Spark + AI Summit 2020 NA - Wednesday Afternoon Keynotes

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
Nate Silver

Data warehouses have a long history in decision support and business intelligence applications. But, data warehouses were not well suited to dealing with the unstructured, semi-structured, and streaming data common in modern enterprises. This led to organizations building data lakes of raw data about a decade ago. But, they also lacked important capabilities. The need for a better solution has given rise to the data lakehouse, which implements similar data structures and data management features to those in a data warehouse, directly on the kind of low cost storage used for data lakes.

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