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Prof. Edwin K. P. Chong (IEEE Fellow, AAAS Fellow)
Colorado State University, USA
Biography: Edwin K. P. Chong received the B.E. degree with First Class Honors from the University of Adelaide, South Australia, in 1987 and the M.A. and Ph.D. degrees in 1989 and 1991, respectively, both from Princeton University, where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991. Since August 2001, he has been a Professor of Electrical and Computer Engineering and Professor of Mathematics at Colorado State University. He coauthored the best-selling book, An Introduction to Optimization (4th Edition, Wiley-Interscience, 2013). He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He was a co-recipient of the 2004 Best Paper Award for a paper in the journal Computer Networks. In 2010, he received the IEEE Control Systems Society Distinguished Member Award.
Prof. Chong is a Fellow of IEEE and of AAAS. He was the founding chairman of the IEEE Control Systems Society Technical Committee on Discrete Event Systems and was an IEEE Control Systems Society Distinguished Lecturer. He was an inaugural Senior Editor of the IEEE Transactions on Automatic Control. He was the General Chair for the 2011 Joint 50th IEEE Conference on Decision and Control and European Control Conference. He served as President of the IEEE Control Systems Society in 2017.
Speech Title: Numerically Stable Wiener Filters
Abstract: Many tasks in robotics and control involve Wiener filtering (including Kalman filtering), founded on linear minimum mean square error (LMMSE) estimation. Computing the Wiener filter output is often ill-conditioned, i.e., numerically unstable. This suggests that unconstrained minimization of the mean square error is an inadequate approach to filter design. To address this, we first develop a unifying framework for studying constrained LMMSE estimation problems. Using this framework, we explore an important structural property of constrained LMMSE filters involving a certain prefilter. Optimality is invariant under invertible linear transformations of the prefilter. This parameterizes all optimal filters by equivalence classes of prefilters. We then clarify that merely constraining the rank of the filter does not suitably address the problem of ill-conditioning. Instead, we adopt a constraint that explicitly requires solutions to be well-conditioned in a certain specific sense. We introduce two well-conditioned filters and show that they converge to the unconstrained LMMSE filter as their truncation-power loss goes to zero, at the same rate as the low-rank Wiener filter. We also show extensions to the case of weighted trace and determinant of the error covariance as objective functions. Finally, we show quantitative results with historical VIX data to demonstrate that our well-conditioned filters have stable performance while the standard LMMSE filter deteriorates with increasing condition number.
Prof. Zhong-Ping Jiang (IEEE Fellow, IFAC Fellow, CAA Fellow)
New York University, USA
Biography: Zhong-Ping JIANG received the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from the Ecole des Mines de Paris (now, called ParisTech-Mines), France, in 1993, under the direction of Prof. Laurent Praly.
Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His main research interests include stability theory, robust/adaptive/distributed nonlinear control, robust adaptive dynamic programming, reinforcement learning and their applications to information, mechanical and biological systems. In these fields, he has written six books and is author/co-author of over 500 peer-reviewed journal and conference papers.
Prof. Jiang is a recipient of the prestigious Queen Elizabeth II Fellowship Award from the Australian Research Council, CAREER Award from the U.S. National Science Foundation, JSPS Invitation Fellowship from the Japan Society for the Promotion of Science, Distinguished Overseas Chinese Scholar Award from the NSF of China, and several best paper awards. He has served as Deputy Editor-in-Chief, Senior Editor and Associate Editor for numerous journals. Prof. Jiang is a Fellow of the IEEE, a Fellow of the IFAC, a Fellow of the CAA and is among the Clarivate Analytics Highly Cited Researchers. In 2021, he is elected as a foreign member of the Academia Europaea.
Speech Title: Learning-Based Control with Application to Autonomous Driving
Abstract: This talk presents a new design paradigm, called “learning-based control”, that is fundamentally different from traditional model-based control and model-free machine learning. Learning-based control is aimed at learning real-time optimal controllers directly from input-output data, for stability and robustness of dynamical systems in uncertain environments. Novel tools and methods for data-driven control are proposed as an entanglement of techniques from reinforcement learning and control theory. This talk reviews our prior work in learning-based control and presents our recent development of learning-based control algorithms for connected and autonomous vehicles in mixed traffic environments.
Prof. Maurizio Porfiri (IEEE Fellow, ASME Fellow)
New York University Tandon School of Engineering, USA
Biography: Maurizio Porfiri is an Institute Professor at New York University Tandon School of Engineering, with appointments in the Center for Urban Science and Progress and the Departments of Mechanical and Aerospace Engineering, Biomedical Engineering, and Civil and Urban Engineering. He received M.Sc. and Ph.D. degrees in Engineering Mechanics from Virginia Tech; a “Laurea” in Electrical Engineering and a Ph.D. in Theoretical and Applied Mechanics from Sapienza University of Rome and the University of Toulon. He is engaged in conducting and supervising research on complex systems, with applications from mechanics to behavior, public health, and robotics.
Speech Title: When zebrafish met engineering
Abstract: Zebrafish are gaining momentum as the third millennium laboratory species for the investigation of several functional and dysfunctional biological processes in humans, including the fundamental mechanisms modulating emotional patterns, learning processes, and individual and social response to alcohol and drugs of abuse. Dynamical systems and robotics offer a powerful range of theoretical and experimental approaches that can advance our understanding of this animal model. In this talk, we report recent advances on: (i) the design of biomimetic robotic fish to elicit highly-controllable and customizable stimuli for laboratory experiments on zebrafish behavior; (ii) the formulation of a new data-driven modeling framework to study zebrafish behavior within unprecedented “in silico” experiments that can help reduce the number of animals in preclinical studies; and (iii) the integration of information-theoretic tools to unravel leader-follower interactions in groups of zebrafish and measure fear response to predators. The presentation is intended to expose neuroscientists to a toolbox of methodological innovations that can enhance their experiments, while offering engineers an overview of fundamental mathematical and technological advancements that can find applications beyond the study of zebrafish.
Prof. Wenqiang Zhang
Fudan University, China
Biography: Dr. Wenqiang Zhang, Professor with the School of Computer Science, and as a deputy dean at the School of AI & Robotics, Fudan University, is also the Vice Director of Shanghai Key Laboratory of Intelligent Information. He engaged in the research work of robotics, AI, and Intelligent Equipment, etc. He has published more than 150 papers and applied for more than 50 invention patents. He has undertaken more than 40 research projects from National Key R&D Program of China , National Natural Science Foundation of China (NSFC), Ministry of Education，Science and Technology Commission of Shanghai Municipality (STCSM). He has created the first autonomous mental development robot-Fudan-I, and successfully developed robots of Fuwa, Ato, Haibao, etc.
Speech Title: Knowledge guided Artificial Intelligence and Application
Abstract: In recent years, artificial intelligence technology, represented by deep learning, has achieved many amazing results in video / image analysis, semantic understanding and so on, but it is not satisfactory in autonomous learning. Inspired by the biological learning mechanism, this report discusses the learning theory and method for audio-visual information fusion in view of the difficulties and challenges faced by small learning samples and difficult skill transfer. Combined with the research work of the research group, some thoughts on key technology and industrial innovation are given.
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