Chris Kennedy was a BIDS - Biomedical Big Data Training (BBDT) Data Science Fellow and a PhD student in biostatistics at UC Berkeley, where he worked with Alan Hubbard. He was also a D-Lab instructor and consultant, and an NIH biomedical big data trainee. His methodological interests encompassed targeted machine learning, randomized trials, causal inference, deep learning, text analysis, signal processing, and computer vision. His applications were primarily in precision medicine, public health, genomics, and election campaigns. His software projects included 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 Haveman is a Professor of Sociology and Business at UC Berkeley. She holds a BA in history and an MBA (from the University of Toronto), and a Ph.D. in organizational behavior and industrial relations (from UC Berkeley). Following positions at Duke University's Fuqua School of Business, Cornell University's Johnson Graduate School of Management, and Columbia University's Graduate School of Business, Professor Haveman joined UC Berkeley in July 2006. Her research interests include how organizations, the fields in which they are embedded, and the careers of their members and employees evolve. Her current work involves American magazines and wineries, Chinese listed firms, and the emerging marijuana market in several US states.