Chris Kennedy is a BIDS Data Science Fellow and a PhD student in biostatistics, where he works with Alan Hubbard. He is also a D-Lab instructor and consultant, and an NIH biomedical big data trainee. His methodological interests encompass targeted machine learning, randomized trials, causal inference, deep learning, text analysis, signal processing, and computer vision. His applications are primarily in precision medicine, public health, genomics, and election campaigns. His software projects include the SuperLearner ensemble learning system and varImpact for variable importance estimation; he leverages high performance computing on Savio and XSEDE clusters to accelerate his work.
Prior to Berkeley he worked in political analytics in DC, running dozens of randomized trials and integrating machine learning into multi-million dollar programs to improve voter turnout for underrepresented Americans. He has also worked to support climate change action through Al Gore’s Climate Reality Project and the Yale Program on Climate Change Communication. He holds an M.A. in political science from UC Berkeley, an M.P.Aff. from the LBJ School of Public Affairs, and a B.A. in government & economics from The University of Texas at Austin.
Heather A. Haveman is a Professor of Sociology and Business in the Department of Sociology, and a Professor of Sociology and Management at the Haas School of Business, UC Berkeley. Her current work involves American magazines, American wineries, Chinese listed firms, the emerging marijuana market in several American states, American law professors, and American wineries.