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Colloquium on Digital Transformation Science

  • June 24September 16, 3 pm CT

    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

    Causal Tensor Estimation

    Devavrat Shah, Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology


    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 and 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 vehiclesIn this talk, we present a framework for causal inference for the “panel” or “longitudinal” setting from the lens of tensor estimation. Traditionally, such panel or longitudinal settings are considered in econometrics literature for program or policy evaluation. Tensor estimation has been considered in machine learning, where tantalizing statistical and computational tradeoffs have emerged for random observation models. We introduce a causal variant of tensor estimation that provides a unified view for prior works in econometrics and provides newer avenues to explore. We discuss a method for estimating such a causal variant of the tensor and various exciting directions for future research, including offline reinforcement learning. This is based on joint work with Alberto Abadie (MIT), Anish Agarwal (MIT), and Dennis Shen (MIT/UC Berkeley).

    Devavrat Shah is a professor in the Department of Electrical Engineering and Computer Science at MIT. His current research interests are at the interface of statistical inference and social data processing. His work has been recognized through prize paper awards in machine learning, operations research, and computer science, as well as career prizes including the 2010 Erlang prize from the INFORMS Applied Probability Society and the 2008 ACM Sigmetrics Rising Star Award. He is a distinguished alumnus of his alma mater IIT Bombay. He co-founded Celect, Inc. (now part of Nike) in 2013 to help retailers decide what to put where by accurately predicting demand using omni-channel data.

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