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Colloquium on Digital Transformation Science

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  June 24December 2, 3 pm CT Closing the Loop on Machine Learning: Data Markets, Domain Expertise, and Human Behavior Quantifying Carbon Credit over U.S. Midwestern Cropland Using AI-Based Data-Model Fusion Kaiyu Guan, Blue Waters Associate Professor of Natural Resources and Environmental SciencesRoy Dong, Research Assistant Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign REGISTER FOR ZOOM WEBINAR 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. In this talk, we will first provide an overview of the background on agriculture carbon credit, and then focus on the quantification of field-level carbon credit, including the issues in the conventional methods, and our proposed “System-of-Systems” solution that leverages various sources of sensing data, process-based modeling, and AI-based Model-Data Fusion methods. to achieve field-level, accurate, scalable and cost-effective quantification. Kaiyu Guan is the Blue Waters Professor 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 vehicles.got his PhD at Princeton University and was a postdoctoral scholar at Stanford University. His research group uses satellite data, computational models, field work, and AI to address how climate and human practices affect crop productivity, water resource availability, and ecosystem functioning. He has published over 100 papers in leading scientific journals and leads over 15 federal grants from NASA, NSF, DOE, and USDA. He is the recipient of an NSF CAREER Award, the NASA New Investigator Award, the AGU Early Career Award in Global Environmental Change, and was a Blavatnik National Award for Young Scientists Finalist.

  
  

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