opportunity to gather your input regarding critical research gaps and research infrastructure needs.


    Host: Dr. James Moore

    Webcast: https://usc.zoom.us/j/95126666153 Passcode: 527255

    Location: Ray R. Irani Hall (RRI) - 321

    WebCast Link: https://usc.zoom.us/j/95126666153 Passcode: 527255

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • CS Colloquium: Mohamed Hussein (USC ISI) - Securing Machine Vision Models

    Fri, Apr 15, 2022 @ 10:00 AM - 11:00 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Mohamed Hussein, USC ISI

    Talk Title: Securing Machine Vision Models

    Abstract: Machine vision has evolved dramatically over the past decade, thanks to the deep learning revolution. Despite their remarkable performance, often surpassing humans, machine vision models are vulnerable to different types of attacks. This talk will focus on two types of attacks as well as methods to secure machine vision models against them. The first is presentation (or more commonly known as spoofing) attacks on biometric authentication systems, in which the attacker presents a fake physical instrument to the system, such as a printed face image, either to conceal their true identity or impersonate a different identity. I will show that combining the power of deep learning with multi-spectral sensing can effectively address this problem by distinguishing spoofing instruments from bona fide presentations. For the challenging makeup attack, I will show that using multi-spectral data, we can construct an image of a person without the applied makeup, and hence reveal their true identity. The second type of attack is adversarial attacks. In this type of attack, imperceptible perturbations can be applied to the input of a machine vision model to alter the model's prediction. I will present a new non-linear activation function, named Difference of Mirrored Exponential terms (DOME), which has the property of inducing compactness to the embedding space of a deep learning model. We found that combining the usage of DOME with adversarial training can boost the robustness against state of the art adversarial attacks. I will conclude by discussing my perspective on the challenges ahead regarding the security of machine vision models.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Dr. Mohamed E. Hussein is a Computer Scientist and a Research Lead at USC ISI. Dr. Hussein obtained his Ph.D. degree in Computer Science from the University of Maryland at College Park, MD, USA in 2009. Then, he spent close to two years as an Adjunct Member Research Staff at Mitsubishi Electric Research Labs, Cambridge, MA, before moving to Alexandria University, Egypt, as a faculty member. Prior to joining ISI in 2017, he spent three years at Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt. During his time as a faculty member in Egypt, Dr. Hussein was the PI/Co-PI on multiple industry and government funded research projects on Sign Language Recognition and Crowd Scene Analysis. He is currently a Co-PI for ISI's projects under IARPA's Odin and BRIAR programs and DARPA's GARD program.

    Host: CS Department

    Webcast: https://usc.zoom.us/j/98761669161

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

    WebCast Link: https://usc.zoom.us/j/98761669161

    Audiences: Everyone Is Invited

    Contact: Cherie Carter

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  • CS Colloquium: Beidi Chen (Stanford University) - Randomized Algorithms for Efficient Machine Learning Systems

    Fri, Apr 15, 2022 @ 02:00 PM - 03:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Beidi Chen, Stanford University

    Talk Title: Randomized Algorithms for Efficient Machine Learning Systems

    Series: CS Colloquium

    Abstract: Machine learning (ML) has demonstrated great promise in scientific discovery, healthcare, and education, especially with the rise of large neural networks. However, large models trained on complex and rapidly growing data consume enormous computational resources. In this talk, I will describe my work on exploiting model sparsity with randomized algorithms to accelerate large ML systems on current hardware with no drop in accuracy.

    I will start by describing SLIDE, an open-source system for efficient sparse neural network training on CPUs that has been deployed by major technology companies and academic labs. SLIDE blends Locality Sensitive Hashing with multi-core parallelism and workload optimization to drastically reduce computations. SLIDE trains industry-scale recommendation models on a 44 core CPU 3.5x faster than TensorFlow on V100 GPU with no drop in accuracy.

    Next, I will present Pixelated Butterfly, a simple yet efficient sparse training framework on GPUs. It uses a simple static block-sparse pattern based on butterfly and low-rank matrices, taking into account GPU block-oriented efficiency. Pixelated Butterfly trains up to 2.5x faster (wall-clock) than the dense Vision Transformer and GPT-2 counterparts with no drop in accuracy.

    I will conclude by outlining future research directions for further accelerating ML pipelines and making ML more accessible to the general community, such as software-hardware co-design, data-centric AI, and ML for scientific computing and medical imaging.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Beidi Chen is a postdoctoral scholar in the CS department at Stanford University, working with Prof. Christopher Ré. Her research focuses on large-scale machine learning and deep learning. Specifically, she designs and optimizes randomized algorithms (algorithm-hardware co-design) to accelerate large machine learning systems for real-world problems. Prior to joining Stanford, she received her Ph.D. from the CS department at Rice University, advised by Prof. Anshumali Shrivastava. She received a BS in EECS from UC Berkeley. She has held internships in Microsoft Research, NVIDIA Research, and Amazon AI. Her work has won Best Paper awards at LISA and IISA. She was selected as a Rising Star in EECS by MIT and UIUC.

    Host: Xiang Ren / Vatsal Sharan

    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, Apr 19, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Ermin Wei, Assistant Professor Dept. of Electrical & Computer Engineering; Dept. of Industrial Engineering & Management Sciences, Northwestern University

    Talk Title: Flexible and Faithful Federated Learning Methods

    Host: Dr. Meisam Razaviyayn

    More Information: April 19, 2022.pdf

    Location: Zoom/Online

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • Mork Family Department Spring Seminars - Prof. Lorenzo Mangolini

    Tue, Apr 19, 2022 @ 04:00 PM - 05:20 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Prof. Lorenzo Mangolini, University of California-Riverside

    Talk Title: TBA

    Host: Professor A.Hodge

    Location: Social Sciences Building (SOS) - B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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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, Apr 20, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Bichen Wu, Meta (former Facebook) Reality Labs

    Talk Title: Efficient Deep Learning for Computer Vision

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

    Abstract: Deep neural networks are empowering increasingly more applications in computer vision. To deploy deep learning to more devices (edge, mobile, AR/VR glasses), we need to tackle numerous challenges to make deep learning more efficient. In this talk, we will focus on two important aspects of efficiency: model efficiency and data efficiency. Improving model efficiency enables packing stronger AI capabilities to devices with limited compute, while better data efficiency unblocks more applications constrained by lack of data. This talk will first introduce the FBNet series of work [1-4], which studies neural architecture search (NAS) methods to automatically develop compute-efficient models to achieve better accuracy-efficiency trade-offs. For data efficiency, this talk will introduce OTTER [5], a data-efficient algorithm that uses language to train vision models to recognize images in a zero-shot manner -- being able to recognize new classes without needing extra labels.

    Papers related to this talk:
    [1] FBNetV1: Wu, Bichen, et al. "Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
    [2] FBNetV2: Wan, Alvin, et al. "Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.
    [3] FBNetV3: Dai, Xiaoliang, et al. "FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function." arXiv preprint arXiv:2006.02049 (2020).
    [4] FBNetV5: Wu, Bichen, et al. "FBNetV5: Neural Architecture Search for Multiple Tasks in One Run."
    [5] OTTER: Wu, Bichen, et al. "Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation."


    Biography: Dr. Bichen Wu is a research scientist at Meta (former Facebook) Reality Labs. His research is focused on efficient deep learning algorithms, models, and systems, aiming to bring AI capabilities to massive edge devices and applications. His paper on Neural Architecture Search -- FBNet, is among the top 0.01% highest cited computer science papers published in 2019. He obtained his Ph.D. from Berkeley AI Research, UC Berkeley and his Bachelor of Engineering from Tsinghua University in 2013.

    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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  • Astani Department of Civil and Environmental Engineering Seminar

    Thu, Apr 21, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Mr. Gerald Jerry Buckwalter, Chief Innovation Officer, American Society of Civil Engineers

    Talk Title: Future World Vision: Engineering the Future Built Environment

    Abstract: From climate change to autonomous vehicles, engineers are confronting a variety of environmental challenges, demographic shifts and technological changes that will require a drastic rethinking of how we build, operate, and maintain our infrastructure systems. Planning for the future is difficult for nearly every organization. ASCE decided to launch the Future World Vision project to help meet this challenge. We compiled and winnowed more than hundred global macrotrends to examine six important sociopolitical, economic, environmental, and technological trends as key drivers of change for future built infrastructure.

    Our desire is that the Future World Vision project will establish ASCE and civil engineers as bold thought leaders, provide a platform to envision the future built environment and ultimately optimize future system performance and the benefit to society, and be a next generation tool that interacts and resonates with those who will create the future built environment the next generation of civil engineers. The Future World Vision platform is an immersive computer model, using gaming engines, that will create virtual future worlds with evocative visuals, multiple characters and rich narratives that explore holistic city, community and neighborhood systems, including the cultural, social, economic, political, ethical and environmental aspects at different scales. This platform will enable engineers to ask the right questions about a future built environment that does not exist yet, contemplate solutions, postulate the resulting benefit to society well in advance of starting to design those solutions. This will enable us to better prepare engineers today for possible future needs and challenges.


    Biography: Gerald Jerry E. Buckwalter has more than 35 years of varied executive leadership in general management, business development, strategy and innovation, program operations and policy development. He has worked in infrastructure, electronics, information technology, commercial security, and technology services markets, spanning military, government, international and commercial domains.

    Mr. Buckwalter is the Chief Innovation Officer of ASCE and was formerly the Chief Operating and Strategy Officer. In that role, Jerry directs a forward-leaning strategic assessment and visualization project called Future World Vision where ASCE is creating a virtual and interactive computer model to assess potential built environments 50 years into the future. He also has his own consulting firm, Strategy Essentials, where he specializes in business, market, and technology strategic planning.

    Prior to joining ASCE, Mr. Buckwalter was a Northrop Grumman Corporate Director of Strategy. His responsibilities included reshaping the company business portfolio, mergers and acquisitions, long-term strategies, innovation initiatives and professional development. Jerry worked for many years coordinating company-wide homeland security business in border and transportation security, emergency preparedness and response, critical infrastructure protection and intelligence gathering.

    Mr. Buckwalter earned a degree in Physics from Monmouth University and has extensive continuing education at George Washington University and the Massachusetts Institute of Technology.


    Host: Dr. Burcin Becerik

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

    Location: Kaprielian Hall (KAP) - 209

    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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  • CS Colloquium: Kuldeep Meel (National University of Singapore) - Counting, Sampling, and Synthesis: The Quest for Scalability

    Mon, Apr 25, 2022 @ 09:00 AM - 10:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Kuldeep Meel, National University of Singapore

    Talk Title: Counting, Sampling, and Synthesis: The Quest for Scalability

    Abstract: The current generation of automated symbolic reasoning techniques excel at the qualitative tasks (i.e., when the answer is Yes
    or No) owing to the dramatic progress in satisfiability solving, also referred to as the SAT revolution. The advances in SAT afford us the luxury to focus on quantitative reasoning tasks, whose development is critical to reason about the increasingly interconnected and complex computing systems.

    In this talk, I will discuss the design of the next generation of automated reasoning techniques to perform higher-order tasks such as quantification (aka counting), sampling of representative behavior, and automated synthesis of systems. Naturally, these tasks are hard from a complexity-theoretic viewpoint, and therefore, our frameworks focus on tight integration of real-world applications, beyond the worst-case analysis algorithmic design and data-driven system design. This has allowed us to achieve significant advances in counting, sampling, and synthesis, providing a new algorithmic toolbox in formal methods, probabilistic reasoning, databases, and design verification. I will discuss the core design principles and the utility of the above techniques on various real applications, including quantitative analysis of AI systems and critical infrastructure resilience estimation.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Kuldeep Meel holds the NUS Presidential Young Professorship in the School of Computing at the National University of Singapore. His research interests lie at the intersection of Formal Methods and Artificial Intelligence. He is a recipient of the 2021 Amazon Research Award for Automated Reasoning, 2019 NRF Fellowship for AI, and was named AI's 10 to Watch by IEEE Intelligent Systems in 2020. His research program's recognition include the 2022 ACM SIGMOD Research Highlight, 2021 ICCAD Best Paper Award Nomination, "Best of PODS-21" invite from ACM TODS, "Best Papers of CAV-20" invite from FMSD journal, IJCAI-19 Sister conferences best paper award track invitation.

    He holds a Ph.D. from Rice University, co-advised by Supratik Chakraborty and Moshe Y. Vardi. His thesis work received the 2018 Ralph Budd Award for Best Ph.D. Thesis in Engineering and the 2014 Outstanding Masters Thesis Award from Vienna Center of Logic and Algorithms, IBM PhD Fellowship, and Best Student Paper Award at CP 2015.


    Host: Mukund Raghothaman

    Webcast: https://usc.zoom.us/j/99187341067

    WebCast Link: https://usc.zoom.us/j/99187341067

    Audiences: Everyone Is Invited

    Contact: Cherie Carter

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  • ISE 651 Epstein Seminar

    Tue, Apr 26, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Carolyn Conner Seepersad, J.Mike Walker Professor, Dept. of Mechanical Engineering, University of Texas, Austin

    Talk Title: Additive Manufacturing and Design Innovation: Challenges and Opportunities

    Host: Prof. Qiang Huang

    More Information: April 26, 2022.pdf

    Location: Online/Zoom

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • CS Distinguished Lecture: Henrik I. Christensen (UC San Diego) - Deploying autonomous vehicles for micro-mobility in urban environments

    Tue, Apr 26, 2022 @ 04:00 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Henrik I. Christensen, UC San Diego

    Talk Title: Deploying autonomous vehicles for micro-mobility in urban environments

    Series: Computer Science Distinguished Lecture Series

    Abstract: Autonomous vehicles are already widely deployed on the inter-states. Providing robust autonomous systems for urban environments is a more difficult challenge, as the road network is more complex, there are many more types of road-users (cars, bikes, pedestrians) and the potential interactions are more complex. In an urban environment it is also harder to use pre-computed HD-maps as the world is more dynamic. We study the design of micro-mobility solutions for the UCSD campus. In this presentation we will discuss an overall systems design, eliminating the need for HD-maps and use course topological maps such as Open Street Maps, fusing vision and lidar for semantic mapping /localization, detection and handling other road-users. Dynamic planning in the presence of other agents. The system has been deployed in multiple long-term test to evaluate performance across weather, season changes, etc. We will present both underlying methods, algorithms, and experimental insights. Finally, we will present some challenges for the future.

    Dr. Christensen will give the 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_M0LX4fKmSIqVhjLX05mWCg

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

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Henrik I Christensen is the Qualcomm Chancellor's Chair of Robot Systems and the director of robotics at UC San Diego. He is an academic, entrepreneur and investor. Dr. Christensen does research on a systems approach to robotics. Solutions need a solid theoretical basis, effective algorithms, a good implementation and must be evaluated using realistic scenarios. He has made contributions to computer vision, SLAM, and systems engineering. His research has been adopted by many companies. Henrik is also the main editor of the US National Robotics Roadmap (2009, 2013, 2016 and 2020). He is serving / has served on a significant number of editorial board (PAMI, IJRR, JFR, RAS, Aut Sys). He co-founded Robust.AI and Robo Global (AUM: $3.5B) and serves as a consultant to companies and agencies across 5 continents


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_M0LX4fKmSIqVhjLX05mWCg

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

    WebCast Link: https://usc.zoom.us/webinar/register/WN_M0LX4fKmSIqVhjLX05mWCg

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Astani Department of Civil and Environmental Engineering Seminar

    Thu, Apr 28, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Sara Billington, Chair and UPS Foundation Professor , Department of Civil and Environmental Engineering, Stanford University

    Talk Title: Hybrid Physical Plus Digital Spaces for Enhanced Sustainability and Wellbeing

    Abstract: Please see attached abstract bio and a photo. Thank you.

    Host: Dr. Burcin Becerik-Gerber

    More Info: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659

    More Information: S. Billington-abstract-bio.pdf

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

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • CS Colloquium: Malte Jung (Cornell University) - Teamwork with Robots

    Thu, Apr 28, 2022 @ 04:00 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Malte Jung, Cornell University

    Talk Title: Teamwork with Robots

    Series: Computer Science Colloquium

    Abstract: Research on Human-robot Interaction to date has largely focused on examining a single human interacting with a single robot. This work has led to advances in fundamental understanding about the psychology of human-robot interaction (e.g. how specific design choices affect interactions with and attitudes towards robots) and about the effective design of human-robot interaction (e,g. how novel mechanisms or computational tools can be used to improve HRI). However, the single-robot-single-human focus of this growing body of work stands in stark contrast to the complex social contexts in which robots are increasingly placed. While robots increasingly support teamwork across a wide range of settings covering search and rescue missions, minimally invasive surgeries, space exploration missions, or manufacturing, we have limited understanding of how groups people will interact with robots and how robots will affect how people interact with each other in groups and teams. In this talk I present empirical findings from several studies that show how robots can shape in direct but also subtle ways how people interact and collaborate with each other in teams.

    Dr. Jung will give the 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_WnzW6E8UQQiLTd8N1tZoVw

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

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Malte Jung is an Associate Professor in Information Science at Cornell University and the Nancy H. '62 and Philip M. '62 Young Sesquicentennial Faculty Fellow. His research brings together approaches from design and behavioral science to build understanding about how we can build robots that function better in group and team settings. His work has received several awards including an NSF CAREER award. He holds a Ph.D. in Mechanical Engineering, and a PhD Minor in Psychology from Stanford University. Prior to joining Cornell, Malte Jung completed a postdoc at the Center for Work, Technology, and Organization at Stanford University. He holds a Diploma in Mechanical Engineering from the Technical University of Munich.


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_WnzW6E8UQQiLTd8N1tZoVw

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

    WebCast Link: https://usc.zoom.us/webinar/register/WN_WnzW6E8UQQiLTd8N1tZoVw

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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