Jennifer Chayes is Associate Provost of Data Science and Information, and Dean of the School of Information, at UC Berkeley. She is Professor of EECS, Mathematics, Statistics, and the School of Information. Before joining Berkeley, she was at Microsoft for over 20 years, where she was Technical Fellow, and founder and managing director of three interdisciplinary labs: Microsoft Research New England, New York City, and Montreal. Chayes has received numerous awards for both leadership and scientific contributions, including the Anita Borg Institute Women of Vision Leadership Award, the John von Neumann Award of the Society for Industrial and Applied Mathematics, and an honorary doctorate from Leiden University. She is a member of the American Academy of Arts and Sciences and the National Academy of Sciences.
Chayes’ research areas include phase transitions in computer science, and structural and dynamical properties of networks including modeling and graph algorithms. Chayes is one of the inventors of the field of graphons, which are widely used for the machine learning of large-scale networks. Her recent work focuses on machine learning, including both theory and applications in cancer immunotherapy, ethical decision making, and climate change.
June 23, 2020 05:00 PM PT
Dr. Phillip Atiba Goff - Racism and Policing: The Path Forward (Center for Policing Equity) - 7:50
Jennifer Chayes - Rapid Response Research for COVID-19 (UC Berkeley) - 29:52
Nate Silver - The Signal and the Noise: the Big Lessons from 20 years of Data Analysis (FiveThirtyEight.com) - 51:07
Racism and Policing: The Path Forward
Dr. Phillip Atiba Goff
Dr. Goff conducts work exploring the ways in which racial prejudice is not a necessary precondition for racial discrimination. That is, despite the normative view of racial discrimination—that it stems from prejudiced explicit or implicit attitudes—his research demonstrates that situational factors facilitate racially unequal outcomes.
Dr. Goff’s model of evidence-based approaches to justice has been supported by the National Science Foundation, Department of Justice, Russell Sage Foundation, W.K. Kellogg Foundation, Open Society Foundations, Open Society Institute-Baltimore, Atlantic Philanthropies, William T. Grant Foundation, the COPS Office, the Major Cities Chiefs Association, the NAACP LDF, NIMH, SPSSI, the Woodrow Wilson Foundation, the Ford Foundation, and the Mellon Foundation among others. Dr. Goff was a witness for the President’s Task Force on 21st Century Policing and has presented before Members of Congress and Congressional Panels, Senate Press Briefings, and White House Advisory Councils.
Rapid Response Research for COVID-19 and Other Challenges: Machine Learning and Data Science at Cal
Prof. Jennifer Chayes
The Division of Computing, Data Science, and Society (CDSS) at UC Berkeley is advancing foundational research and educating the next generation of scientists and practitioners to leverage computing and data to take on pressing societal problems. In recent history, no societal challenge has been as far-reaching and critical as the COVID-19 pandemic. Solutions to this complex, global challenge will stress many aspects of computing and data science, from analysis of sparse, biased, and variable data; to simulation of large networks of human interaction; to sifting through biological and chemical data to find treatments and vaccines; to influencing both policy makers and public opinion more broadly.
In this talk, I will describe the overall vision of CDSS and how it is transforming education and research at UC Berkeley, building bridges across a diverse set of programs, and disrupting the traditional siloed university structure. The emergence of the COVID-19 pandemic has accelerated ramp-up of this new Division and the interdisciplinary research and collaboration it fosters. It also has highlighted the importance of delivering inclusive, rigorous data science education at scale, a hallmark of the Berkeley program. I will draw on examples from across campus of how computing and data are being used to address the pandemic, and how these challenges will stress the scale, performance, privacy, and resilience of the underlying data systems, driving a next generation of requirements for systems like Spark.
The Signal and the Noise: the Big Lessons from 20 Years of Data Analysis
In this technical keynote, Nate will highlight his biggest lessons from the past 20 years of data analysis and how it correlates to his methodology of building the election model and challenges in forecasting.