Announcements

• Colloquium on Digital Transformation Science

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  August 27September 24, 3 pm CT Targeted Dynamic Interventions in Networked Epidemic Models Towards AI for Healthcare with Applications to the COVID-19 Pandemic Sanmi Koyejo, Assistant Professor of Computer Science, University of Illinois at Urbana-ChampaignAsuman Ozdaglar, MathWorks Professor of Electrical Engineering and Computer Science, MIT Francesca Parise, Assistant Professor of Electrical and Computer Engineering, Cornell University REGISTER FOR ZOOM WEBINAR Epidemic spread models are playing an increasingly central role for understanding and policy making in the context of With the emergence of public healthcare crises such as the COVID-19 pandemic. Many of these models consider homogeneous populations, thus failing to capture rich heterogeneities in terms of risk factors, vulnerabilities, economic participation, location, and social interactions. In this talk, we will present networked SIR models that capture groups of agents with different characteristics and interaction patterns. We will then discuss targeted dynamic interventions in terms of testing and lockdown policies that minimize spread of infection while also containing social and economic damages. Our focus will be on dynamic time-varying policies that adaptively adjust as a function of the infection level in the community. Asuman Ozdaglar received the B.S. degree from the Middle East Technical University (1996), and the S.M. (1998) and Ph.D. (2003) degrees from the Massachusetts Institute of Technology. She’s the Mathworks Professor of Electrical Engineering and Computer Science in the EECS Department at the MIT. She’s the Department Head of EECS and the Deputy Dean of Academics in the Schwarzman College of Computing. Her research expertise includes optimization theory, distributed optimization and control, and network analysis. Her awards include a Microsoft fellowship, NSF Career award, 2008 Donald P. Eckman award of the American Automatic Control Council, Class of 1943 Career Development Chair, inaugural Steven and Renee Innovation Fellowship, and 2014 Spira teaching award. Pandemic, it is increasingly evident that AI is among the most powerful tools for addressing early detection of diseases, triage, treatment planning, and patient management, among many other pressing healthcare problems. What will it take to build effective machine learning systems for healthcare? This talk will outline our research progress towards answering this question. To this end, I will present emerging technical advances in federated learning, modeling, evaluation, privacy, and trustworthiness. I will also outline how we are bringing these tools to bear to aid in analyzing medical images from COVID-19 patients. Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to neuroscience and biomedical imaging. Koyejo has been the recipient of several awards including a Best Paper award from the Conference on Uncertainty in Artificial Intelligence (UAI), a Kavli Fellowship, an IJCAI Early Career Spotlight, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo serves on the board of the Black in AI organizationFrancesca Parise joined the School of Electrical and Computer Engineering at Cornell University as an assistant professor in July 2020. Before then, she was a postdoctoral researcher at the Laboratory for Information and Decision Systems at MIT. She defended her PhD at the Automatic Control Laboratory, ETH Zurich, Switzerland in 2016 and she received the B.Sc. and M.Sc. degrees in Information and Automation Engineering in 2010 and 2012, from the University of Padova, Italy, where she simultaneously attended the Galilean School of Excellence. Francesca was recognized as an EECS rising star in 2017 and is the recipient of the Guglielmo Marin Award from the “Istituto Veneto di Scienze, Lettere ed Arti,” the SNSF Early Postdoc Fellowship, the SNSF Advanced Postdoc Fellowship, and the ETH Medal for her doctoral work.

  
  

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