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• Colloquium on Digital Transformation Science

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  August 1320, 3 pm CT Predictive and Prescriptive Analytics for the Lessons from COVID-19 Pandemic : Efficiency vs. Resilience Moshe Y. Vardi, University Professor, Karen Ostrum George Distinguished Service Professor in Computational Engineering, Rice UniversityDimitris Bertsimas, Boeing Professor of Operations Research and Associate Dean of Business Analytics, MIT REGISTER FOR ZOOM WEBINARThe In both computer science and economics, efficiency is a cherished property. In computer science, the field of algorithms is almost solely focused on their efficiency. In economics, the main advantage of the free market is that it promises "economic efficiency." A major lesson from COVID-19 is that both fields have over-emphasized efficiency and under-emphasized resilience. Professor Vardi argues that resilience is a more important property than efficiency and discusses how the two fields can broaden their focus to make resilience a primary consideration. He will include a technical example, showing how we can shift the focus in formal reasoning from efficiency to resilience. Moshe Y. Vardi is a University Professor and the Karen Ostrum George Distinguished Service Professor in Computational Engineering at Rice University. He is the recipient of three IBM Outstanding Innovation Awards, the ACM SIGACT Goedel Prize, the ACM Kanellakis Award, the ACM SIGMOD Codd Award, the Blaise Pascal Medal, the IEEE Computer Society Goode Award, the EATCS Distinguished Achievements Award, the Southeastern Universities Research Association's Distinguished Scientist Award, and the ACM SIGLOG Church Award. He is the author and co-author of over 600 papers and the books Reasoning about Knowledge and Finite Model Theory and Its Applications. He is a Fellow of the American Association for the Advancement of Science, the American Mathematical Society, the Association for Computing Machinery, the American Association for Artificial Intelligence, the European Association for Theoretical Computer Science, the Institute for Electrical and Electronic Engineers, and the Society for Industrial and Applied Mathematics. He is a member of the US National Academy of Engineering and National Academy of Science, the American Academy of Arts and Science, the European Academy of Science, and Academia Europaea. He holds six honorary doctorates. He is currently a Senior Editor of the Communications of the ACM, after having served for a decade as its Editor-in-Chief COVID-19 pandemic creates unprecedented challenges for healthcare providers and policy makers. How to triage patients when healthcare resources are limited? Whom to test? And how to design social distancing policies to contain the disease and its socioeconomic impact? Dimitris Bertsimas and Alexandre Jacquillat of MIT Sloan School of Management believe that analytics can provide an answer and have collected data from clinical studies, case counts, and hospital collaborations at www.covidanalytics.io. This colloquium will present their epidemiological model of the disease’s dynamics, a machine-learning model of mortality risk, and a resource allocation model. It will address: How can we predict admissions in intensive care units using machine learning? How does COVID-19 impact different demographic and socioeconomic populations? How does mobility impact the disease’s spread? How to optimize social distancing policies? How to augment COVID-19 tests with data-driven warnings that identify high-risk subjects? Bertsimas will present a new machine learning model for predicting being COVID-positive (https://covidanalytics.io/infection_calculator) and mortality (https://covidanalytics.io/mortality_calculator) using data from over 40 hospitals around the world, along with high-performance computing (using the C3 AI Suite), and advanced machine learning and artificial intelligence. He will summarize his research group’s end-to-end ML/AI methods, spanning epidemiological modeling (to model the disease’s spread), machine learning (to predict ICU admissions and test results), causal inference (to investigate disparities across populations), and optimal control (to support social distancing guidelines), as well as a new optimization model for allocating vaccines to minimize deaths.

  
  

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Have Questions? Please contact one of us:

• Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
• R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
• Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)