SUNMONTUEWEDTHUFRISAT
Events for February 16, 2022
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ECE Seminar - Scalable and Trustworthy Learning for Distributed Intelligence
Wed, Feb 16, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Tianyi Chen, Assistant Professor, Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute
Talk Title: Scalable and Trustworthy Learning for Distributed Intelligence
Abstract: The past decade has witnessed the revival of artiï¬cial intelligence (AI) and machine learning (ML) in almost every branch of science and technology. The "fuel" to AI and ML is supplied by the surge of data and computing power. Today, data and computing power are distributed among wireless devices and companies that we term data owners. Due to the pressing need for data in AI/ML tasks and the increasing concerns on data privacy, a sizeable number of AI/ML tasks will be executed across networked data owners with the vision of distributed intelligence.
In this talk, I will use federated learning (FL) as an example of distributed intelligence. I will highlight its key challenges when it interacts with wireless networks such as efficiency, privacy, security, and robustness. I will focus on two aspects of scalable and trustworthy FL - efficiency and privacy. From a unified view of information correlation among iterative FL updates, I will elaborate i) how we can leverage such correlation to improve the efficiency of FL; and ii) how such correlation may be leveraged by a malicious third party to risk data privacy. Our methods are simple to implement, and they come with rigorous performance guarantees. I will conclude this talk by highlighting a few directions that I will pursue towards distributed intelligence beyond FL.
Biography: Tianyi Chen is an Assistant Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), where he is jointly supported by the RPI - IBM Artificial Intelligence Research Partnership. Dr. Chen received his B. Eng. degree in Electrical Engineering from Fudan University in 2014, and the Ph.D. degree in Electrical and Computer Engineering from the University of Minnesota in 2019. During 2017-2018, he has been a visiting scholar at Harvard University, University of California, Los Angeles, and University of Illinois Urbana-Champaign. Dr. Chen's research focuses on theoretical and algorithmic foundations of optimization, machine learning, and statistical signal processing, with applications in networked computing systems such as wireless and IoT systems.
Dr. Chen is the inaugural recipient of IEEE Signal Processing Society Best PhD Dissertation Award in 2020 and a recipient of NSF CAREER Award in 2021. He is also the co-author of several best paper awards such as the Best Student Paper Award at the NeurIPS Federated Learning Workshop in 2020 and at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in 2021.
Host: Dr. Salman Avestimehr, avestime@usc.edu
Webcast: https://usc.zoom.us/j/98177654001?pwd=R1h6bUJZUXcxZENZYWtVYmorRVNFQT09WebCast Link: https://usc.zoom.us/j/98177654001?pwd=R1h6bUJZUXcxZENZYWtVYmorRVNFQT09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Feb 16, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Changliu Liu, Robotics Institute, Carnegie Mellon University
Talk Title: Safe Control and Learning for Effective Human-Robot Collaboration
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: In this talk, I will discuss our recent work on safe control and learning for effective human-robot collaboration. I will first introduce safe control methods using energy-function-based methods, then discuss how to combine them with learning controllers where an explicit analytical dynamic model of the system is usually not available (especially in human-robot systems). These safe control methods will enable safe reinforcement learning with zero training time violation. Then I will discuss about methods to robustly learn models to predict human behaviors. The key challenge we need to address is the distribution shift between the offline collected human behavioral data and the online measured human behaviors. To mitigate the distribution shift, we introduce two methods: online model adaptation, and offline verification-guided data augmentation. These methods have been applied to facilitate human-robot collaboration in industrial assembly tasks. I will conclude the talk with future visions on how to effectively deploy human-robot systems on factory floors.
Biography: Dr. Changliu Liu is an assistant professor in the Robotics Institute, School of Computer Science, Carnegie Mellon University (CMU), where she leads the Intelligent Control Lab. Prior to joining CMU, Dr. Liu was a postdoc at Stanford Intelligent Systems Laboratory. She received her Ph.D. from University of California at Berkeley and her bachelor's degrees in engineering and economics from Tsinghua University. Her research interests lie in the design and verification of intelligent systems with applications to manufacturing and transportation. She published the book "Designing robot behavior in human-robot interactions" with CRC Press in 2019. She received many best paper awards, Rising Star in EECS, NSF Career Award, Amazon Research Award, and Ford URP Award.
Host: Pierluigi Nuzzo and Somil Bansal
Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxwLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw
Audiences: Everyone Is Invited
Contact: Talyia White
