speakers

Roy Smith is a Professor in the Automatic Control Laboratory at the Swiss Federal Institute of Technology (ETH, Zurich) in Switzerland. From 1990 to 2010 he was on the faculty of the Electrical Engineering Dept. at the University of California, Santa Barbara. He received his undergraduate education at Canterbury University in New Zealand (1980) and a Ph.D. from the California Institute of Technology (1990). His research interests include: the identification and control of uncertain systems, and distributed estimation, communication and control systems. His application experience includes: process control, automotive engines, flexible space structures, aeromanoeuvring Mars entry vehicles, formation flying of spacecraft, magnetically levitated bearings, high energy accelerator control, thermoacoustic engines, airborne wind energy, and energy control for buildings. He has been a long time consultant to the NASA Jet Propulsion Laboratory on guidance, navigation and control aspects of interplanetary and deep space science spacecraft. He is a Fellow of the IEEE & IFAC, an Associate Fellow of the AIAA, and a member of SIAM and NZAC.

Mohammad Khosravi received a B.Sc. in electrical engineering and a B.Sc. in mathematical sciences from Sharif University of Technology, Tehran, Iran, in 2011. He obtained a postgraduate diploma in mathematics from ICTP, Trieste, Italy, in 2012. He was a research assistant in the mathematical biology group at Institute for Research in Fundamental Sciences, Tehran, Iran, in 2012-2014. He received his M.A.Sc. degree in electrical and computer engineering from Concordia University, Montreal, Canada, in 2016. Currently, he is a Ph.D. candidate at Automatic Control Laboratory, ETH Zurich. He has won several awards, including the gold medal of the National Mathematics Olympiad, the Outstanding Student Paper Award in CDC 2020, and the Outstanding Reviewer Award for IEEE Journal of Control Systems Letters. His research interests are on data-driven and learning-based methods in modeling, control & optimization, and their applications in buildings, energy, and industrial systems.

Dynamical systems are omnipresent in various fields of science and different modern technologies. Being in the era of data, one may pose the question of how to identify or learn a dynamical system using collected data. In many situations, in addition to data, we may have some side information about the dynamics to be incorporated in the identification problem in a correct and tractable manner. The origin of this side information can be the physics, the nature of the system or it may come from observed behaviours in data or past experiments. We present a method for the case where the side information is knowledge about the region of attraction (ROA) of an equilibrium point, i.e., a subset of the ROA containing the equilibrium point is known. The identification method is based on an optimisation problem minimising the fitting error while guaranteeing the incorporation of the knowledge about the ROA. The side information is integrated into the identification problem by considering an unknown Lyapunov function verifying the stability property. The resulting problem is a non-convex infinite-dimensional optimisation with an infinite number of constraints. To obtain a tractable formulation, a suitably designed finite subset of the constraints is considered. The subsequent problem admits a solution in form of a linear combination of the feature maps of the kernel and their derivatives. This linear parametric form allows us to formulate an equivalent finite-dimensional optimisation problem with a quadratic cost function subject to linear and bilinear constraints. We demonstrate the method by several examples and verify the advantage of incorporation of side information in this identification problem.

Dionysios Kalogerias is an assistant professor of Electrical Engineering at Yale University. He received the PhD degree in Electrical and Computer Engineering (ECE) from Rutgers, The State University of New Jersey (2017), the MSc degree in Signal Processing and Communications from the University of Patras, Greece (2012), and the Meng degree in Computer Engineering and Informatics, also from the University of Patras, Greece (2010). Before joining Yale, Dionysios was an assistant professor with the Department of ECE, Michigan State University (2020 – 2021). Prior to that, he held postdoctoral appointments with the Department of Electrical and Systems Engineering, University of Pennsylvania (2019 – 2020), and the Department of Operations Research and Financial Engineering, Princeton University (2017 – 2019). Dionysios’ research is in the areas of machine learning, reinforcement learning, optimization, signal processing, sequential decision making, and risk, and their applications in autonomous networked systems, wireless communications, security and privacy, and system trustworthiness. He has received several awards for his work, including the Best Paper Award at ICASSP 2020, and the Best Paper of the Special Sessions Award at ICASSP 2016.

Risk-awareness is becoming an increasingly important issue in modern statistical learning theory and practice, especially due to the need to meet strict reliability requirements in high-stakes, critical applications. Examples appear naturally in many areas, such as energy, finance, robotics, radar/lidar, networking and communications, autonomy, safety, and the Internet-of-Things. In such settings, risk-aware learning formulations are particularly appealing, since they can explicitly balance the performance of optimal predictors between average-case and “difficult” to learn, infrequent, or worst-case examples, inducing a form of statistical robustness in the learning outcome. In particular, among all measures of risk, Conditional Value-at-Risk (CV@R) is by far one of the most popular (if not the most), and has been recently considered as a performance criterion in supervised statistical learning, as it is also related to desirable operational features, such as safety, fairness, distributional robustness, and prediction error stability. However, due to its variational definition, CV@R is commonly believed to result in difficult optimization problems, even for smooth and strongly convex loss functions. In this talk, we present new results disproving this statement, establishing noisy (i.e., fixed accuracy) linear convergence of stochastic gradient descent for sequential CV@R learning, for a large class of not necessarily strongly-convex (or even convex) loss functions satisfying a set-restricted Polyak-\L ojasiewicz inequality. This class contains all smooth and strongly convex losses as special cases, confirming that classical problems, such as linear least squares regression, can be solved efficiently under the CV@R criterion, just as their risk-neutral versions. We then illustrate our results numerically on indicative risk-aware learning tasks, also verifying their validity in practice.

Dorsa Sadigh is an assistant professor in Computer Science and Electrical Engineering at Stanford University. Her research interests lie in the intersection of robotics, learning, and control theory. Specifically, she is interested in developing algorithms for safe and adaptive human-robot interaction. Dorsa has received her doctoral degree in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley in 2017, and has received her bachelor’s degree in EECS from UC Berkeley in 2012. She is awarded the NSF CAREER award, the AFOSR Young Investigator award, the IEEE TCCPS early career award, the Google Faculty Award, and the Amazon Faculty Research Award.

Erdem Bıyık is a fifth-year Ph.D. candidate in the Electrical Engineering department at Stanford. He has received his B.Sc. degree from Bilkent University, Turkey, in 2017; and M.Sc. degree from Stanford University in 2019. He is interested in human-robot interaction and multi-agent systems. Specifically, he works on developing learning algorithms for robots that actively query the humans and on training the robot policies that influence the humans or other robots to get more cooperative, which in turn improves the payoff for all agents.

In this talk, we will discuss a formalism for human-robot interaction built upon ideas from representation learning. Specifically, We will first discuss the notion of latent strategies — low dimensional representations sufficient for capturing non-stationary interactions. We will then talk about the challenges of learning such representations when interacting with humans, and how we can develop data-efficient techniques that enable actively learning computational models of human behavior from demonstrations, preferences, or physical corrections. Finally, we will introduce an intuitive control paradigm that enables seamless collaboration based on learned representations, and further discuss how that can be used for further influencing humans.