Tag: Past

November 11, 2022 11:00am – 12:00pm

Location: Technology Square Research Building 118 auditorium

Abstract

The introduction of machine learning (ML) and artificial intelligence (AI) creates unprecedented opportunities for achieving full autonomy. However, learning-based methods in building autonomous systems can be extremely brittle in practice and are not designed to be verifiable. In this talk, I will present several of our recent efforts that combine ML with formal methods and control theory to enable the design of provably dependable and safe autonomous systems. I will introduce our techniques to generate safety certificates and certified decision and control for complex autonomous systems, even when the systems have a large number of agents, follow nonlinear and nonholonomic dynamics, and need to satisfy high-level specifications.

Biography

Chuchu Fan an Assistant Professor in the Department of Aeronautics and Astronautics and LIDS at MIT. Before that, she was a postdoc researcher at Caltech and got her Ph.D. from the Electrical and Computer Engineering Department at the University of Illinois at Urbana-Champaign in 2019. She earned her bachelor’s degree from Tsinghua University, Department of Automation. Her group at MIT works on using rigorous mathematics including formal methods, machine learning, and control theory for the design, analysis, and verification of safe autonomous systems. Chuchu’s dissertation work “Formal methods for safe autonomy” won the ACM Doctoral Dissertation Award in 2020.

October 21, 2022 11:00am – 12:00 pm

Location: Technology Square Research Building 509

Abstract

Graphon has recently been introduced by Lovasz, Sos, etc. to study very large graphs. A graphon can be understood as either the limit object of a convergent sequence of graphs, or, a statistical model from which to sample large random graphs. We take here the latter point of view and address the following problem: What is the probability that a random graph sampled from a graphon has a Hamiltonian decomposition? We have recently observed the following phenomenon: In the asymptotic regime where the size of the random graph goes to infinity, the probability tends to be either 0 or 1, depending on the underlying graphon. In this talk, we establish this “zero-one” property for the class of step-graphons and provide a geometric characterization.

Biography

Xudong Chen is an Assistant Professor in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder. Prior to that, he was a postdoctoral fellow in the Coordinated Science Laboratory at the University of Illinois, Urbana-Champaign. He obtained the B.S. degree in Electronics Engineering from Tsinghua University, China, in 2009, and the Ph.D. degree in Electrical Engineering from Harvard University, Massachusetts, in 2014. He is an awardee of the 2020 Air Force Young Investigator Program, a recipient of the 2021 NSF CAREER award, and the recipient of the 2021 Donald P. Eckman award. His current research interests are in the area of control theory, stochastic processes, optimization, graph theory and their applications in modeling, analysis, control, and estimation of large-scale complex systems.

April 19, 2022 11:00 am – 12:00 pm

Location: Instructional Center 105

Also live streamed at https://gatech.zoom.us/j/96813456832

Zoom Meeting ID: 968 1345 6832

Abstract

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.

April 8, 2022 11:00am – 12:00pm

Location: 254 Classroom Skiles

Abstract

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.

March 11, 2022 11:00am

Zoom Meeting:

https://gatech.zoom.us/j/92868957681?pwd=c25WbytZSk1pUzMzZVA4MXE3Q0FEUT09

Meeting ID: 928 6895 7681

Passcode: 841672

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.

February 25, 2022 11:00am

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.

November, 16, 2021, 11:00 am – 12:00 pm

Location: Instructional Center 215

Also live streamed at https://bluejeans.com/297337886/0746

Abstract

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.

October 29, 2021 11:00am-12:30pm

Location: Pettit Microelectronics Building, 102A&B Conference Room

Abstract

Lower-body exoskeletons and prostheses have the potential of restoring autonomy to millions of individuals with ambulatory disabilities, and thereby improving quality of life. However, achieving locomotive stability is challenging enough from a robotics and control perspective, let alone addressing the added complexity of satisfying subjective gait preferences of a human user. Thus, the goal of this talk is to discuss how theoretic approaches to bipedal locomotion, based upon nonlinear controllers with formal guarantees of stability, can be coupled with learning to achieve user-preferred stable locomotion.

In my talk, I will first discuss the theoretical underpinnings of achieving provably stable locomotion via nonlinear control theory, and apply this methodology to various dynamic robotic platforms experimentally. Then, I explore the unification of preference-based learning with this formal approach to locomotion to explicitly optimize user preference and comfort. Finally, I experimentally demonstrate the combined framework on a full lower-body exoskeleton, with both non-disabled subjects and subjects with complete motor paraplegia. This work, therefore, demonstrates the utility of coupling preference-based learning with control theory in a structured fashion with a view towards practical application. Ultimately, this result provides a formal approach for achieving locomotive autonomy with robotic assistive devices that has the potential to accelerate clinical implementation and enable the use of these devices in everyday life.

Biography

Maegan Tucker is currently a PhD Candidate in the Mechanical and Civil Engineering Department at the California Institute of Technology (Caltech). She received her Bachelor of Science in Mechanical Engineering from Georgia Tech in 2017. Her research is centered around developing systematic methods of achieving stable, robust, and natural bipedal locomotion on lower-body assistive devices, as well as developing human-in-the-loop methods to customize the experimental locomotion based on subjective user feedback.

September 17, 2021 10-11am

BlueJeans: https://gatech.bluejeans.com/877658093/8250

Abstract

Recent revolutions in sensing, computation, communication, and actuation technologies have been boosting the development and implementation of data-driven decision making, greatly advancing the monitoring and control of complex network systems. In this talk, we will focus on real-time distributed decision-making algorithms for networked systems. The first part will be on the  scalable multiagent reinforcement learning algorithms and the second part will be on the model free control methods for power systems based on continuous time zeroth-order optimization methods. We will show that exploiting network structure or underlying physical dynamics will facilitate the design of scalable real-time learning and control methods.

Biography

Na Li is a Gordon McKay professor in Electrical Engineering and Applied Mathematics at Harvard University.  She received her Bachelor degree in Mathematics from Zhejiang University in 2007 and Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate at Massachusetts Institute of Technology 2013-2014.  Her research lies in control, learning, and optimization of networked systems, including theory development, algorithm design, and applications to real-world cyber-physical societal system. She received NSF career award (2016), AFSOR Young Investigator Award (2017), ONR Young Investigator Award(2019),  Donald P. Eckman Award (2019), McDonald Mentoring Award (2020), along with some other awards.