Location: 118 Auditorium, Technology Square Research Building
George Yin
Professor
Department of Mathematics
University of Connecticut
Abstract
In this talk, we will present some of our recent work on stochastic Kolmogorov systems. The motivation stems from dealing with important issues of ecological and biological systems. Focusing on environmental noise, we aim to address such fundamental questions: “what are the minimal conditions for long-term persistence of a population, or long-term coexistence of interacting species”. Some optimal control problems are also examined. [The talk reports some of our joint works with D.H. Nguyen, N.T. Dieu, N.H. Du, and N.N Nguyen.]
Biography
George Yin received the B.S. degree in mathematics from the University of Delaware in 1983, and the M.S. degree in electrical engineering and the Ph.D. degree in applied mathematics from BrownUniversity in 1987. He joined the Department of Mathematics, Wayne State University in 1987 and became Professor in 1996 and University Distinguished Professor in 2017. He moved to the University of Connecticut in 2020. His research interests include stochastic processes, stochastic systems theory, and applications. He was Chair of the SIAM Activity Group on Control and Systems Theory, and was Co-chair of a number of conferences; he served on the Board of Directors of the American Automatic Control Council. He is Editor-in-Chief of SIAM Journal on Control and Optimization. He serves on (or served on) editorial boards of over 20 journals and book series including Automatica 1995-2011, IEEE Transactions on Automatic Control 1994-1998, and IEEE Control Systems Letters 2017-2019. He is a Fellow of IEEE, Fellow of IFAC, and Fellow of SIAM.
Dr. Radhakishan Sohanlal Baheti, a pioneering member of the IEEE Control Systems Society (CSS), passed away on March 9, 2021 at the age of 76. He served as a program director for the Energy, Power, Control, and Networks Program in the Division of Electrical, Communications and Cyber Systems at the U.S. National Science Foundation (NSF), overseeing a broad spectrum of research areas in control theory, power systems, robotics, multiagent systems, and data science.
Dr. Baheti received the B.S. and M.S. degrees in electrical engineering in India from Visvesvaraya Regional College of Engineering, Nagpur, and Birla Institute of Technology and Science, Pilani, respectively. In 1970, he came to the United States and received an M.S. degree in information and computer science from the University of Oklahoma and a Ph.D. degree in electrical and computer engineering from Oregon State University. In 1976, Dr. Baheti joined the Control Engineering Laboratory of General Electric (GE) Corporate Research and Development Center in Schenectady, New York. His work focused on advanced multivariable control for jet engines, signal and image processing systems, computer-aided control system design, vision-based robots for precision welding, model-based fault identification, and parallel implementation of Kalman filters. Dr. Baheti and his colleagues received the IR-100 Award for the robotic welding vision system. During his tenure at GE, he organized a series of educational workshops for engineers that resulted in innovative product developments and enhanced university collaborations with GE.
In 1989, Dr. Baheti (or Kishan, as he was fondly known to his friends and colleagues) joined the NSF as a program director in the Division of Electrical, Communications, and Cyber Systems. For more than 30 years in this role, he was a tireless worker for the support of the entire control system community in the United States and abroad. Among his many seminal contributions were the development of NSF initiatives on cyberphysical systems, semiconductor manufacturing, the National Robotics Initiative, and the NSF Electric Power Research Institute (NSF-EPRI) Initiative on Intelligent Control.
In addition to his usual directorial duties in control engineering, Kishan was also involved in many multidisciplinary research initiatives:
Pramod Khargonekar:
I had the good fortune to work with Kishan closely from 2013 to 2016 during my tenure as assistant director for the Engineering Directorate at NSF. I saw him directly in action as he worked with colleagues from many other divisions to develop numerous new programs in smart grids, wireless communications, sensors, micro and nano systems, science of learning, and dynamics and control of biological and medical systems. Without his creative energy and people skills, our community would have missed many of these opportunities.
Kishan served as an associate editor for IEEE Transactions on Automatic Control. He was a member of the CSS Board of Governors, chair for the Public Information Committee, and Awards chair for the American Automatic Control Council. He received the Distinguished Member Award from the IEEE CSS. His other awards include the 2012 Robert H. Janowiak Outstanding Leadership and Service Award from the Electrical and Computer Engineering Department Heads Association (ECEDHA), Outstanding Men of America Award, and multiple service awards from the NSF. In 1997, he was elected Fellow of IEEE.
Kishan is best known for his pioneering advocacy for the control systems research community in the United States through his continuous collaboration with IEEE CSS and IEEE Power & Energy Society. He was instrumental in bringing researchers and educators in these communities together through a series of workshops, tutorials, special sessions, and industry engagement events held at leading conferences. From 2013 to 2019, he organized six international workshops on distributed energy management systems that brought together leading international researchers in smart grids from the United States, Japan, Germany, Norway, and India. In recent years, he also led NSF efforts in the 10 big ideas on harnessing the data revolution, connecting researchers in control theory with those in machine learning and data science. His openness to new directions, new areas of research, and community building are deemed as legendary by all his peers.
Aranya Chakrabortty:
Kishan played a leading role in shaping the intellectual direction of our field, and a number of us got our very first grants through Kishan’s programs at NSF. He was the biggest cheerleader there is for young researchers in our community … Looking at the volume of work that Kishan has done in his life makes me feel that if I can even rise up to his knees, I would consider that to be an achievement of a lifetime.
Participants at the System Identification Workshop, GE CR&D, June 5–6, 1986. Front middle: Kishan Baheti and Lennart Ljung; second row: Howard Kaufman (far left) and Joe Chow (second from right).
Kishan was an avid long-distance runner, a marathoner, an ardent yoga practitioner, and a voracious reader. He frequently participated in local run events in the Washington, D.C. area as well as in the Boston Marathon.
Kishan Baheti running in the Boston Marathon
These many professional accomplishments and impacts only capture a partial picture of Kishan Baheti. While he was a brilliant and successful professional, his personal qualities made him a great human being and a friend to many. He was kindhearted and magnanimous. He had deep compassion for fellow human beings. He had a tremendous capacity to see the positive and good in each and every situation, no matter how difficult. He believed in others and helped them believe in themselves. He had that rare quality: wisdom. As a part of the control systems research community, we are all deeply grateful for what we have received from our beloved Kishan. We will miss him sorely and will cherish his memories forever.
May Kishan rest in peace.
The material comes from Control Systems Magazine, Aug 2021, Page 99-102.
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Making use of modern black-box AI tools is potentially transformational for online optimization and control. However, such machine-learned algorithms typically do not have formal guarantees on their worst-case performance, stability, or safety. So, while their performance may improve upon traditional approaches in “typical” cases, they may perform arbitrarily worse in scenarios where the training examples are not representative due to, e.g., distribution shift or unrepresentative training data. This represents a significant drawback when considering the use of AI tools for energy systems and autonomous cities, which are safety-critical. A challenging open question is thus: Is it possible to provide guarantees that allow black-box AI tools to be used in safety-critical applications? In this talk, I will introduce recent work that aims to develop algorithms that make use of black-box AI tools to provide good performance in the typical case while integrating the “untrusted advice” from these algorithms into traditional algorithms to ensure formal worst-case guarantees. Specifically, we will discuss the use of black-box untrusted advice in the context of online convex body chasing, online non-convex optimization, and linear quadratic control, identifying both novel algorithms and fundamental limits in each case.
Biography
Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences at Caltech. He received his Ph.D., M.Sc., and B.Sc. in Computer Science from Carnegie Mellon University and has been a faculty at Caltech since 2007. Adam’s research strives to make the networked systems that govern our world sustainable and resilient. He is best known for his work spearheading the design of algorithms for sustainable data centers and his co-authored book on “The Fundamentals of Heavy-tails”. He is a recipient of multiple awards, including the ACM Sigmetrics Rising Star award, the ACM Sigmetrics Test of Time award, the IEEE Communications Society William R. Bennett Prize, multiple teaching awards, and is a co-author of papers that have received “best paper” awards at a wide variety of conferences across computer science, power engineering, and operations research.
We invite you to submit the title and a short abstract of your work, should you wish to present your research at the symposium, no later than March 31st, 2022. To register, please fill out the registration form.
Guidelines for Poster Submission
For the DCL poster design, please feel free to choose any template/design available on the internet or provided by MS PowerPoint or even create your own poster design.
Please upload your poster to the google drive folder if you will present at the symposium. The deadline for the poster submission is April 10th, 2022.
Additionally, please print your poster and present it at the poster session in LOVE Manufacturing Building (MRDC II) hallway. The LOVE building will be available for poster set-up beginning at 10:00 am on Thursday. The poster session is scheduled to begin at 10:30 am.
The Media Lab in Library offers students and staff large format gloss printing. Here is the link to submit a poster for printing in Media Lab. The cost is
18″ x 24″ poster: $4.50
24″ x 36″ poster: $9.00
We will be providing easels, form boards (30×40), and clips.
We provide a poster example below for your reference:
April 14, 2022 2:30 pm – 3:30 pm, Plenary talk 2 for DCL Student Symposium 2022
Location: 4211 Conference room, MRDC building
Craig Woolsey
Professor
Virginia Tech
Abstract
Model-based control design for ocean and atmospheric vehicles typically starts with a linear approximation of the system dynamics. And for good reason. A control system based on a linearized dynamic model outperforms alternatives when the linear model is accurate — that is, for small perturbations from a nominal state. Control system performance degrades with the approximation, however. In scenarios where the small perturbation model is inappropriate, one must consider nonlinear modeling and control. Examples from the speaker’s experience include a small surface craft tracking a desired trajectory with variable speed and course, a submerged vessel maneuvering near the surface in waves, biomimetic vehicles that vary their shape for propulsion and control in water or in air, and a fixed-wing aircraft that maneuvers aggressively through the atmosphere. In considering these examples, a unifying theme will emerge: using the (nonlinear) mechanical system structure of the governing equations to obtain provably effective control strategies.
Biography
Craig Woolsey a Professor in Virginia Tech’s Kevin T. Crofton Department of Aerospace and Ocean Engineering (AOE). The principal aim of Prof. Woolsey’s research is to improve performance and robustness of autonomous vehicles, particularly ocean and atmospheric vehicles. Woolsey is a past recipient of the NSF Career Award and the ONR Young Investigator Program Award and recently served on the National Academies Committee to Assess the Risks of Unmanned Aircraft Systems (UAS) Integration. Woolsey is vice-chair (and chair-elect) of the AIAA Atmospheric Flight Mechanics Technical Committee (TC) and an active member of the IEEE TC on Manufacturing, Robotics, and Automation and the IFAC TC on Marine Systems. Prof. Woolsey teaches courses in ocean and atmospheric vehicle dynamics and in linear and nonlinear control. With AOE colleagues Mazen Farhood and Cornel Sultan, Woolsey co-directs the Nonlinear Systems Laboratory (http://www.nsl.aoe.vt.edu/). With his colleague Kevin Kochersberger, Prof. Woolsey also co-directs the Virginia Tech site within the Center for Unmanned Aircraft Systems (https://c-uas.org/), an NSF Industry/University Cooperative Research Center.
April 14, 2022 1:00 pm – 2:00 pm, Plenary talk 1 for DCL Student Symposium 2022
Location: 4211 Conference room, MRDC building
Ufuk Topcu
Associate Professor
The University of Texas at Austin
Abstract
Autonomous systems are emerging as a driving technology for countlessly many applications. Numerous disciplines tackle the challenges toward making these systems trustworthy, adaptable, user-friendly, and economical. On the other hand, the existing disciplinary boundaries delay and possibly even obstruct progress. I argue that the nonconventional problems that arise in designing and verifying autonomous systems require hybrid solutions at the intersection of learning, formal methods, and controls. I will present examples of such hybrid solutions in the context of learning in sequential decision-making processes: physics-informed neural networks for the modeling of unknown dynamical systems and joint task inference and reinforcement learning. These results offer novel means for effectively integrating physics-based, contextual, or structural prior knowledge into data-driven learning algorithms. They improve data efficiency by several orders of magnitude and generalizability to environments and tasks that the system had not experienced previously.
Biography
Ufuk Topcu is an Associate Professor in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin, where he holds the W. A. “Tex” Moncrief, Jr. Professorship in Computational Engineering and Sciences I. He is a core faculty member at the Oden Institute for Computational Engineering and Sciences and Texas Robotics and the director of the Autonomous Systems Group. Ufuk obtained his Doctor of Philosophy degree from the University of California, Berkeley in 2008. Prior to joining The University of Texas at Austin, he was with the Department of Electrical and Systems Engineering at the University of Pennsylvania. He was a postdoctoral scholar at the California Institute of Technology until 2012. Ufuk’s research focuses on the theoretical and algorithmic aspects of the design and verification of autonomous systems, typically in the intersection of formal methods, reinforcement learning, and control theory. He takes a relatively broad view on autonomy and tends to tackle abstract problems motivated by challenges cutting across multiple applications of autonomy. His research contributions have been recognized by the NSF CAREER Award, the Air Force Young Investigator Award, the IEEE CSS Antonio Ruberti Young Researcher Prize, and Oden Institute Distinguished Researcher Award. He is a member of the Computing Community Consortium Council.
The optimal mass transport problem is a classical problem in mathematics, and dates back to 1781 and work by G. Monge where he formulated an optimization problem for minimizing the cost of transporting soil for construction of forts and roads. Historically the optimal mass transport problem has been widely used in economics in, e.g., planning and logistics, and was at the heart of the 1975 Nobel Memorial Prize in Economic Sciences. In the last two decades there has been a rapid development of theory and methods for optimal mass transport and the ideas have attracted considerable attention in several economic and engineering fields. These developments have led to a mature framework for optimal mass transport with computationally efficient algorithms that can be used to address problems in the areas of systems, control, and estimation.
In this talk, I will give an overview of the optimal mass transport framework and show how it can be applied to solve problems in state estimation and ensemble control. In particular, I will present a version of the optimal transport problem where the cost function is adapted to take into account the dynamics of the underlying systems. Also duality results between control and estimation will be considered together with illustrative examples. The approach is non-parameteric and can be applied to problems ranging from a multi-agent systems to a continuous flow of systems. This problem can also be formulated as a multi-marginal optimal transport problem and we show how several common problems, e.g., barycenter and tracking problems, can be seen as special cases of this. This naturally leads to consider structured optimal transport problems, which both can be used to model a rich set of problems and can be solved efficiently using customized methods inspired by the Sinkhorn iterations. This also connects to the Schrödinger bridge problem and ensemble hidden Markov models.
Biography
Johan Karlsson received an MSc degree in Engineering Physics from KTH in 2003 and a PhD in Optimization and Systems Theory from KTH in 2008. From 2009 to 2011, he was with Sirius International, Stockholm. From 2011 to 2013 he was working as a postdoctoral associate at the Department of Computer and Electrical Engineering, University of Florida. From 2013 he joined the Department of Mathematics, KTH, as an assistant professor and since 2017 he is working as an associate professor. His current research interests include inverse problems, methods for large scale optimization, and model reduction, for applications in remote sensing, signal processing, and control theory.
Department of Mechanical and Aerospace Engineering
Rutgers, The State University of New Jersey
Abstract
Since the launch of the first satellite (Sputnik 1) in 1957, humans have created a lot of objects in orbit around Earth. The estimated number of space objects larger than 10 cm is presently approaching 37,000, 1000000 between 1 and 10cm, and for objects smaller than 1cm the number exceeds 330 million. Both the number of space objects and the number of conflicts between these objects are increasing exponentially.
This talk overviews the research we have been pursuing on to address the challenges posed by the growth of space debris. We will first introduce the Modified Chebyshev-Picard Iteration (MCPI) Methods, which are a set of parallel-structured methods for solution of initial value problems and boundary value problems. The MCPI methods have been recommended as the “promising and parallelizable method for orbit propagation” by the National Research Council. The talk will then highlight our recent results to develop a physics-based learning approach to predict space objects’ trajectories with higher accuracy and higher reliability than those of the current methods. Last, we will present our research in autonomous, performance-driven, and online trajectory planning and tracking of space robotics for space debris removal with the goal to solve the problem in real time.
Biography
Dr. Xiaoli Bai is an Associate Professor in the department of Mechanical and Aerospace Engineering at Rutgers, The State University of New Jersey. She obtained her PhD degree of Aerospace Engineering from Texas A&M University. Her current research interests include astrodynamics and Space Situational Awareness; spacecraft guidance, control, and space robotics; and Unmanned Aerial Vehicle navigation and control. She was an Associate Fellow for the Class of 2021 in the American Institute of Aeronautics and Astronautics (AIAA), a recipient of the 2019 NASA Early Career Faculty award, The 2016 Air Force Office of Scientific Research Young Investigator Research Program award, Outstanding Young Aerospace Engineer Award from Texas A&M University in 2018, A. Water Tyson Assistant Professor Award from Rutgers in 2018, and Amelia Earhart Fellowship.
Location: Technology Square Research Building (TSRB) 509
Abstract
The web of interconnections between today’s technology and society is upending many traditional ways of doing things: the internet of things, bitcoin, the sharing economy, and connected autonomous vehicles are increasingly in the public mind. As such, computer scientists and engineers must be increasingly conscious of the interplay between the technical performance of their systems and the personal objectives of users, customers, and adversaries. I will present our recent work studying the design tradeoffs faced by a planner who wishes to influence and optimize the behavior of a group of self-interested individuals. A key goal is to balance the potential benefits of implementing a given behavior-influencing scheme with its potential costs; we seek to systematically avoid schemes which are likely to create perverse incentives. We will explore these concepts in two contexts: routing of autonomous vehicles in mixed-autonomy transportation networks and decision design for communication-denied distributed multiagent systems. We will ask “when is altruism good?” through the lens of the Price of Anarchy and a new conceptual framework that we term the “Perversity Index,” which captures the potential harm that an incentive scheme may cause. Ultimately, we seek to develop foundations for a theory of robust socially-networked systems which leverages the decision processes of automated components to enhance overall system reliability and performance.
Biography
Philip Brown is an Assistant Professor in the Department of Computer Science at the University of Colorado at Colorado Springs. He received the PhD in Electrical and Computer Engineering from the University of California, Santa Barbara under the supervision of Jason Marden. He received the Master and Bachelor of Science in Electrical Engineering from the University of Colorado at Boulder and Georgia Tech (respectively), between which he developed process control technology for the biofuels industry. Philip is interested in the impact of human social behavior on the performance of large-scale infrastructure and software systems, and studies this by combining concepts from game theory and feedback control of distributed systems. Philip was a finalist for best student paper at IEEE CDC in 2016 and 2017, received the 2018 CCDC Best PhD Thesis award from UCSB, and the Best Paper Award from GameNets 2021.
Dynamic watermarking, as an active intrusion detection technique, can potentially detect replay attacks, spoofing attacks, and deception attacks in the feedback channel for control systems. In this talk, we will discuss our recent work on a novel dynamic watermarking algorithm for finite-state finite-action Markov decision processes and present bounds on the mean time between false alarms, and the mean delay between the time an attack occurs and when it is detected. We further compute the sensitivity of the performance of the control system as a function of the watermark. We demonstrate the effectiveness of the proposed dynamic watermarking algorithm by detecting a spoofing attack in a sensor network system.
Biography
Abhishek Gupta is an assistant professor at Electrical and Computer Engineering at The Ohio State University. He completed his Ph.D. in Aerospace Engineering (2014), MS in Applied Mathematics (2012), and MS in Aerospace Engineering (2011), all from University of Illinois at Urbana-Champaign (UIUC). He completed his undergraduate in Aerospace Engineering from Indian Institute of Technology, Bombay, India (2005-09). His research develops new theory and algorithms for stochastic control problems, games, and optimization problems, with applications to secure cyberphysical systems and develop market mechanisms for deep renewable integration. He is a recipient of Kenneth Lee Herrick Memorial Award at UIUC and Lumley Research Award at OSU.
