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Events for the 4th week of March
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ECE-S Seminar - Dr Shiry Ginosar
Mon, Mar 20, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Shiry Ginosar, Postdoctoral Fellow | University of California, Berkeley
Talk Title: Toward Artificial Social Intelligence
Abstract: As the covid pandemic made abundantly clear-”multi-faceted, face-to-face interaction is the most effective form of communication-”much more so than written text messages or phone calls. And yet, most current AI efforts focus primarily on text systems. In my work, I try to push the limits of machine perception systems toward artificially intelligent agents that can perceive and model the rich, multimodal signals of face-to-face human social interaction: speech and communicative gesture. I will cover several projects that take steps in this direction in the one-to-many scenario of lectures and monologues and one-on-one dyadic face-to-face communication. Through these examples, I will argue that it is possible to model minute, indescribable visual and auditory details of multi-faceted human communication using data-driven methods without relying on annotation. I will then broaden the discussion to questions in social intelligence, such as body language, abstract communicative motion, and spatiotemporal trends of social norms, and suggest directions for future inquiries.
Biography: Shiry Ginosar is a Computing Innovation Postdoctoral Fellow at UC Berkeley, advised by Jitendra Malik. She completed her Ph.D. in Computer Science at UC Berkeley, under the supervision of Alyosha Efros. Prior to joining the Computer Vision group, she was part of Bjoern Hartmann's Human-Computer Interaction lab at Berkeley. Earlier in her career, she was a Visiting Scholar at the CS Department of Carnegie Mellon University, with Luis von Ahn and Manuel Blum in the field of Human Computation. Between her academic roles, she spent four years at Endeca as a Senior Software Engineer. In her distant past, Shiry trained fighter pilots in F-4 Phantom flight simulators as a Staff Sergeant in the Israeli Air Force. Shiry's research has been covered by The New Yorker, The Wall Street Journal, and the Washington Post, amongst others. Her work has been featured on PBS NOVA, exhibited at the Israeli Design Museum and is part of the permanent collection of the Deutsches Museum. Her patent-pending research work inspired the founding of a startup. Shiry has been named a Rising Star in EECS, and is a recipient of the NSF Graduate Research Fellowship, the California Legislature Grant for graduate studies, and the Samuel Silver Memorial Scholarship Award for combining intellectual achievement in science and engineering with serious humanistic and cultural interests.
Host: Dr Antonio Ortega, aortega@usc.edu
Webcast: https://usc.zoom.us/j/93935933525?pwd=cVVWd2JoQzBhcXZuWDAzalp3eEZYUT09More Information: ECE Seminar Announcement 03.20.2023 - Shiry Ginosar.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/93935933525?pwd=cVVWd2JoQzBhcXZuWDAzalp3eEZYUT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Rika Antonova (Stanford University) - Enabling Self-sufficient Robot Learning
Mon, Mar 20, 2023 @ 11:00 AM - 12:00 PM
Computer Science
Conferences, Lectures, & Seminars
Speaker: Rika Antonova, Stanford University
Talk Title: Enabling Self-sufficient Robot Learning
Series: CS Colloquium
Abstract: Autonomous exploration and data-efficient learning are important ingredients for helping machine learning handle the complexity and variety of real-world interactions. In this talk, I will describe methods that provide these ingredients and serve as building blocks for enabling self-sufficient robot learning.
First, I will outline a family of methods that facilitate active global exploration. Specifically, they enable ultra data-efficient Bayesian optimization in reality by leveraging experience from simulation to shape the space of decisions. In robotics, these methods enable success with a budget of only 10-20 real robot trials for a range of tasks: bipedal and hexapod walking, task-oriented grasping, and nonprehensile manipulation.
Next, I will describe how to bring simulations closer to reality. This is especially important for scenarios with highly deformable objects, where simulation parameters influence the dynamics in unintuitive ways. The success here hinges on finding a good representation for the state of deformables. I will describe adaptive distribution embeddings that provide an effective way to incorporate noisy state observations into modern Bayesian tools for simulation parameter inference. This novel representation ensures success in estimating posterior distributions over simulation parameters, such as elasticity, friction, and scale, even for scenarios with highly deformable objects and using only a small set of real-world trajectories.
Lastly, I will share a vision of using distribution embeddings to make the space of stochastic policies in reinforcement learning suitable for global optimization. This research direction involves formalizing and learning novel distance metrics on this space and will support principled ways of seeking diverse behaviors. This can unlock truly autonomous learning, where learning agents have incentives to explore, build useful internal representations and discover a variety of effective ways of interacting with the world.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Rika is a postdoctoral scholar at Stanford University and a recipient of the NSF/CRA Computing Innovation Fellowship for research on active learning of transferable priors, kernels, and latent representations for robotics. Rika completed her Ph.D. work on data-efficient simulation-to-reality transfer at KTH. Earlier, she obtained a research Master's degree from the Robotics Institute at Carnegie Mellon University, where she developed Bayesian optimization methods for robotics and for personalized tutoring systems. Before that, Rika was a software engineer at Google, first in the Search Personalization group and then in the Character Recognition team (developing open-source OCR engine Tesseract).
Host: Jesse Thomason
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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PHD Thesis Defense - Dimitris Stripelis
Mon, Mar 20, 2023 @ 11:00 AM - 12:00 PM
Computer Science
University Calendar
PHD Thesis Defense - Dimitris Stripelis
Title:
Heterogeneous Federated Learning
Committee Members:
Jose-Luis Ambite (Chair), Cyrus Shahabi, Paul Thompson, Greg Ver Steeg
Abstract:
Data relevant to machine learning problems are distributed across multiple data silos that cannot share their data due to regulatory, competitiveness, or privacy reasons. Federated Learning has emerged as a standard computational paradigm for distributed training of machine learning and deep learning models across silos. However, the participating silos may have heterogeneous system capabilities and data specifications. In this thesis, we address the challenges in federated learning arising from both computational and semantic heterogeneities. We present federated training policies that accelerate the convergence of the federated model and lead to reduced communication, processing, and energy costs during model aggregation, training, and inference. We show the efficacy of these policies across a wide range of challenging federated environments with highly diverse data distributions in benchmark domains and in neuroimaging. We conclude by describing the federated data harmonization problem and presenting a comprehensive federated learning and integration system architecture that addresses the critical challenges of secure and private federated data harmonization, including schema mapping, data normalization, and data imputation.
Location: https://usc.zoom.us/j/93599773555?pwd=TmI3M1JvTkxEV05DSmQ3dzYyVElmQT09
Audiences: Everyone Is Invited
Contact: Asiroh Cham
Event Link: https://usc.zoom.us/j/93599773555?pwd=TmI3M1JvTkxEV05DSmQ3dzYyVElmQT09
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AME Seminar
Mon, Mar 20, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Weiyu Li, Stanford
Talk Title: Battery Avatar: First-Principles Modeling and Data Analytics
Abstract: Rechargeable lithium batteries are electrochemical devices that are widely used in portable electronics and electric-powered vehicles. A breakthrough in battery performance requires advancements in battery cell configurations at the microscale level. This, in turn, places a premium on the ability to accurately predict complex multiphase thermoelectrochemical phenomena, e.g., migration of ions interacting with composite porous materials that constitute a battery cell microstructure. Optimal design of porous cathodes requires efficient quantitative models of microscopic (pore-scale) electrochemical processes and their impact on battery performance. In this talk, I will discuss effective properties (electrical conductivity, ionic diffusivity, reaction parameters) of a composite electrode comprising the active material coated with a mixture of the binder and conductor (the carbon binder domain or CBD). When used to parameterize the industry-standard pseudo-twodimensional (P2D) models, they significantly improve the predictions of lithiation curves in the presence of CBD. On the lithium anode, dendritic growth is a leading cause of degradation and catastrophic failure of lithium-metal batteries. Deep understanding of this phenomenon would facilitate the design of strategies to reduce, or completely suppress, the instabilities characterizing electrodeposition on the lithium anode. This would improve the safety of lithium-metal batteries with liquid electrolyte and all-solid-state lithium batteries. I will present the results of our analysis, which indicate that the use of anisotropic electrolytes and buffer layers can suppress dendritic growth of lithium metal.
Biography: Weiyu Li has received her M.Sc. degree in Mechanical and Aerospace Engineering from Princeton University and is scheduled to obtain her PhD in Energy Science and Engineering from Stanford University in the Spring of 2023. Her research focuses on modeling and simulation of electrochemical transport in energy storage systems, aiming to provide mechanistic insights into the optimal design of porous electrodes, electrolyte, etc. Her other research interests include data assimilation and biomedical modeling. Weiyu Li is the recipient of the Siebel Scholars Award in Energy Science, class of 2023, and of the Princeton University Fellowship in Natural Sciences and Engineering.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: Olin Hall of Engineering (OHE) - 406
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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ECE-S Seminar - Dr Jiaqi Gu
Tue, Mar 21, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Jiaqi Gu, PhD Candidate | University of Texas at Austin
Talk Title: Light in Artificial Intelligence: Hardware/Software Co-Design for Photonic Machine Learning Computing
Abstract: The proliferation of big data and artificial intelligence (AI) has motivated the investigation of next- generation AI computing hardware to support massively parallel and energy-hungry machine learning (ML) workloads. Photonic computing, or computing using light, is a disruptive technology that can bring orders-of- magnitude performance and efficiency improvement to AI/ML with its ultra-fast speed, high parallelism, and low energy consumption. There has been growing interest in using nanophotonic processors for performing optical neural network (ONN) inference operations, which can make transformative impacts in future datacenters, automotive, smart sensing, and intelligent edge. However, the substantial potential in photonic computing also brings significant design challenges, which necessitates a cross-layer co-design stack where the circuit, architecture, and algorithm are designed and optimized in synergy.
In this talk, I will present my exploration to address the fundamental challenges faced by optical AI and to pioneer a hardware/software co-design methodology toward scalable, reliable, and adaptive photonic neural accelerator designs. First, I will delve into the critical area scalability issue of integrated photonic tensor units and present specialized photonic neural engine designs with domain-specific customization that significantly "compresses" the circuit footprint while realizing comparable inference accuracy. Next, I will present efficient on-chip training frameworks to show how to build a self-learnable photonic accelerator and overcome the robustness and adaptability bottlenecks by directly training the photonic circuits in situ. Then, I will introduce how to close the virtuous cycle between photonics and AI by applying AI/ML to photonic device simulation. In the end, I will conclude the talk with future research directions of emerging domain-specific photonic AI hardware with an intelligent end-to-end co-design & automation stack and deploying it to support real-world applications.
Biography: Jiaqi Gu is a final-year Ph.D. candidate in the Department of Electrical and Computer Engineering at The University of Texas at Austin, advised by Prof. David Z. Pan and co-advised by Prof. Ray T. Chen. Prior to UT Austin, he received his B.Eng. from Fudan University, Shanghai, China, in 2018. His research interests include emerging post-Moore hardware design for efficient computing, hardware/software co-design, photonic machine learning, and AI/ML algorithms.
He has received the Best Paper Award at the ACM/IEEE Asian and South Pacific Design Automation Conference (ASP-DAC) in 2020, the Best Paper Finalist at the ACM/IEEE Design Automation Conference (DAC) in 2020, the Best Poster Award at the NSF Workshop for Machine Learning Hardware Breakthroughs Towards Green AI and Ubiquitous On-Device Intelligence in 2020, the Best Paper Award at the IEEE Transaction on Computer-Aided Design of Integrated Circuits and Systems (TCAD) in 2021, the ACM Student Research Competition Grand Finals First Place in 2021, and Winner of the Robert S. Hilbert Memorial Optical Design Competition in 2022.
Host: Dr Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/j/99786583943?pwd=MnlmNGxQUUIwWXpWbk0wTUhrQWsxZz09More Information: ECE Seminar Announcement 03.21.2023 - Jiaqi Gu.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/99786583943?pwd=MnlmNGxQUUIwWXpWbk0wTUhrQWsxZz09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Yue Zhao (CMU) - Scalable and Automated Systems and Algorithms for Unsupervised ML
Tue, Mar 21, 2023 @ 11:00 AM - 12:00 PM
Computer Science
Conferences, Lectures, & Seminars
Speaker: Yue Zhao, Carnegie Mellon University
Talk Title: Scalable and Automated Systems and Algorithms for Unsupervised ML
Series: CS Colloquium
Abstract: Many real-world events do not have outcome labels. For example, the fraudulence of a transaction remains unknown until it is discovered. This is where unsupervised machine learning (ML) becomes crucial in real-world scenarios as it can make decisions based solely on observations. In this talk, I will address two key challenges in unsupervised ML: (i) developing scalable learning systems that can handle large amounts of data, and (ii) automating the selection of the best ML model. The first part of the talk will cover an ML system called TOD, which can "compile" a diverse group of ML algorithms for GPU acceleration. The second part will describe an automated algorithm called MetaOD, which can select top ML models for various applications without relying on labels or evaluations. Lastly, I will discuss my future plans, including the ML+X initiative, which aims to bring the advantages of ML systems and automation to other domains, and the creation of a fully automated ML pipeline that chooses hardware, systems, and models seamlessly.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Yue Zhao is a Ph.D. candidate at CMU, working with Prof. Leman Akoglu and Prof. Zhihao Jia. He focuses on creating scalable and automated ML systems and algorithms, and has published over 30 papers in top venues such as VLDB, MLSys, JMLR, and NeurIPS. His open-source systems (https://github.com/yzhao062) have been widely deployed in firms and industries such as Morgan Stanley and Tesla, and have received over 15,000 GitHub stars and 10 million downloads. Yue has received the CMU Presidential Fellowship and Norton Graduate Fellowship. More information about him can be found at https://www.andrew.cmu.edu/user/yuezhao2/.
Host: Robin Jia
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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DEN@Viterbi - Online Graduate Engineering Virtual Information Session
Tue, Mar 21, 2023 @ 12:00 PM - 01:00 PM
Distance Education Network, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a virtual information session via WebEx, providing an introduction to DEN@Viterbi, our top ranked online delivery system. Discover the 40+ graduate engineering and computer science programs available entirely online.
Attendees will have the opportunity to connect directly with USC Viterbi representatives during the session to discuss the admission process, program details and the benefits of online delivery.
Register Today!WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=efdd95dd866832ba5f12889f63f11b0b9
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
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Epstein Institute - ISE 651 Seminar
Tue, Mar 21, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Brian Denton, Professor and Dept. Chair, Dept. of Industrial & Operations Engineering, University of Michigan, Ann Arbor
Talk Title: Optimization in the Presence of Model Ambiguity in Markov Decision Processes
Host: Dr. Sze-chuan Suen
More Information: March 21, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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ECE-S Seminar - Dr Ivan de Oliveira Nunes
Wed, Mar 22, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Ivan de Oliveira Nunes, Assistant Professor | Rochester Institute of Technology (RIT)
Talk Title: Architectures for Verifiable Confidentiality, Integrity, and Availability in Resource-Constrained Embedded Devices
Abstract: Embedded devices are increasingly ubiquitous and their importance is hard to overestimate. While they often support safety-critical functions (e.g., in medical devices, industrial control systems, and sensor- alarm combinations), these devices are usually implemented under strict cost and energy budgets, using low-end microcontroller units (MCUs) that lack sophisticated security mechanisms. On the lower end of the scale, these devices are small, cheap, and specialized. They tend to host small CPUs, have very limited memory, and run simple software. Nonetheless, if such devices are left unprotected, consequences of forged sensor readings or ignored actuation commands can be catastrophic, particularly, in safety-critical settings. This prompts the following three questions: (1) how to trust data produced, or verify that commands were correctly performed, by a simple remote embedded device? (2) how to actively prevent malware that infects embedded devices from exfiltrating private sensor data? and (3) how to guarantee that safety-critical tasks are always performed in a timely manner, irrespective of malware infections?
Motivated by these questions, this talk will overview a set of architectures based on hardware/software (HW/SW) co-designs to provide provable guarantees about data confidentiality, software integrity, and availability in (potentially compromised) embedded devices. In particular, I will discuss three formally verified HW/SW co-designs, each realizing one of the aforementioned goals (namely APEX [SEC'20], GAROTA [SEC'22], and VERSA [S&P'22]) and how they have been securely implemented atop the popular TI MSP430 micro-controller at a relatively low-cost.
Biography: Ivan De Oliveira Nunes is an Assistant Professor of Computing Security at the Rochester Institute of Technology (RIT). Before RIT, he received his Ph.D. degree in 2021 from the University of California Irvine (UCI). Ivan also holds a Bachelor's degree in Computer Engineering from the Federal University of Espirito Santo (UFES), Brazil, and a Master's degree in Computer Science from the Federal University of Minas Gerais (UFMG), Brazil. In recent years, he has worked on several topics, including IoT Security, Hardware-assisted security, HW/SW Co-design, Network Security, and Applied Cryptography. His research interests span the fields of security and privacy, computing systems, computer networking, applied cryptography, and especially their intersection.
Host: Dr Bhaskar Krishnamachari, bkrishna@usc.edu
Webcast: https://usc.zoom.us/j/93387896454?pwd=MVdwL2NHS1hqSXFlaFhPaE91WHVGUT09More Information: ECE Seminar Announcement 03.23.23 - Ivan de Oliveira Nunes.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/93387896454?pwd=MVdwL2NHS1hqSXFlaFhPaE91WHVGUT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Lindsay Sanneman (MIT) - Transparent Value Alignment: Foundations for Human-Centered Explainable AI in Alignment
Wed, Mar 22, 2023 @ 11:00 AM - 12:00 PM
Computer Science
Conferences, Lectures, & Seminars
Speaker: Lindsay Sanneman , MIT
Talk Title: Transparent Value Alignment: Foundations for Human-Centered Explainable AI in Alignment
Series: CS Colloquium
Abstract: Alignment of robot objectives with those of humans can greatly enhance robots' ability to act flexibly to safely and reliably meet humans' goals across diverse contexts from space exploration to robotic manufacturing. However, it is often difficult or impossible for humans, both expert and non-expert, to enumerate their objectives comprehensively, accurately, and in forms that are readily usable for robot planning. Value alignment is an open challenge in artificial intelligence that aims to address this problem by enabling robots and autonomous agents to infer human goals and values through interaction. Providing humans with direct and explicit feedback about this value learning process through approaches for explainable AI (XAI) can enable humans to more efficiently and effectively teach robots about their goals. In this talk, I will introduce the Transparent Value Alignment (TVA) paradigm which captures this two-way communication and inference process and will discuss foundations for the design and evaluation of XAI within this paradigm. First, I will present a novel suite of metrics for assessing alignment which have been validated through human subject experiments by applying approaches from cognitive psychology. Next, I will discuss the Situation Awareness Framework for Explainable AI (SAFE-AI), a human factors-based framework for the design and evaluation of XAI across diverse contexts including alignment. Finally, I will propose design guidance for XAI within the TVA context which is grounded in results from a set of human studies comparing a broad range of explanation techniques across multiple domains. I will additionally highlight how this research relates to real-world robotic manufacturing and space exploration settings that I have studied. I will conclude the talk by discussing the future vision of this work.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Lindsay Sanneman is a final year PhD candidate in the Department of Aeronautics and Astronautics at MIT and a member of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on the development of models, metrics, and algorithms for explainable AI (XAI) and AI alignment in complex human-autonomy interaction settings. Since 2018, she has been a member of MIT's Work of the Future task force and has visited over 50 factories worldwide alongside an interdisciplinary team of social scientists and engineers in order to study the adoption of robotics in manufacturing. She is currently a Siegel Research Fellow and has presented her work in diverse venues including the Industry Studies Association and the UN Department of Economic and Social Affairs.
Host: Heather Culbertson
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Computer Science General Faculty Meeting
Wed, Mar 22, 2023 @ 12:00 PM - 02:00 PM
Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526- Hybrid
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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AME Seminar
Wed, Mar 22, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Renee Zhao, Stanford University
Talk Title: Multifunctional Origami Robots
Abstract: In this talk, I will introduce our recent work on origami mechanisms and actuation strategies for applications spanning from biomedical devices to foldable space structures. The first topic is magnetically actuated millimeter-scale origami medical robots for effective amphibious locomotion in severely confined spaces or aqueous environments. The origami robots are based on the Kresling origami, whose thin shell structure 1) provides an internal cavity for drug storage, 2) permits torsion-induced contraction as a crawling mechanism and a pumping mechanism for controllable liquid medicine dispensing, 3) serves as propellers that spin for propulsion to swim, 4) offers anisotropic stiffness to overcome the large resistance from the severely confined spaces in biomedical environments. For the second part of my talk, the concept of hexagonal ring origami folding mechanism will be introduced as a strategy for deployable/foldable structures for space applications. The hexagonal rings can tessellate 2D/3D surfaces and each ring can snap to its stable folded configuration with only 10.6% of the initial area. Through finite-element analysis and the rod model, snap-folding of the hexagonal ring with slight geometric modification and residual strain are studied for easy folding of the ring to facilitate the design and actuation of hexagonal ring origami assemblies for functional foldable structures with extreme packing ratio.
Biography: Renee Zhao is an Assistant Professor of Mechanical Engineering at Stanford University. Renee received her PhD degree in Solid Mechanics from Brown University in 2016. She spent two years as a postdoc associate at MIT working on modeling of soft composites. Before Renee joined Stanford, she was an Assistant Professor at The Ohio State University from 2018 to 2021. Her research concerns the development of stimuli-responsive soft composites and shape morning mechanisms for multifunctional robotic systems. Renee is a recipient of the NSF Career Award (2020), AFOSR YIP (2023), ASME Journal of Applied Mechanics award (2021), the 2022 ASME Pi Tau Sigma Gold Medal, and the 2022 ASME Henry Hess Early Career Publication Award.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: John Stauffer Science Lecture Hall (SLH) - 102
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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PhD Thesis Proposal - Gautam Salhotra
Thu, Mar 23, 2023 @ 09:00 AM - 11:00 AM
Computer Science
University Calendar
Title: Accelerating Robot Reinforcement Learning Using Demonstrations
Committee: Gaurav Sukhatme (Chair), SK Gupta, Laurent Itti, Stefanos Nikolaidis, Somil Bansal
Date: Thursday March 23, 9am PST
Abstract:
Reinforcement learning is a promising and, recently, popular tool to solve robotic tasks such as object manipulation and locomotion. However, it is also well known for being a very hard problem setting to explore in. In contrast, Learning from demonstrations (LfD) methods train agents to the desired solution using demonstrations from a teacher.
I will explore the role of LfD methods to guide the exploration of RL methods, with the aim of applying it to regular object manipulation tasks. I will talk about work that uses planners and trajectory optimizers to guide RL, and then discuss the role human experts can play in LfD for RL. Finally, I will talk about proposed projects that can extend the current work to get the benefits of demonstrations while avoiding the downsides of obtaining them.
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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ECE-S Seminar - Dr Stephen Xia
Thu, Mar 23, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Stephen Xia, Postdoctoral Scholar | University of California, Berkeley
Talk Title: Embedded Intelligence Towards Smarter, Healthier and Safer Environments
Abstract: We have seen remarkable growth in smart devices and artificial intelligence in all aspects of our lives. Despite the ever-growing amount of AI around us, our environments are still far from truly intelligent. At the touch of a button, we have access to powerful AI that can easily outperform any human in complex tasks, yet our environments still cannot alert us to dangerous approaching vehicles, nor help us find our lost child in a busy grocery store, something all of us do regularly and intuitively. In this talk, I will present two lines of work that bridge the gap between AI and truly intelligent environments.
First, I will introduce my work on embedded acoustic intelligence. I will start by presenting my work on embedding acoustic intelligence into wearables we commonly carry, such as headphones and helmets, to create safer cities. These low-cost and long-lasting wearables leverage novel architectures that utilize a combination of physics-based models and machine learning techniques to alert pedestrians and construction workers of dangers from oncoming vehicles, ultimately acting as a second pair of ears that create a sphere of safety around us. Next, I will discuss how we can take lessons learned from urban safety to realize a generalized selective audio filtering architecture that allows us to embed robust acoustic intelligence into a diverse set of real-time and resource-constrained applications and platforms. This architecture dynamically leverages the physics of audio and a wide range of data-driven machine learning models to allow engineers and developers to enhance and suppress custom sounds in their applications.
Second, I will present my work on creating more configurable, adaptive, and evolving environments, which are three critical characteristics we need to realize to create truly intelligent environments. I will first touch on several works that allow anyone, regardless of their technical background, to easily deploy and configure complex sensing solutions, such as camera networks for indoor occupant tracking, without needing any domain or expert knowledge. Second, I will introduce my work on adaptive smart home systems that jointly consider human preferences and available resources within the environment to improve home automation and greatly reduce the barrier of entry for smart home technologies. Finally, I will present several works where we realize new dormant sensing and compute capabilities in several platforms, such as drones, by only leveraging processes already present, thereby "evolving" new capabilities completely for free.
Biography: Stephen Xia is a Postdoctoral Scholar in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, advised by Dr. Prabal Dutta and Dr. Xiaofan (Fred) Jiang. Stephen received his Ph.D. in 2022 from Columbia University and his B.S. in 2016 from Rice University, all in Electrical Engineering. His research lies at the intersection between systems, embedded machine learning, and signal processing, spanning areas in mobile and embedded systems, Internet-of-Things, cyber-physical systems, artificial intelligence, and smart health. His work takes a joint physics-based and data-driven approach to realize truly intelligent and autonomous environments by embedding and dynamically utilizing compute, sensing, actuation, storage, and networking resources all around us. Stephen's research has been highlighted by many popular media outlets, including Mashable, Fast Company, and Gizmodo, and has received various distinctions, including Best Demo Awards at ACM SenSys 2021, ACM/IEEE IPSN 2020, ACM/IEEE IoTDI 2018, and the Best Presentation Award at IEEE VNC 2018.
Host: Dr Murali Annavaram, annavara@usc.edu
Webcast: https://usc.zoom.us/j/93387896454?pwd=MVdwL2NHS1hqSXFlaFhPaE91WHVGUT09More Information: ECE Seminar Announcement 03.23.2023 - Stephen Xia.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/93387896454?pwd=MVdwL2NHS1hqSXFlaFhPaE91WHVGUT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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NL Seminar-Designing and Evaluating Language Models for Human Interaction
Thu, Mar 23, 2023 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Mina Lee, Stanford University
Talk Title: Designing and Evaluating Language Models for Human Interaction
Series: NL Seminar
Abstract: REMINDER
Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.
Despite the recent advancements in language models LMs, most LMs are not optimized for, nor are they evaluated on, real-world usage with human interaction. In this talk, I will present my research on designing and evaluating LMs for human LM interaction. Concretely, I will first describe how we can support human editing needs by enabling any LM to perform text infilling at any position in a document i.e., fill in the blanks. I will then introduce CoAuthor, a platform for capturing human LM interaction in collaborative writing as rich, replayable, keystroke level interaction traces. With the platform, I demonstrate how collecting a large interaction dataset and analyzing the traces provide unique insights into LM capabilities regarding language, ideation, and collaboration. Lastly, I will propose a new framework, HALIE Human AI Language based Interaction Evaluation, that defines the components of interactive systems and evaluation metrics for human LM interaction beyond writing. I will conclude by discussing open challenges and future directions in this field.
Biography: Mina Lee is a final year Ph.D. candidate at Stanford University, advised by Professor Percy Liang. Her research goal is to design and evaluate language models to enhance our productivity and creativity and understand how these models change the way we write. She has built various writing assistants, including an autocomplete system, a contextual thesaurus system, and a creative story writing system, as well as evaluated language models based on their ability to interact with humans and augment human capabilities.
She was named one of MIT Technology Reviews Korean Innovators under 35 in 2022, and her work has been published in top tier venues in natural language processing e.g., ACL and NAACL, machine learning e.g., NeurIPS, and human computer interaction e.g., CHI. Her recent work on human AI collaborative writing received an Honorable Mention Award at CHI 2022 and was featured in various media outlets including The Economist.
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://usc.zoom.us/j/93116592657Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://usc.zoom.us/j/93116592657
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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Semiconductors & Microelectronics Technology Seminar - Heng Wang, Thursday, March 23 at 11am in EEB 132
Thu, Mar 23, 2023 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Heng Wang, Illinois Institute of Technology
Talk Title: The Thermoelectric Effect under Photon Excitation
Series: Semiconductors & Microelectronics Technology
Abstract: Thermoelectric phenomena allow energy conversion between heat and electricity, which can be used in energy harvesting, solid state refrigeration, and temperature regulation. The physical origin of these phenomena are well understood with semi-classic theories such as the Boltzmann transport theory. Carefully conducted experiments often reveal results as predicted by such theories. Nonetheless, carrier transport not only happens when the system is near thermal equilibrium, as for the case of thermoelectric phenomena, but also happens in excited systems with electrons far from thermal equilibrium. And this draws our interest over the past a few years. In this talk we will discuss the characteristic, the physical origin, and measurement strategies of the thermoelectric effect under photon excitation (which is one version of the photo-thermoelectric phenomena). We will discuss a few case studies, what can these results tell us about the materials, and potential applications. There are still much to understand with this effect and we hope this discussion could stimulate more interest and applications as well.
Biography: Heng Wang is an assistant professor at department of Mechanical, Materials and Aerospace Engineering, Illinois Institute of Technology. He received his B.S. in materials science and engineering from Tsinghua University, China, and his PhD in materials science from California Institute of Technology. Before joining IIT he worked as a postdoctoral researcher at the Molecular Foundry, Lawrence Berkeley National Lab. He has over ten years of research experience in thermoelectric materials, physics, and devices, with more than 13000 citations. His current research interests include high-performance thermoelectric materials, as well as device design, manufacturing, and new applications. In addition, he is particularly interested in the interplay of photoelectric and thermoelectric phenomena.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu, J. Ravichandran
More Information: HengWang_0323.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium: Benjamin Eysenbach (CMU) - Self-Supervised Reinforcement Learning
Thu, Mar 23, 2023 @ 11:00 AM - 12:00 PM
Computer Science
Conferences, Lectures, & Seminars
Speaker: Benjamin Eysenbach , CMU
Talk Title: Self-Supervised Reinforcement Learning
Series: CS Colloquium
Abstract: Reinforcement learning (RL) promises to harness the power of machine learning to solve sequential decision making problems, with the potential to enable applications ranging from robotics to chemistry. However, what makes the RL paradigm broadly applicable is also what makes it challenging: only limited feedback is provided for learning to select good actions. In this talk, I will discuss how we have made headway of this challenge by designing self-supervised RL methods, ones that can learn representations and skills for acting using unsupervised (reward-free) experience. These skill learning methods are practically-appealing and have since sparked a vibrant area of research. I will also share how we have answered some open theoretical questions in this area.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Benjamin Eysenbach is a final-year PhD student at Carnegie Mellon University. His research has developed machine learning algorithms for sequential decision making. His algorithms not only achieve a high degree of performance, but also carry theoretical guarantees, are typically simpler than prior methods, and draw connections between many areas of ML and CS. Ben is the recipient of the NSF and Hertz graduate fellowships. Prior to the PhD, he was a resident at Google Research and studied math as an undergraduate at MIT.
Host: Jyo Deshmukh
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Dr. Zhou Li (University of California Irvine) - Debugging the Fragmented DNS Infrastructure at Scale
Thu, Mar 23, 2023 @ 04:00 PM - 05:20 PM
Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Zhou Li, University of California Irvine
Talk Title: Debugging the Fragmented DNS Infrastructure at Scale
Series: CS Colloquium
Abstract: Domain Name System (DNS) is a fundamental infrastructure that supports almost all sorts of Internet activities. However, service failures and breach of DNS are not rare, and some even led to the shutdown of large data centers, though DNS was designed under the goals like resiliency from the very beginning. We argue that the root causes are that DNS infrastructure has become too fragmented and its protocols have become much more complex, so new research efforts are needed to harden the DNS infrastructure. In this talk, I'll describe our efforts in this direction. First, I'll talk about two new DNS attacks we identified under the settings of domain revocation and conditional resolution, and their implications. Second, I'll talk about how we measure the operational status of DNS-over-Encryption at a large scale. Finally, I'll conclude the talk with an outlook for DNS-related research.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Zhou Li is an Assistant Professor at UC Irvine, EECS department, leading the Data-driven Security and Privacy Lab. Before joining UC Irvine, he worked as Principal Research Scientist at RSA Labs from 2014 to 2018. His research interests include Domain Name System (DNS), Graph Security analytics, Privacy Enhancement Technologies and Side-channel analysis. He received the NSF CAREER award, Amazon Research Award, Microsoft Security AI award and IRTF Applied Networking Research Prize.
Host: Weihang Wang
Location: Seeley G. Mudd Building (SGM) - 124
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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ECE-EP seminar - David Burghoff, Friday, March 24th at 10am in EEB 132
Fri, Mar 24, 2023 @ 10:00 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: David Burghoff, Notre Dame
Talk Title: Broadband quantum and nonlinear photonics at long wavelengths
Series: ECE-EP Seminar
Abstract: While the longwave infrared and terahertz ranges have potential to revolutionize disease detection and environmental monitoring, there is currently a lack of compact broadband sources and integrated photonics platforms. I will discuss some of the work of my group that seeks to address this grand challenge. First, I will discuss our development of quantum cascade laser-based frequency combs, light sources that fill the gap between broadband incoherent sources and lasers. I will showcase how we created the first combs in the terahertz range and how our experimental investigations of these combs led to our discovery of a new fundamental comb state that manifests in any laser at any wavelength. Next, I will delve into our development of ultra-low-loss platforms for long wavelengths based on hybrid photonic integration, which allowed us to create optical resonators in the longwave infrared with quality factors two orders of magnitude better than the state-of-the-art. Finally, I will discuss our creation of ptychoscopy, a new sensing modality that allows for ultra-precise measurements of optical spectra. This measurement enables the measurement of remote signals with quantum-limited frequency resolution over the entire bandwidth of a comb, for the first time allowing incoherent spectra to be characterized with the precision techniques of combs.
Biography: David Burghoff is an Assistant Professor at Notre Dame, where his lab blends photonics with quantum devices to develop novel sensing and computing modalities. Prior to this, he was a postdoctoral fellow and research scientist at the Massachusetts Institute of Technology, where he led a team working in DARPA's SCOUT program. He also received his Ph.D. from MIT, where he won the J.A. Kong Award for MIT's Outstanding Electrical Engineering Thesis. He co-chaired the 2022 and 2020 International Quantum Cascade Laser School and Workshop, and he was one of only five faculty nationally named as a 2022 Moore Inventor's Fellow. His other awards include the ONR Young Investigator Program Award, the NSF CAREER Award, the AFOSR Young Investigator Program Award, and the Intelligence Community Postdoctoral Fellowship.
Host: ECE-Electrophysics
More Information: David Burghoff Seminar Announcement.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Photonics Seminar - Stefan Badescu, Friday, March 24th at 10:30am in EEB 248
Fri, Mar 24, 2023 @ 10:30 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Stefan Badescu, Sensors Directorate, AFRL
Talk Title: The role of gain-loss distribution in topological laser arrays
Series: Photonics Seminar Series
Abstract: Motivated by earlier demonstrations of III-V topological lasers, I will present insights from modeling of ring arrays with engineered distributions of gain and loss. In addition, I will discuss the influence of Corbino geometrical parameters on the bulk density of states and on the properties of topological states, including the interplay between disorder, quality factors, and gain contrast. In the second part I will present progress with fabrication of device structures as part of a collaboration between Air Force Research Laboratory and the Ohio State University.
Biography: Stefan C. Badescu received his PhD in theoretical condensed matter physics in 2002 from Brown University, with work in quantum diffusion and in computational material science. From 2002 he was a National Research Council fellow at Naval Research Laboratory, with work in quantum computing. From 2005 he was a research faculty with University of Maryland at College Park with work on spin qubits and on carbon materials. He joined the Air Force in 2011 with computational work on wide bandgap materials for electronics and on III-V semiconductors. More recently he led a Topological Photonics subproject on 'Topologically Enabled Devices'.
Host: Mercedeh Khajavikhan, Michelle Povinelli, Constantine Sideris; Hossein Hashemi; Wade Hsu; Mengjie Yu; Wei Wu; Tony Levi; Alan E. Willner; Andrea Martin Armani
More Information: Stefan Badescu Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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BME Seminar Speaker, Dr. Alexander Hoffmann
Fri, Mar 24, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Alexander Hoffmann, Professor of Microbiology and Immunology at UCLA
Talk Title: Systems biology, immune cell signaling
Host: BME Professor Stacey Finley - ZOOM link available on request
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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ECE-S Seminar Announcement: Dr. Christian Cuba Samaniego
Fri, Mar 24, 2023 @ 01:00 PM - 02:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Christian Cuba Samaniego, Research Fellow, Department of Immunology, Harvard Medical School
Talk Title: Adapting feedback control and pattern recognition paradigms for biotechnological applications
Abstract: Engineering synthetic genetic networks with desired behavior for robust adaptation or complex decision-making is challenging. Current approaches rely on different negative regulation techniques or logic-based operators, which suffer from suboptimal performance. To address this limitation, we introduce two design principles: (1) ultrasensitive input-output behavior and (2) tunable thresholds. Here, we engineer ultrasensitive-based networks to both achieve adaptive behavior through feedback control and build synthetic genetic programs for molecular pattern recognition by implementing neural computing networks in living cells.
Biography: Christian Cuba Samaniego received his BS degree in Mechatronic Engineering from "Universidad Nacional de Ingenieria" in Lima-Peru in 2009. He obtained his PhD in Mechanical Engineering from University of California Riverside in 2017 under the supervision of Prof. Elisa Franco. He joined the Biological Engineering Department at Massachusetts Institute of Technology as a postdoc under the supervision of Prof. Ron Weiss (2019), and Mechanical and Aerospace Engineering Department in the lab of Prof. Elisa Franco (2022). Currently, Christian is a research fellow in the Department of Immunology at Harvard Medical School in the lab of Prof. Ming-Ru Wu. His current research is at the interface of Control Theory, Systems and Synthetic Biology, and Machine Learning. I am specially interested in the design, analysis and applications of biomolecular feedback control systems and molecular neural networks for decision-making (molecular pattern recognition) in living cells.
Host: Dr. Urbashi Mitra (ubli@usc.edu)
Webcast: https://usc.zoom.us/j/93768871353?pwd=c0haOXhxREVBY05VbUs0cDh4YTMzdz09More Information: ECE Seminar Announcement-Cuba-Samaniego-032423.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/93768871353?pwd=c0haOXhxREVBY05VbUs0cDh4YTMzdz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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KIUEL X VGSA Karaoke Night
Fri, Mar 24, 2023 @ 06:30 PM - 08:00 PM
USC Viterbi School of Engineering
Student Activity
Join KIUEL and VGSA for a night of karaoke. There will be food and prizes
Location: Sign into EngageSC to View Location
Audiences:
Contact: Kamau Abercrombia
Event Link: https://engage.usc.edu/viterbi/rsvp?id=389072
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WIE Leadership Conference
Sat, Mar 25, 2023 @ 09:30 AM - 03:05 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
The WIE Leadership Conference theme this year is "Channeling Your Inner Champion". The conference is designed for you to discover ways to enhance your personal and professional growth. A diverse group of speakers from academia and industry will facilitate workshops, discussions, and presentations on various topics such as ENGR: Stomping Out Stereotypes, Creating an Elevator Pitch, and Leveraging Your Cultural Wealth. This is also an opportunity for you to further build your community of support.
*** If you have any questions please reach out to us at uscviterbiwie@gmail.com ***Location: Sign into EngageSC to View Location
Audiences: Everyone Is Invited
Contact: Thelma Federico Zaragoza
Event Link: https://engage.usc.edu/WIE/rsvp?id=388766
