SUNMONTUEWEDTHUFRISAT
Events for the 5th week of September
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ECE Seminar: Verifiable Control of Learning-Enabled Autonomous Systems
Tue, Sep 26, 2023 @ 12:00 PM - 01:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Lars Lindemann, Assistant Professor, USC Thomas Lord Department of Computer Science
Talk Title: Verifiable Control of Learning-Enabled Autonomous Systems
Abstract: Autonomous systems research shows great promise to enable many future technologies such as autonomous driving, intelligent transportation, and robotics. Accelerated by the computational advances in machine learning and AI, there has been tremendous success in the development of learning-enabled autonomous systems over the past years. At the same time, however, new fundamental questions arise regarding the safety and reliability of these increasingly complex systems that operate in dynamic and unknown environments. In this talk, I will provide new insights and discuss exciting opportunities to address these challenges.
In the first part of the talk, we focus on reasoning about uncertainty of learning-enabled components in an autonomy stack. Existing model-based techniques are usually too conservative or do not scale. I will instead advocate for conformal prediction as an accurate and computationally lightweight alternative. We will first use conformal prediction to design predictive runtime verification algorithms that quantify uncertainty of learning-enabled systems. These algorithms can effectively compute the probability of a task violation during the execution of the system. I will then show how to design probabilistically safe motion planning algorithms in dynamic environments using such uncertainty quantification. While existing data-driven approaches quantify uncertainty heuristically, we quantify uncertainty in a distribution-free manner. Using ideas from adaptive conformal prediction, we can even deal with distribution shifts, i.e., when test and training distributions are different. We illustrate the method on a self-driving car and a drone that avoids a flying frisbee. In the second part of the talk, I present an optimization framework to learn safe control laws from expert demonstrations. In most safety-critical systems, expert demonstrations in the form of system trajectories that showcase safe system behavior are readily available or can easily be collected. I will propose a constrained optimization problem with constraints on the expert demonstrations and the system model to learn control barrier functions for safe control. Formal guarantees are provided in terms of the density of the data and the smoothness of the system model. We then discuss how we can account for model uncertainty and hybrid system models, and how we can learn safe control laws from high-dimensional sensor data. Two case studies on a self-driving car and a bipedal robot illustrate the method.
Biography: Lars Lindemann is currently an Assistant Professor at the Department of Computer Science at the University of Southern California where he leads the Safe Autonomy and Intelligent Distributed Systems (SAIDS) lab. Prior to joining USC, he was a Postdoctoral Fellow in the Department of Electrical and Systems Engineering at the University of Pennsylvania from 2020 and 2022. He received the Ph.D. degree in Electrical Engineering from KTH Royal Institute of Technology in 2020. Prior to that, he received the M.Sc. degree in Systems, Control and Robotics from KTH in 2016 and two B.Sc. degrees in Electrical and Information Engineering and in Engineering Management from the Christian-Albrecht University of Kiel in 2014. His current research interests include systems and control theory, formal methods, and autonomous systems. Lars received the Outstanding Student Paper Award at the 58th IEEE Conference on Decision and Control and the Student Best Paper Award (as a co-author) at the 60th IEEE Conference on Decision and Control. He was a finalist for the Best Paper Award at the 2022 Conference on Hybrid Systems: Computation and Control and for the Best Student Paper Award at the 2018 American Control Conference.
Host: Dr. Rahul Jain, rahul.jain@usc.edu
Webcast: Webcast: https://usc.zoom.us/j/99747592573?pwd=YmNGYkJCK1V5SEQwcU1jVllwQVFwZz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: Webcast: https://usc.zoom.us/j/99747592573?pwd=YmNGYkJCK1V5SEQwcU1jVllwQVFwZz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Quantum Science & Technology Seminar - Alec Eickbusch, Thursday, 9/28 at 2pm in EEB 248
Thu, Sep 28, 2023 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Alec Eickbusch, Yale University
Talk Title: Advances in control and error correction of GKP codes in superconducting circuits
Series: Quantum Science & Technology Seminar Series
Abstract: The past four years have seen rapid experimental progress in realizing the quantum error correction code proposed in 2001 by Gottesman, Kitaev, and Preskill (GKP) in which logical states are encoded as oscillator grid states [1]. Recent milestones include code preparation and real-time error correction in trapped-ion motional modes and in superconducting cavities [2-6]. In this overview talk, I will review experiments from our lab at Yale that have led to these advances, focusing on the engineering of an experimental architecture for the code\'s realization in superconducting circuits [2]. I also will demonstrate how the same tools can be used for universal control of an oscillator with weak dispersive coupling to a qubit [5]. Finally, I will share our recent results on optimizing the error correction protocol using model-free reinforcement learning, leading to the demonstration of a fully error-corrected quantum memory with coherence beyond break even [6].
[1] Gottesman, Kitaev, Preskill, PRA 2001; [2] Campagne-Ibarcq, Eickbusch, Touzard et al. Nature 2020 ; [3] Flühmann et al. Nature 2019 ; [4] de Neeve et al. Nature Physics 2022; [5] Eickbusch et al. Nature Physics 2022; [6] Sivak et al. Nature 2023
Biography: Alec Eickbusch is a PhD candidate in applied physics at Yale University in the group of Michel Devoret. Alec did his undergraduate studies at the University of Texas at Austin where he earned degrees in physics and electrical engineering. At Yale, Alec\'s research has focused on bosonic quantum error correction in superconducting circuits and quantum control of high-quality oscillators. Alec is also a consultant for Nord Quantique, and he will join Google Quantum AI as a research scientist in Fall 2023.
Host: Quntao Zhang, Wade Hsu, Mengjie Yu, Jonathan Habif & Eli Levenson-Falk
Webcast: https://usc.zoom.us/j/92644326549Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/92644326549
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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ISSS - Changzhi Li, Friday, 9/29 at 2pm in EEB 248
Fri, Sep 29, 2023 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Changzhi Li, Texas Tech University
Talk Title: Portable Radar Systems at the Human-Microwave Frontier: Life Activity Sensing and Human Tracking
Series: Integrated Systems
Abstract: By sensing various life activities with microwave signals, portable radar with state-of-the-art front-end and measurement algorithms has great potential to improve healthcare, security, and human-machine interface. This presentation will first provide an overview on the state-of-the-art smart radar sensors powered by advanced digital/RF beamforming, multiple-input and multiple-output (MIMO), inverse synthetic-aperture radar (ISAR) technique, and deep learning. A few examples based on interferometry, Doppler, frequency-shift keying (FSK), and frequency-modulated continuous-wave (FMCW) modes at 5.8 GHz, 24 GHz, and 120 GHz will be discussed. In addition, the use of nonlinear technologies will be reported, with a focus on in-band third-order intermodulation measurement for enhanced target identification and parameter extraction. Case studies at this exciting human-microwave frontier will be given on physiological signal sensing, non-contact human-computer interface, driving behavior recognition, human tracking, and anomaly detection.
As smart radar sensors enter the healthcare, automotive, and smart living sectors of daily life, measures to enhance its security against malicious attacks are of paramount importance. This part of the talk will discuss possible ways of malicious attacks to radar sensors. Then technologies that mitigate potential spoofing attacks will be unveiled to make smart radar sensors more secure and trustworthy. Finally, this talk will conclude with future industrial and academic R&D outlooks for microwave short-range life activities sensing.
Biography: Changzhi Li received a Ph.D. degree in Electrical Engineering from the University of Florida, Gainesville, FL in 2009. He is a Professor at Texas Tech University. His research interest is microwave/millimeter-wave sensing for healthcare, security, and human-machine interface.
Dr. Li is an MTT-S Distinguished Microwave Lecturer. He was a recipient of the IEEE MTT-S Outstanding Young Engineer Award, the IEEE Sensors Council Early Career Technical Achievement Award, the ASEE Frederick Emmons Terman Award, the IEEE-HKN Outstanding Young Professional Award, and the U.S. National Science Foundation (NSF) Faculty CAREER Award. He is an Associate Editor of the IEEE JOURNAL OF ELECTROMAGNETICS, RF AND MICROWAVES IN MEDICINE AND BIOLOGY. He is the General Co-chair of the 2023 IEEE Radio & Wireless Week (RWW). He served as the chair of the MTT-S Technical Committee \"Biological Effect and Medical Applications of RF and Microwave\" from 2018 to 2019, the TPC Chair of the 2022 IEEE RWW, a TPC Co-Chair of the IEEE MTT-S International Microwave Biomedical Conference (IMBioC) from 2018 to 2019, and the IEEE Wireless and Microwave Technology Conference from 2012 to 2013. He is a Fellow of the National Academy of Inventors.
Host: MHI - ISSS, Hashemi, Chen and Sideris
Webcast: Zoom Meeting ID: 919 9842 7261, Passcode: 520437More Information: Abstract and Bio_C_Li.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: Zoom Meeting ID: 919 9842 7261, Passcode: 520437
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
