Along with the poster, a pre-recorded video presentation explaining the work submitted in the poster is required. The presentation slides should not be merely a single slide showing the submitted poster. Below is some requirement and instructions on submitting your video.
Video file requirements:
Recommended video length for regular and invited presentations: 2 minutes
Maximum video length for regular and invited presentations: 3 minutes
File size limit for regular/invited/tutorial presentations: 100MB
File format: MP4
Minimum height: 480 pixels
Video aspect ratio: 16:9
Presentation slides are required to be no more 5 slides and to submitted as a PDF along with the video
Recording instructions:
A recommended tool for effective recording is using the Zoom platform. Create a meeting where the speaker is the only participant, share the screen (presentation slides) and activate the presenter camera and record. Please refer to the following link for further instructions on Zoom recording.
Make sure that you record the whole screen to avoid having trouble with the aspect ratio of your recording.
It is highly recommended that the presenter’s face is also recorded in the video. However, by doing so, make sure that the video inset is not hiding important parts of your slides.
Use suitable software to ensure that the video size is below 100MB and has the right format (MP4). For example, “Handbrake” is an open-source video transcoder for converting video to MP4 and for file compression. The default settings typically reduce the size of Zoom-generated videos by a factor of three.
Abstract: We report recent results on two classical inference problems in linear systems. (i) In the first problem, we wish to identify the dynamics of a canonical linear time-invariant systems $x_{t+1}=Ax_t+\eta_{t+1}$ from an observed trajectory. We provide system-specific sample complexity lower bound satisfied by any $(\epsilon, \delta)$-PAC algorithm, i.e., yielding an estimation error less than $\epsilon$ with probability at least $1-\delta$. We further establish that the Ordinary Least Squares estimator achieves this fundamental limit. (ii) In the second inference problem, we aim at identifying the best arm in bandit optimization with linear rewards. We derive instance-specific sample complexity lower bounds for any $\delta$-PAC algorithm, and devise a simple track-and-stop algorithm achieving this lower bound. In both inference problems, the analysis relies on novel concentration results for the spectrum of the covariates matrix.
Biography
Alexandre Proutiere is professor in the Decision and Control System division at KTH, Stockholm Sweden since 2011. Before joining KTH he was esearcher at Microsoft Research (Cambridge) from 2007 to 2011,research engineer at France Telecom R&D from 2000 to 2006, Invited lecturer and researcher at the computer science department ENS Paris from 2004 to 2006. He received a PhD in Applied Mathematics from Ecole Polytechnique, graduated in Mathematiques from Ecole Normale Superieure. He also received an engineering degree from Telecom Paris, and is an engineer from Corps des Mines. He won the ACM Sigmetrics rising star award in 2009, ACM best papers awards at Sigmetrics 2004 and 2010, and Mobihoc 2009. His research interests are in probability and their applications, and more specifically today in learning in dynamical systems.
The 2021 Georgia Tech Decision and Control Laboratory (DCL) Student Symposium will be held virtually during April 15th-16th (Thursday-Friday), 2021.
The purpose of the symposium is to bring all students and postdoctoral researchers in systems and control together to share their research. The student’s presentation will consist of 3-minute recorded talks together with corresponding electronic poster presentations. Additionally, the symposium will include planetary talks by distinguished scholars outside Georgia Tech, and some other activities. The full program will be announced by the middle of March.
The submission of the electronic poster and 3-minute recorded videos will be open from Monday, March 15th till Sunday, April 4th, 2021. Information for submission and guidelines for the poster formatting and video recordings will be made available soon.
W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering at UIUC.
Abstract
Learning-based control paradigms have seen many success stories with various robots and co-robots in recent years. However, as these robots prepare to enter the real world, operating safely in the presence of imperfect model knowledge and external disturbances is going to be vital to ensure mission success. In the first part of the talk, we present an overview of L1 adaptive control, how it enables safety in autonomous robots, and discuss some of its success stories in the aerospace industry. In the second part of the talk, we present some of our recent results that explore various architectures with L1 adaptive control while guaranteeing performance and robustness throughout the learning process. An overview of different projects at our lab that build upon this framework will be demonstrated to show different applications.
Biography
Naira Hovakimyan received her MS degree in Theoretical Mechanics and Applied Mathematics in 1988 from Yerevan State University in Armenia. She got her Ph.D. in Physics and Mathematics in 1992 from the Institute of Applied Mathematics of Russian Academy of Sciences in Moscow. She is currently a W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering at UIUC. In 2015 she was named inaugural director for Intelligent Robotics Lab of Coordinated Science Laboratory at UIUC. She has co-authored two books, eleven patents and more than 450 refereed publications. She was the recipient of the SICE International scholarship for the best paper of a young investigator in the VII ISDG Symposium (Japan, 1996), the 2011 recipient of AIAA Mechanics and Control of FlightAward, the 2015 recipient of SWE Achievement Award, the 2017 recipient of IEEE CSS Award for Technical Excellence in Aerospace Controls, and the 2019recipient of AIAA Pendray Aerospace Literature Award. In 2014 she was awarded the Humboldt prize for her lifetime achievements. She is Fellow and life member of AIAA and a Fellow of IEEE. She is cofounder and chief scientist of IntelinAir. Her work in robotics for elderly care was featured in the New York Times, on Fox TV and CNBC. Her research interests are in control, estimation and optimization, autonomous systems, game theory and their broad applications across various industries.
Across many sectors, ranging from manufacturing to healthcare to the military theater, there is growing interest in the potential impact of automation that is truly collaborative with humans. Realizing this impact, though, rests on first addressing the fundamental challenge of designing automation to be aware of, and responsive to, the human with whom it is interacting. While a significant body of work exists in intent inference based on human motion, a human’s physical actions alone are not necessarily a predictor of their decision-making. Indeed, cognitive factors, such as trust and workload, play a substantial role in their decision making as it relates to interactions with autonomous systems. In this talk, I will describe our interdisciplinary efforts at tackling this problem, focusing on recent work in which we synthesized a near-optimal control policy using a trust-workload POMDP (partially-observable Markov decision process) model framework that captures changes in human trust and workload for a context involving interactions between a human and an intelligent decision-aid system. Using automation transparency as the feedback variable, we designed a policy to balance competing performance objectives in a reconnaissance mission study in which a virtual robotic assistant aids human subjects in surveying buildings for physical threats. I will present experimental validation of our control algorithm through human subject studies and highlight how our approach is able to mitigate the negative consequences of “over trust†which can occur in such interactions. I will also briefly discuss our related work involving the use of psychophysiological data and classification techniques as an alternative method toward real-time trust estimation.
Biography
Dr. Neera Jain is an Assistant Professor in the School of Mechanical Engineering and a faculty member in the Ray W. Herrick Laboratories at Purdue University. She directs the Jain Research Laboratory with the aim of advancing technologies that will have a lasting impact on society through a systems-based approach, grounded in dynamic modeling and control theory. A major thrust of her research is the design of human-aware automation through control-oriented modeling of human cognition. A second major research thrust is optimal design and control of complex energy systems. Dr. Jain earned her M.S. and Ph.D. degrees in mechanical engineering from the University of Illinois at Urbana-Champaign in 2009 and 2013, respectively. She earned her S.B. from the Massachusetts Institute of Technology in 2006. Upon completing her Ph.D., Dr. Jain was a visiting member of the research staff at Mitsubishi Electric Research Laboratories where she designed model predictive control algorithms for HVAC systems. In 2015 she was a visiting summer researcher at the Air Force Research Laboratory at Wright-Patterson Air Force Base. Dr. Jain and her research have been featured in NPR and Axios. As a contributor for Forbes.com, she writes on the topic of human interaction with automation and its importance in society. Her research has been supported by the National Science Foundation, Air Force Research Laboratory, Office of Naval Research, as well as private industry.
I will introduce three problems in mathematical oncology all of which involve nonlinear dynamics and control theory. First, I will describe our work using Markov chain models to forecast metastatic progression. The models treat progression as a (weighted) random walk on a directed graph whose nodes are tumor locations, with transition probabilities obtained through historical autopsy date (untreated progression) and longitudinal data (treated) from Memorial Sloan Kettering and MD Anderson Cancer Centers. Then, I will describe our models that use evolutionary game theory (replicator dynamics with prisoner’s dilemma payoff matrix) to design multi-drug adaptive chemotherapy schedules to mitigate chemo-resistance by suppressing ‘competitive release’ of resistant cell populations. The models highlight the advantages of antagonistic drug interactions (over synergistic ones) in shaping the fitness landscape of co-evolving populations. Finally, I will describe our work on developing optimal control schedules (based on Pontryagin’s maximum principle) that maximize cooperation for prisoner’s dilemma replicator dynamical systems. As much as possible with the Zoom format, I hope the seminar will be interactive and a starting point for further discussions.
Biography
Professor Newton received his B.S. (cum laude) degree in Applied Mathematics/Physics at Harvard University in 1981 and his Ph.D. in 1986 from the Division of Applied Mathematics at Brown University. He then moved to the Mathematics Department at Stanford University to work as a post-doctoral scholar under J.B. Keller. He became Assistant (1987) and Associate Professor (1993) in the Mathematics Department at the University of Illinois Champaign-Urbana (UIUC) and at the Center for Complex Systems Research (CCSR) at the Beckman Institute. In 1993 he moved to the Aerospace & Mechanical Engineering Department and the Mathematics Department at the University of Southern California and was promoted to Full Professor in 1998. Trained as an applied mathematician, Professor Newton’s work focuses on developing mathematical models for nonlinear dynamical processes in continuum mechanics and biophysics, currently focusing mostly on mathematical oncology and systems biology. He has held visiting appointments at Caltech, Brown, Hokkaido University, The Kavli Institue for Theoretical Physics at UC Santa Barbara, and The Scripps Research Institute where he functioned as head of the mathematical modeling section of the NCI supported Physical Sciences Oncology Center (2009-2014). He is currently a Professor of Applied Mathematics, Engineering, and Medicine in the Viterbi School of Engineering, the Dornsife College of Letters, Arts and Sciences, the Norris Comprehensive Cancer Center in the Keck School of Medicine, and a founding affiliate member of the LJ Ellison Institute for Transformative Medicine of USC. He currently serves as Editor-in-Chief of the Journal of Nonlinear Science (SpringerNature).
This talk will give an overview of old and new results and directions in stochastic approximation algorithms, broadly split into basic theory, variants, and applications. Central to all this will be the ‘o.d.e’ (for ‘Ordinary Differential Equations’) approach to their analysis.
Biography
Prof. Vivek S. Borkar is CSIR Bhatnagar Emeritus Fellow at Indian Institute of Technology Bombay. He obtained his B.Tech. (EE) from IIT Bombay, M.S. (Systems and Control) from Case Western Reserve Uni., and Ph.D. (EECS) from the Uni. of California, Berkeley, in 1976, 77, 80 resp. He has held positions in TIFR Centre and Indian Institute of Science, Bengaluru, and Tata Inst. of Fundamental Research and IIT Bombay in Mumbai. He is a Fellow of IEEE, AMS, TWAS and various science and engineering academies in India. He was awarded the S. S. Bhatnagar Prize in engineering sciences by the Government of India in 1992 and was an invited speaker at the International Congress of Mathematicians in Madrid in 2006. His research interests are in stochastic control and optimization, inclusive of theory, algorithms, and applications, particularly to communications.
Perhaps the most challenging aspect of research on multi-agent dynamical systems, formulated as non-cooperative stochastic differential/dynamic games (SDGs) with asymmetric dynamic information structures is the presence of strategic interactions among agents, with each one developing beliefs on others in the absence of shared information. This belief generation process involves what is known as second-guessing phenomenon, which generally entails infinite recursions, thus compounding the difficulty of obtaining (and arriving at) an equilibrium. This difficulty is somewhat alleviated when there is a high population of agents (players), in which case strategic interactions at the level of each agent become much less pronounced. This leads, under some structural constraints, to what is known as mean field games (MFGs), which have been the subject of intense research activity during the last ten years or so.
MFGs constitute a class of non-cooperative stochastic differential games where there is a large number of players or agents who interact with each other through a mean field coupling term—also known as the mass behavior or the macroscopic behavior in statistical physics—included in the individual cost functions and/or each agent’s dynamics generated by a controlled stochastic differential equation, capturing the average behavior of all agents. One of the main research issues in MFGs with no hierarchy in decision making is to study the existence, uniqueness and characterization of Nash equilibria with an infinite population of players under specified information structures and further to study finite-population approximations, that is to explore to what extent an infinite-population Nash equilibrium provides an approximate Nash equilibrium for the finite-population game, and what the relationship is between the level of approximation and the size of the population.
Following a general overview of the difficulties brought about by strategic interactions in finite-population SDGs, the talk will dwell on two classes of MFGs: those characterized by risk sensitive (that is, exponentiated) objective functions (known as risk-sensitive MFGs) and those that have risk-neutral (RN) objective functions but with an additional adversarial driving term in the dynamics (known as robust MFGs). In stochastic optimal control, it is known that risk-sensitive (RS) cost functions lead to a behavior akin to robustness, leading to establishment of a connection between RS control problems and RN minimax ones. The talk will explore to what extent a similar connection holds between RS MFGs and robust MFGs, particularly in the context of linear-quadratic problems, which will allow for closed-form solutions and explicit comparisons between the two in both infinite- and finite-population regimes and with respect to the approximation of Nash equilibria in going from the former to the latter. The talk will conclude with a brief discussion of several extensions of the framework, such as to hierarchical decision structures with a small number of players at the top of the hierarchy (leaders) and an infinite population of agents at the bottom (followers) as well as to games where players make noisy observations.
Biography
Tamer BaÅŸar has been with the University of Illinois at Urbana-Champaign since 1981, where he holds the academic positions of Swanlund Endowed Chair; Center for Advanced Study (CAS) Professor of Electrical and Computer Engineering; Professor, Coordinated Science Laboratory; Professor, Information Trust Institute; and Affiliate Professor, Mechanical Science and Engineering. He is also the Director of the Center for Advanced Study. At Illinois, he has also served as Interim Dean of Engineering and Interim Director of the Beckman Institute for Advanced Science and Technology. He is a member of the US National Academy of Engineering; Fellow of IEEE, IFAC, and SIAM; a past president of the IEEE Control Systems Society (CSS), the founding president of the International Society of Dynamic Games (ISDG), and a past president of the American Automatic Control Council (AACC). He has received several awards and recognitions over the years, including the highest awards of IEEE CSS, IFAC, AACC, and ISDG, the IEEE Control Systems Technical Field Award, and a number of international honorary doctorates and professorships, most recently an honorary doctorate from KTH, Sweden. He has over 900 publications in systems, control, communications, optimization, networks, and dynamic games, including books on non-cooperative dynamic game theory, robust control, network security, wireless and communication networks, and stochastic networks. He was Editor-in-Chief of the IFAC Journal Automatica between 2004 and 2014, and is currently editor of several book series. His current research interests include stochastic teams, games, and networks; multi-agent systems and learning; data-driven distributed optimization; epidemics modeling and control over networks; security and trust; energy systems; and cyber-physical systems.
Assistant Professor; Demetrius T. Paris Junior Professor
Congratulations to Sam Coogan on receiving the 2020 Donald P. Eckman Award!
Sam was recognized with this award at the American Control Conference, which was virtually held July 1-3. The Eckman Award recognizes an outstanding young engineer in the field of automatic control. Sam’s research is in the area of dynamical systems and autonomy and focuses on developing fundamental theory for verification and control of networked and autonomous systems with an emphasis on applications in transportation systems.
