Plenary Speakers

Domitilla del Vecchio (MIT)
Emilio Frazzoli (ETH/nuTonomy)
Fredrik Gustafsson (Linkoeping)
Maurice Heemels (Eindhoven)
Mihailo Jovanovic (USC)
Naomi Leonard (Princeton)
Ben Recht (Berkeley)
Sri Sarma (Hopkins)
Alireza Tahbaz-Salehi (Northwestern)
Dawn Tilbury (Michigan/NSF)

Talks

Engineering Biological Networks: A Control Theoretic Framework

Biological networks control every aspect of life and the possibility to engineer them de-novo has opened the path to a number of exciting applications, from revolutionary drugs and clean energy, to programmed bugs that track and kill cancer cells. A promising approach to engineer a complex system is to compose simpler modules together, under the assumption that these modules will keep their input/output behavior unchanged upon insertion into a larger system. Indeed, this is a pervasive approach used today to engineer biological circuits in the field of synthetic biology. However, when engineered biological modules are composed together, their input/output salient properties often change in rather astonishing ways. This is typically handled by re-designing every component of a system once a new component is added, leading to a daunting, combinatorial, optimization problem. This is a current bottleneck to the ability of engineering biological networks that behave as predicted and are sufficiently sophisticated to tackle real-life problems. In this talk, I will give an overview of a number of causes of modularity failing in engineered biological networks, focusing on problems of loading and resource sharing, the latter being a current major issue in the field. I will introduce a control theoretic framework, grounded on the concept of retroactivity, that has equipped us with both a system-level way to model lack of modularity and control theoretic solutions to restore it. I will conclude the talk by describing how concepts from controls and networks may be adapted to address the new physical constraints that biology imposes.

Domitilla Del Vecchio received the Ph. D. degree in Control and Dynamical Systems from the California Institute of Technology, Pasadena, and the Laurea degree in Electrical Engineering (Automation) from the University of Rome at Tor Vergata in 2005 and 1999, respectively. From 2006 to 2010, she was an Assistant Professor in the Department of Electrical Engineering and Computer Science and in the Center for Computational Medicine and Bioinformatics at the University of Michigan, Ann Arbor. In 2010, she joined Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where she is currently Professor and member of the Synthetic Biology Center. She is a recipient of the 2016 Bose Research Award (MIT), the Donald P. Eckman Award from the American Automatic Control Council (2010), the NSF Career Award (2007), the American Control Conference Best Student Paper Award (2004), and the Bank of Italy Fellowship (2000).

Autonomy and Decision Making in Ride Hailing Networks: Progress and Open Questions

In recent years, many of the most urgent questions related to autonomous vehicles were purely technological and vehicle centered: how safe are these vehicles, how comfortable are they, what scenarios can they handle? As public trials worldwide are becoming more common, capabilities and limitations are becoming clearer. On the other hand, much less systematic research is available on the operation and impact of large-scale fleets of autonomous vehicles offering mobility-on-demand services. What are the key parameters and constraints on operation? What will be the impact on congestion, parking, mass transit? How many seats should the robotic taxis have? Answering these questions is an exercise that requires not only technical know-how but also the consideration of societal and economic aspects. While much is still in the dark, research of most recent years has answered some of the open questions. This talk will provide insights into these answers of what we already know about autonomous ride-hailing networks. Moreover, it will also highlight some of the aspects which until today remain unanswered.

Emilio Frazzoli is a professor of Dynamic Systems and Control at ETH Zurich, and Chief Scientist of Aptiv's Autonomous Mobility group. His main research interest are in robotics, autonomous systems, and intelligent mobility. In acknowledgement of his seminal work in these fields, he has received several awards, including the the 2015 IEEE George S. Axelby Award and the 2017 IEEE Kiyo Tomiyasu Award, and has been named an IEEE Fellow in 2019. He has published more than 200 papers in the fields of robotics, autonomous vehicles, and drones. A former full professor at MIT, he directed the research group that first demonstrated an autonomous mobility ("robotaxi") service to the public, and performed the first analysis of the social and economic impact of such a service, based on real transportation data. In 2013 he founded nuTonomy with Karl Iagnemma, and served as its Chief Technology Officer until its acquisition by Aptiv in 2017.

Distributed fusion with communication constraints

Fusion in this context refers to combining estimates in local nodes to a global estimate. From a statistical point of view, the first (mean vector) and second (covariance matrix) order moments can easily be combined if the estimates are independent and the full covariance matrix can be communicated. However, if either of this is not true, approximate safe fusion strategies must be applied. Safe fusion should yield a conservative covariance matrix which must not be smaller than the true one. We survey some known strategies in literature, and present some new ones for the case when only the diagonal of the covariance matrix is communicated from the local nodes. We describe a real world scenario where thousands of taxi cars compute local estimates of the local road condition, which is combined to a global road condition map.

Fredrik Gustafsson is professor in Sensor Informatics at Department of Electrical Engineering, Linkoping University, since 2005. He received the M.Sc. degree in electrical engineering 1988 and the Ph.D. degree in Automatic Control, 1992, both from Linkoping University. He is the author of five books, more than 200 conference papers, 100 journal papers and some 30 patents. His current h-index is 58 (Google Scholar). He has supervised 25 PhD and more than 200 master theses. He was an associate editor for IEEE Transactions of Signal Processing 2000-2006, IEEE Transactions on Aerospace and Electronic Systems 2010-2012, and EURASIP Journal on Applied Signal Processing 2007-2012. He was awarded the Arnberg prize by the Royal Swedish Academy of Science (KVA) 2004, elected member of the Royal Academy of Engineering Sciences (IVA) 2007, and elevated to IEEE Fellow 2011. In 2014, he was awarded a Distinguished Professor grant from the Swedish Research Council. He was an adjunct entrepreneurial professor at Twente University 2012-2013.
He was awarded the Harry Rowe Mimno Award 2011 for the tutorial "Particle Filter Theory and Practice with Positioning Applications", which was published in the AESS Magazine in July 2010. This paper was ranked #4 out of 13077 articles published in the category IEEE Engineering Aerospace between 2010 and 2014 based on Web of Science citation data. He was a co-author of "Smoothed state estimates under abrupt changes using sum-of-norms regularization" that received the Automatica paper prize in 2014. He is a co-founder of the companies NIRA Dynamics (automotive safety, including tire pressure monitoring systems found in about 50 million cars today), Softube (plug-ins used in tens of thousands of music studios), and Senion (indoor navigation for smartphones deployed in more than 30 countries in all six continents).

Event-triggered communication and control in networked systems

Computer and communication technologies are developing at tremendous speeds leading to an increasingly networked and wireless world. These technologies bring new opportunities for the control of (large-scale) interconnected systems, but also raise new challenging questions for the resulting networked control systems in which more and more data is exchanged. One important question is how to determine the time instances at which data is exchanged between (sub)systems, agents, sensors, controllers and/or actuators such that desirable closed-loop stability and performance properties are guaranteed, while keeping the number of transmissions small. To efficiently use the available communication resources, we move away from the classical time-triggered control and communication schemes in which transmissions take place periodically in time. Instead, we propose to use event-triggered schemes as alternative and (more) resource-aware paradigms, as it seems natural to trigger control and communication actions by well-designed events involving the system's state, output or any other locally available information. We will discuss the main ideas, recent advances, and open questions for these event-triggered control and communication schemes in the context of distributed and multi-agent systems. The focus will be on periodic event-triggered control (PETC) schemes that unite the benefits of time-triggered control regarding ease of digital implementation on the one hand and event-triggered control to reduce communication on the other hand. The theory will be complemented by experiments in the area of cooperative driving (platooning) and robotics exploiting wireless communication.

Maurice Heemels received the M.Sc. degree in mathematics and the Ph.D. degree in control theory (both summa cum laude) from the Eindhoven University of Technology (TU/e), the Netherlands, in 1995 and 1999, respectively. From 2000 to 2004, he was with the Electrical Engineering Department, TU/e and from 2004 to 2006 with the Embedded Systems Institute (ESI). Since 2006, he has been with the Department of Mechanical Engineering, TU/e, where he is currently a Full Professor. He held visiting professor positions at the Swiss Federal Institute of Technology (ETH), Switzerland (2001) and at the University of California at Santa Barbara (2008). In 2004, he worked also at the company Oce, the Netherlands. His current research interests include hybrid and cyber-physical systems, networked and event-triggered control systems and constrained systems including model predictive control. Maurice served/s on the editorial boards of Automatica, Nonlinear Analysis: Hybrid Systems, Annual Reviews in Control, and IEEE Transactions on Automatic Control. He was a recipient of a personal VICI grant (1.5 MEuro) awarded by STW (Dutch Technology Foundation, NWO/TTW) and is the current chair of the IFAC Technical Committee on Networked Systems. He is the founding father of a bi-annual PhD school on multi-disciplinary research topics such as hybrid, networked and cyber-physical systems educating over 600 PhD students worldwide since 2003. He was/is IPC (co-)chair of IFAC ADHS'12, ECC'13, IFAC NECSYS'13, IFAC ADHS'18, and ECC’21. He is a Fellow of the IEEE.

Non-smooth composite optimization: A primal-dual method based on the proximal augmented Lagrangian

Several problems in the design of large-scale networks of dynamical systems can be formulated as non-smooth composite optimization problems in which the objective function is given by the sum of a differentiable term and a non-differentiable regularizer. For example, the edge addition to improve network performance, redesign of the edge weights to optimize certain performance metric, and selection of important nodes in a network with a given topology fit into this category. In this talk, we will describe a primal-dual method based on the proximal augmented Lagrangian for solving this class of non-smooth optimization problems. After introducing an auxiliary variable, we utilize the proximal operator of the nonsmooth regularizer to transform the associated augmented Lagrangian into a function that is once, but not twice, continuously differentiable. Saddle points of this function, which we call proximal augmented Lagrangian, correspond to the solution of the original optimization problem. This function is used to develop customized algorithms based on the first and second order primal-dual methods. When the differentiable component of the objective function is strongly convex with a Lipschitz continuous gradient, we employ the theory of integral quadratic constraints to prove global exponential stability of the primal-descent dual-ascent gradient method. We also use a generalization of the Hessian to define second order updates on this function and prove global exponential stability of the corresponding differential inclusion. We close the talk by discussing the classes of problems that are amenable to distributed optimization and compare performance of the developed method to the state-of-the-art alternatives.

Mihailo R. Jovanovic is a professor in the Ming Hsieh Department of Electrical and Computer Engineering and the founding director of the Center for Systems and Control at the University of Southern California. He was a faculty in the Department of Electrical and Computer Engineering at the University of Minnesota, Minneapolis, from December 2004 until January 2017, and has held visiting positions with Stanford University and the Institute for Mathematics and its Applications. His current research focuses on large-scale and distributed optimization, design of controller architectures, dynamics and control of fluid flows, and fundamental performance limitations in the control of large-scale networks of dynamical systems. He serves as an Associate Editor of the IEEE Transactions on Control of Network Systems, and had served as the Chair of the APS External Affairs Committee, a Program Vice-Chair of the 55th IEEE Conference on Decision and Control, an Associate Editor of the SIAM Journal on Control and Optimization (from 2014 until 2017), and an Associate Editor of the IEEE Control Systems Society Conference Editorial Board (from 2006 until 2010). Prof. Jovanovic is a fellow of APS and IEEE. He received a CAREER Award from the National Science Foundation in 2007, the George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society in 2013, and the Distinguished Alumnus Award from the UC Santa Barbara in 2014.

Multi-Armed Bandits and Distributed Decision Making in Networks

Decision making in explore-exploit tasks, from resource allocation to search in an uncertain environment, can be modeled using multi-armed bandit (MAB) problems, where the decision maker chooses sequentially in time among multiple options with uncertainty in rewards. I will present a framework for evaluating and designing distributed decision making in heterogeneous multi-player multi-armed bandit problems in which agents communicate according to a network graph. For agents sharing their estimates with neighbors, we propose an explore-exploit centrality index that distinguishes an agent’s performance as a function of its location in the network. For agents only observing their neighbors’ choices and rewards, we propose an index that distinguishes performance as a function of the agent’s “sociability” and that its neighbors. I will discuss applications to robotic teams and to human groups.

Naomi Ehrich Leonard is Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty in Applied and Computational Mathematics at Princeton University. She is a MacArthur Fellow, and Fellow of the American Academy of Arts and Sciences, SIAM, IEEE, IFAC, and ASME. She received her BSE in Mechanical Engineering from Princeton University and her PhD in Electrical Engineering from the University of Maryland. Her research is in control and dynamics with application to multi-agent systems, autonomous vehicles, robotic networks, collective animal behavior, and human decision dynamics.

The Merits of Models in Continuous Reinforcement Learning

Classical control theory and machine learning have similar goals: acquire data about the environment, perform a prediction, and use that prediction to impact the world. However, the approaches they use are frequently at odds. Controls is the theory of designing complex actions from well-specified models, while machine learning makes intricate, model-free predictions from data alone. For contemporary autonomous systems, some sort of hybrid may be essential in order to fuse and process the vast amounts of sensor data recorded into timely, agile, and safe decisions. In this talk, I will examine the relative merits of control-centric model-based and learning-centric model-free methods in data-driven control problems. I will discuss quantitative estimates on the number of measurements required to achieve a high quality control performance and statistical techniques that can distinguish the relative power of different methods. In particular, I will show how model-free methods are considerably less sample efficient than their model-based counterparts. I will also describe how notions of robustness, safety, constraint satisfaction, and exploration can be transparently incorporated in model-based methods. I will conclude with a discussion of possible positive roles for model-free methods in contemporary autonomous systems that may mitigate their high sample complexity and lack of reliability and versatility.

Benjamin Recht is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Ben's research group studies the theory and practice of optimization algorithms with a particular focus on applications in machine learning and control. Ben is the recipient of a Presidential Early Career Award for Scientists and Engineers, an Alfred P. Sloan Research Fellowship, the 2012 SIAM/MOS Lagrange Prize in Continuous Optimization, the 2014 Jamon Prize, the 2015 William O. Baker Award for Initiatives in Research, and the 2017 NIPS Test of Time Award.

Risk-taking bias in human decision-making is controlled via a distributed right-left brain push-pull network

A person's decisions vary even when options stay the same, like when a gambler changes bets despite constant odds of winning. Internal bias (e.g., emotion) contributes to this variability and is shaped by past outcomes, yet its neurobiology during decision-making is not well understood. To map neural networks encoding bias, we administered a gambling task to 10 participants implanted with intracerebral depth electrodes in cortical and subcortical structures. We predicted the variability in betting behavior within and across patients by individual bias, which is estimated through a dynamical model of choice. Our analysis further revealed that high-frequency activity increased in the right hemisphere when participants were biased towards risky bets, while it increased in the left hemisphere when participants were biased away from risky bets. Our findings provide the first electrophysiological evidence that risk-taking bias is a lateralized distributed push-pull neural system governing counterintuitive and highly variable decision-making in humans.

Sri Sarma received the B.S. degree in electrical engineering from Cornell University, Ithaca NY, in 1994; and an M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in, Cambridge MA, in 1997 and 2006, respectively. From 2000-2003 she took a leave of absence to start a data analytics company. From 2006--2009, she was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the Massachusetts Institute of Technology, Cambridge. She is now an associate professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University, Baltimore MD. Her research interests include modeling, estimation and control of neural systems using electrical stimulation. She is a recipient of the GE faculty for the future scholarship, a National Science Foundation graduate research fellow, a L'Oreal For Women in Science fellow, the Burroughs Wellcome Fund Careers at the Scientific Interface Award, the Krishna Kumar New Investigator Award from the North American Neuromodulation Society, and a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and the Whiting School of Engineering Robert B. Pond Excellence in Teaching Award.

Networks and the Macroeconomy

This talk gives an overview of the recent literature on production networks in macroeconomics. It starts by presenting the theoretical foundations of how input-output linkages can function as a mechanism for the propagation and amplification of shocks and how networks can transform microeconomic shocks into macroeconomic fluctuations. It then provides a brief guide to the growing literature that explores these themes empirically and quantitatively.

Alireza Tahbaz-Salehi is an associate professor of Managerial Economics and Decision Sciences Department at the Kellogg School of Management, Northwestern University. Prior to joining Kellogg, he was the Daniel W. Stanton Associate Professor of Business at Columbia Business School. His research focuses on the implications of network economies for information aggregation, business cycle fluctuations, and financial stability.

A Multi-agent Distributed Control Approach to Complex Manufacturing Systems

Many manufacturing systems today have been optimized for mass production, making the same products over and over again with high quality and low cost. There is an increased demand for customized or even personalized production, which can be possible while utilizing many of the same machines currently existing on plant floors. However, the control systems must be completely redefined. The Internet of Things and networked control systems are key enabling technologies to realize this vision.

Decentralized control strategies, specifically agent-based control, can be used to enable customized and personalized production, while improving the flexibility and responsiveness of manufacturing systems. A manufacturing plant floor has both resource agents, representing the processing and material handling resources available, as well as product agents, representing the parts that traverse through the factory, being transformed from raw materials to finished products. Agents each have their own goals, and make decisions based on these goals, their communications with each other, and information available from the physical system. This presentation will cover our recent work on product agents, and discuss how this decentralized approach can lead to improved productivity. Implementation on a small-scale automated testbed will be presented.

Dawn M. Tilbury received the B.S. degree in Electrical Engineering, summa cum laude, from the University of Minnesota in 1989, and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California, Berkeley, in 1992 and 1994, respectively. In 1995, she joined the faculty of the University of Michigan, Ann Arbor, where she is currently Professor of Mechanical Engineering with a joint appointment in Electrical Engineering and Computer Science. Her research interests lie broadly in the area of control systems, including applications to robotics and manufacturing systems. She has published more than 150 articles in refereed journals and conference proceedings. She was elected Fellow of the IEEE in 2008 and Fellow of the ASME in 2012, and is a Life Member of SWE. Since June of 2017, she has been the Assistant Director for Engineering at the National Science Foundation.