Uncategorized

Friday, April 29, 2022
4:10 – 5:00 p.m.
Zoom: https://tamu.zoom.us/j/96343481647
 
Dr. Vijay Subramanian
Associate Professor in the EECS at the University of Michigan, Ann Arbor, MI

Talking Points

• Multi-agent dynamic games with asymmetric information
• Information states
• Compression based equilibria
• Sequential decomposition

Title
“Games in Multi-Agent Dynamic Systems: Decision-Making with Compressed Information”

Abstract
The model of multi-agent dynamic systems has a wide range of applications in numerous socioeconomic and engineering settings: spectrum markets, e-commerce, transportation networks, power systems, etc. In this model, each agent takes  actions over time to interact with the underlying system as well as each other to achieve their respective objectives. In many applications of this model, agents have access to a huge amount of information that increases over time. Determining solutions of such multi-agent dynamic games can be complicated due to the huge domains of strategies. Meanwhile, agents have restrictions on their computational power and communication capability as well as latency limitations, which prevent them from implementing complicated strategies. Therefore, it is important to identify suitable compression schemes so that at equilibrium each agent can make decisions based on a compressed version of their information instead of the full information. However, compression of information could result in loss of some or all equilibrium outcomes. This talk  presents results on this issue for a general class of multi-agent dynamic games, and designs and analyzes appropriate  information compression schemes. Our results highlight the tension among information compression, preservation of  equilibrium outcomes, and applicability of sequential decomposition algorithms to find compression-based equilibria. This is joint work with Dengwang Tang (University of California, Berkeley) and Demos Teneketzis (University of Michigan).

Biography
Vijay Subramanian received the Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign,  Champaign, IL, USA, in 1999. He worked at Motorola Inc., at the Hamilton Institute, Maynooth, Ireland, for many years, and in the EECS Department, Northwestern University, Evanston, IL, USA. In Fall 2014, he started in his current position as Associate Professor with the  EECS Department at the University of Michigan, Ann Arbor. His research interests are in stochastic analysis, random  graphs, multi-agent systems, and game theory (mechanism and information design) with applications to social, economic and technological networks.

Dr. Subramanian: https://subramanian.engin.umich.edu

On Zoom @ 4:10 p.m. on Friday, 4/29/22

Join Zoom Meeting
https://tamu.zoom.us/j/96343481647
Meeting ID: 963 4348 1647

Friday, April 22, 2022
4:10 – 5:00 p.m.
 
ETB 1020 – **In-person**
 
Takashi Tanaka
Assistant Professor, University of Texas at Austin
Department of Aerospace Engineering and Engineering Mechanics
 
Title: “Minimum-Information Kalman-Bucy Filtering and Fundamental Limitation of Continuous-Time Data Compression”

Talking Points:

• Event-based vs. frame-based cameras
• Information theory for networked control systems
• Causal source coding

Abstract
Motivated by a practical scenario where a continuous-time source signal is encoded, compressed, and transmitted to a remote user where the signal is reproduced in a real-time manner (e.g., streaming of neuromorphic camera data), we study the fundamental trade-off between the encoding data rate and the best achievable data quality (distortion). After briefly reviewing the “causal” rate-distortion theory in discrete-time, in this talk, we consider the problem of estimating a continuous-time Gauss-Markov source process observed through a vector Gaussian channel with an adjustable channel gain matrix. For a given (generally time-varying) channel gain matrix, we provide formulas to compute (i) the mean-square estimation error attainable by the classical Kalman-Bucy filter, and (ii) the mutual information between the source process and its Kalman-Bucy estimate. We then formulate a novel “optimal channel gain control problem” where the objective is to control the channel gain matrix strategically to minimize the weighted sum of these two performance metrics. To develop insights into the optimal solution, we first consider the problem of controlling a time-varying channel gain over a finite time interval. A necessary optimality condition is derived based on Pontryagin’s minimum principle. For a scalar system, we show that the optimal channel gain is a piece-wise constant signal with at most two discontinuities. We also consider the problem of designing the optimal time-invariant gain to minimize the average cost over an infinite time horizon. A novel semidefinite programming (SDP) heuristic is proposed to compute the optimal solution.

Biography
Takashi Tanaka is an Assistant Professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin since 2017. He received his B.S. degree from the University of Tokyo in 2006, M.S. and Ph.D. degrees from UIUC in 2009 and 2012, all in Aerospace Engineering. Prior to joining UT Austin, he held postdoctoral researcher positions at MIT and KTH Royal Institute of Technology. His research interest is broad in control, optimization, games, and information theory; most recently their applications to networked control systems, real-time data sharing, and strategic perception. He is the recipient of the DARPA Young Faculty Award, the AFOSR Young Investigator Program award, and the NSF Career award.

Personal Website: http://sites.utexas.edu/tanaka/

In-Person @ ETB 1020 @ 4:10 p.m. on Friday, 4/22/22

Friday, April 8, 2022
4:10 – 5:00 p.m.
 
ETB 1020 – **In-person**
 
Carmen Mota
Licensed Professional Counselor-Supervisor, Texas State Board of Examiners of Professional Counselors
Engineering Counselor for Texas A&M University
 
Talking Points:

• Recognizing Unhealthy Anxiety,
• Challenging Anxious Thinking, and
• Plan for Managing Anxiety

Title: “Your Anxiety Toolbox”

Abstract
The focus of this presentation is to increase our knowledge of anxiety, including anxiety symptoms and underlying thinking patterns. Attendees make an individualized Plan for Managing Anxiety.

Biography
Carmen Mota: I have been a counselor since 2011 and worked in the areas of developmental delays and mental health since 2002. Throughout this time, I have worked with people from diverse backgrounds and in every age group. I continue to learn from and be inspired by every person that I meet in the counseling relationship. Both the counselor and the client/student bring so much to the counseling relationship and form a therapeutic and collaborative relationship. I am trained in CBT (Cognitive Behavior Therapy), CPT (Cognitive Processing Therapy for trauma work), Sand tray, Play therapy, Filial therapy for parents, and have some limited training in Equine therapy. The approach I use with each client/student begins with Rogerian theory, as I believe “unconditional positive regard” is essential in creating a safe space to begin the counseling relationship. As a counselor, my goal is to help clients/students find their hope and self-compassion. I strive to assist clients/students in achieving their goals, both academic and personal.

I’m a Licensed Professional Counselor-Supervisor, Texas State Board of Examiners of Professional Counselors, No.65959. My education was at M.A., Counseling, Sam Houston State University and have a B.S., Psychology, Minor in Sociology, Texas A&M University

A large portion of my career has been educating people on mental health awareness. I believe I have a responsibility to challenge the stigmas that still exist in society and within families. And in my work, challenging the stigmas against mental health and seeking treatment, begins with individuals. When we accept ourselves, differences, struggles, and all, we can show the world that differences are okay. That diversity is not only okay, but important. Our uniqueness should be celebrated. I am a trained family facilitator with NAMI, (National Alliance on Mental Illness) and highly encourage their support groups and educational workshops as well. We cannot ignore the effects that social injustice and stigma has on us all. I began this work with educating myself and challenging my own biases and misconceptions. I am a life-long learner, of others, the world, and myself.

As a supervisor, I get to help shape future counselors. I believe it’s necessary for the interns I work with to have a well-rounded experience, including individual, couples, and group therapy, and crisis management. It is essential that counseling interns gain insight into themselves, including their own experiences and backgrounds and particular biases that may impact helping others. When beneficial, I encourage interns to begin working with a therapist themselves. Compassion for self and others is a necessary component in supervision.

Department Website: https://caps.tamu.edu/

In-Person @ ETB 1020 @ 4:10 p.m. on Friday, 4/8/22

Friday, April 1, 2022
4:10 – 5:00 p.m.
Zoom: https://tamu.zoom.us/j/96343481647
 
Dr. Siva Theja Maguluri
Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

Talking Points

• Lyapunov methods for Stochastic Approximation and Reinforcement learning
• Generalized Moreau Envelop based on infimal convolution smoothing as a Lyapunov function for Stochastic Approximation of contractive operators
• Unified framework to obtain sample complexity of a large class of RL algorithms
• Linear speedup in the number of agents for Federated Reinforcement learning

Title
“A Lyapunov Theory of Finite-Sample Guarantees of Stochastic Approximation and Reinforcement Learning”

Abstract
The focus of our work is to obtain finite-sample and/or finite-time convergence bounds of various model-free Reinforcement Learning (RL) algorithms. Many RL algorithms involve solving the Bellman fixed point equation, which is done using Stochastic Approximation (SA). SA is a popular approach for solving fixed point equations when the information is corrupted by noise. We develop a Lyapunov framework and obtain mean square error bounds on the convergence of a general class of SA algorithms for contractive operators under general norms and Markovian noise. The key tool we use is generalized Moreau envelope as a smooth potential/ Lyapunov function. These powerful results immediately provide sample complexity results of a large class of RL algorithms including TD learning, Q-learning, actor-critic algorithms, their off-policy variants, and their distributed variants. The talk will present a couple of these applications in off-policy RL and/or Federated RL.

Biography
Siva Theja Maguluri is Fouts Family Early Career Professor and Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He obtained his Ph.D. and MS in ECE as well as MS in Applied Math from UIUC, and B.Tech in Electrical Engineering from IIT Madras. His research interests span the areas of Control, Optimization, Algorithms and Applied Probability. In particular, he works on Reinforcement Learning theory, scheduling, resource allocation and revenue optimization problems that arise in a variety of systems including Data Centers, Cloud Computing, Wireless Networks, Block Chains, Ride hailing systems, etc. His research and teaching are recognized through several awards including the  “Best Publication in Applied Probability” award, NSF CAREER award, second place award at INFORMS JFIG best paper competition, Student best paper award at IFIP Performance, “CTL/BP Junior Faculty Teaching Excellence Award,” and “Student Recognition of Excellence in Teaching: Class of 1934 CIOS Award.”

Dr. Maguluri: https://sites.google.com/site/sivatheja/

On Zoom @ 4:10 p.m. on Friday, 4/1/22

Join Zoom Meeting
https://tamu.zoom.us/j/96343481647
Meeting ID: 963 4348 1647

Friday, March 4, 2022
4:10 – 5:00 p.m.
Zoom: https://tamu.zoom.us/j/96343481647
 
Dr. Tommaso Melodia
William L. Smith Professor, Northeastern University

Talking Points
– Open, Programmable, and Virtualized Wireless Architectures
– O-RAN architecture: Research Challenges
– Deep Reinforcement Learning for sequential decision making in the Open RAN
– Resources available for at scale prototyping, testing, data collection: PAWR, Colosseum

Title
“Toward AI-based Control and Orchestration in the Open RAN: Architectures, Algorithms, Testbeds”

Abstract
This talk will present an overview of our work on laying the basic principles to design open, programmable, AI-driven, and virtualized next-generation wireless networks. We will cover in detail challenges and opportunities associated with the evolution of cellular systems into cloud-native softwarized architectures enabling fine grained AI-based control of end-to-end functionalities on mobile devices, in the radio access network, and at the edge. We will also discuss existing testbeds and platforms available to the community for prototyping, experimentation, and data collection in virtualized and softwarized wireless systems.

Biography
Tommaso Melodia is the William Lincoln Smith Professor with the Department of Electrical and Computer Engineering at Northeastern University in Boston. He is also the Founding Director of the Institute for the Wireless Internet of Things and the Director of Research for the PAWR Project Office. He received his Laurea (integrated BS and MS) from the University of Rome – La Sapienza and his Ph.D. in Electrical and Computer Engineering from the Georgia Institute of Technology in 2007. He is an IEEE Fellow and recipient of the National Science Foundation CAREER award. Prof. Melodia is serving as Editor in Chief for Computer Networks, and has served as Associate Editor for IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, IEEE Transactions on Multimedia, among others. He was the Technical Program Committee Chair for IEEE Infocom 2018, and General Chair for ACM MobiHoc 2020, IEEE SECON 2019, ACM Nanocom 2019, and ACM WUWNet 2014. Prof. Melodia’s research on modeling, optimization, and experimental evaluation of wireless networked systems has been funded by US federal agencies and industry.

Dr. Melodia: https://ece.northeastern.edu/wineslab/tmelodia.php

On Zoom @ 4:10 p.m. on Friday, 3/4/22

Join Zoom Meeting
https://tamu.zoom.us/j/96343481647
Meeting ID: 963 4348 1647

Friday, February 25, 2022
4:10 – 5:00 p.m.
Zoom: https://tamu.zoom.us/j/96343481647
 
Dr. Adam Wierman
Professor of Computing and Mathematical Sciences and Director of Information Sciences and Technology, Caltech

Title
“Online Optimization and Control using Black-Box Predictions”

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.

For more on Dr. Wierman see https://adamwierman.com.

On Zoom @ 4:10 p.m. on Friday, 2/25/22

Join Zoom Meeting
https://tamu.zoom.us/j/96343481647
Meeting ID: 963 4348 1647