The Colloquium on Digital Transformation is a series of weekly online talks on how artificial intelligence, machine learning, and big data can lead to scientific breakthroughs with large-scale societal benefit.
Summer 2021 Series
June 10, 2021, 1 pm PT/4 pm ET
Security of Cyberphysical Systems
P.R. Kumar, Professor of Electrical & Computer Engineering and Industrial & Systems Engineering
The coming decades may see large-scale deployment of networked cyber-physical systems to address global needs in areas such as energy, water, health care, and transportation. However, as recent events have shown, such systems are vulnerable to cyber attacks. We begin by revisiting classical linear systems theory, developed in more innocent times, from a security-conscious, even paranoid, viewpoint. Then we present a general technique, called “dynamic watermarking,” for detecting any sort of malicious activity in networked systems of sensors and actuators. We then present a field test experimental demonstration of this technique on an automobile on a test track, a process control system, a simulation study of defense against an attack on Automatic Gain Control (AGC) in a synthetic power system, and an emulated attack on a solar powered home. This is joint work with Bharadwaj Satchidanandan, Jaewon Kim, Woo Hyun Ko, Tong Huang, Lantian Shangguan, Kenny Chour, Jorge Ramos, Prasad Enjeti, Le Xie, and Swaminathan Gopalswamy.
P.R. Kumar is a Professor of Electrical & Computer Engineering and Industrial & Systems Engineering at Texas A&M University. Prior to that, he served in the Department of Mathematics at the University of Maryland, Baltimore County (1977-84) and the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign (1985-2011). His current focus includes Machine Learning (ML), Cyber-Physical Systems (CPS), security, privacy, UTM, 5G, wireless networks, and power systems. He is a member of the U.S. National Academy of Engineering, the World Academy of Sciences, and Indian National Academy of Engineering. Honors include a Doctor Honoris Causa by ETH, the IEEE Field Award for Control Systems, the Eckman Award of AACC, the Ellersick Prize of IEEE ComSoc, the Outstanding Contribution Award of ACM SIGMOBILE, the Infocom Achievement Award, and the SIGMOBILE Test-of-Time Paper Award. He is a Fellow of IEEE and ACM.
June 17, 2021, 1 pm PT/4 pm ET
Data-Driven Coordination of Distributed Energy Resources
Alejandro D. Dominguez-Garcia, Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
The integration of distributed energy resources (DERs) such as rooftop photovoltaics installations, electric energy storage devices, and flexible loads, is becoming prevalent. This integration poses numerous operational challenges on the lower-voltage systems to which DERs are connected, but also creates new opportunities for provision of grid services. In the first part of the talk, we discuss one such operational challenge—ensuring proper voltage regulation in the distribution network to which DERs are connected. To address this problem, we propose a Volt/VAR control architecture that relies on the proper coordination of conventional voltage regulation devices (e.g., tap changing under load (TCUL) transformers and switched capacitors) and DERs with reactive power provision capability. In the second part of the talk, we discuss one such opportunity—utilizing DERs to provide regulation services to the bulk power grid. To leverage this opportunity, we propose a scheme for coordinating the response of the DERs so that the power injected into the distribution network (to which the DERs are connected) follows some regulation signal provided by the bulk power system operator. Throughout the talk, we assume limited knowledge of the particular power system models and develop data-driven methods to learn them. We then utilize these models to design appropriate controls for determining the set-points of DERs (and other assets such as TCULs) in an optimal or nearly-optimal fashion.
Alejandro D. Dominguez-Garcia is a Professor, William L Everitt Scholar, and Grainger Associate in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. His research program aims to develop technologies for providing a reliable and efficient supply of electricity—a key to ensuring societal welfare and sustainable economic growth. He received the NSF CAREER Award in 2010, and the Young Engineer Award from the IEEE Power and Energy Society in 2012. He was selected by the UIUC Provost to receive a Distinguished Promotion Award in 2014, and he received the UIUC College of Engineering Dean’s Award for Excellence in Research in 2015.
June 24, 2021, 1 pm PT/4 pm ET
Closing the Loop on Machine Learning: Data Markets, Domain Expertise, and Human Behavior
Roy Dong, Research Assistant Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
As machine learning and data analytics are increasingly deployed in practice, it becomes more and more pressing to consider the ecosystem created by such methods. In recent years, issues of data provenance, the veracity of available data, vulnerabilities to data manipulation, and human perceptions/behavior have had a growing effect on the overall performance of our intelligent systems. In the first part of this talk, I consider a game-theoretic model for data markets, and demonstrate that whenever multiple data purchasers compete for data sources without exclusivity contracts, there is a fundamental degeneracy in the equilibria, independent of each data purchaser’s learning capabilities. In the second part of this talk, we discuss issues of causal inference, which are essential when our learning algorithms are used to make decisions. We analyze how passively observed data can be efficiently combined with actively collected trial data to most efficiently recover causal structures. In the last section of this talk, I will discuss some of our recent experiments with human participants in the context of intelligent building control, and show that commonly designed mechanisms assuming utility-maximizing behavior may fall short of theoretical performance in practice.
Roy Dong is a Research Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He received a BS Honors in Computer Engineering and a BS Honors in Economics from Michigan State University in 2010 and a PhD in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2017, where he was funded in part by the NSF Graduate Research Fellowship. From 2017 to 2018, he was a postdoctoral researcher in the Berkeley Energy & Climate Institute (BECI) and a visiting lecturer in the Department of Industrial Engineering and Operations Research at UC Berkeley. His research uses tools from control theory, economics, statistics, and optimization to understand the closed-loop effects of machine learning, with applications in cyber-physical systems such as the smart grid, modern transportation networks, and autonomous vehicles.
Watch for the announcement of the C3.ai DTI Fall 2021 Colloquium Series