Nick Adams, PhD, is a sociologist and full-time research fellow at the Berkeley Institute for Data Science (BIDS). His substantive work analyzes protester and police interactions as revealed through 8,000 news accounts of nearly 200 US Occupy campaigns. His TextThresher software provides the human-powered machinery to process these data in high quantity with high quality. A builder of research communities across UC Berkeley's campus, Nick founded and leads the Computational Text Analysis Working Group at Berkeley’s D-Lab and BIDS' Text Across Domains (Text XD) initiative. He also serves on the Social Science Research Council’s Committee on Digital Culture and is a contributing editor to Mobilizing Ideas, the online journal of social movements research.
Marla Stuart is a fellow with the Guizhou Berkeley Big Data Innovation Research Center (GBIC), a research hub based in Guizhou Province, China that is dedicated to improving the health and well-being of China’s population. GBIC focuses on developing actionable programmatic and policy recommendations for consideration by government agencies. Marla leads the GBIC computational lab which collects, wrangles and models data from government bureaus and other sources to support the research goals of agency partners and GBIC faculty. Her own research concentrates on understanding the applicability of data science approaches in social welfare research and practice settings.
Marla spent twenty years conducting practice-based research in public and private organizations that provide health and human services in vulnerable communities. This included fifteen years on the Navajo Nation in Arizona where she worked with local communities to develop health and social services evaluation approaches derived from traditional Navajo philosophy and values.
Marla earned her Masters of Social Work from the University of Washington in Seattle with a focus on planned social change. She received her PhD from the School of Social Welfare at Berkeley. Her dissertation explored government efforts to scale the use of evidence-based services. It used public government records and crowd-sourced and computational data-extraction methods to create measures of these strategies. It assessed the relative effects of these public strategies on scaling progress using time-to-event analysis. It found that county governments are well positioned to implement scaling strategies and that the proportion of social service providers adopting evidence-informed services can be increased as can the proportion of county funding directed to these organizations. This study design is highly replicable and as such provides a general model to apply to other local environments to identify common county levers that effectively promote the scaling of evidence-informed social services.
Elena Glassman is an EECS postdoctoral researcher at the Berkeley Institute of Design, advised by Bjoern Hartmann. She earned her EECS PhD at MIT CSAIL in August 2016, where she created scalable systems that analyze, visualize, and provide insight into the code of thousands of programming students. Prior to entering the field of human-computer interaction, she earned her M.Eng. in the MIT CSAIL Robot Locomotion Group. She has been a visiting researcher at the Stanford Biomimetics and Dextrous Manipulation Lab and a summer research intern at both Google and Microsoft Research, working on systems that help people teach and learn. Before receiving the BIDS Moore/Sloan Data Science Fellowship, she was awarded the Intel Foundation Young Scientist Award, both the NSF and NDSEG graduate fellowships, the MIT EECS Oral Master’s Thesis Presentation Award, a Best of CHI Honorable Mention, and the MIT Amar Bose Teaching Fellowship for innovation in teaching methods.
Chris is a PhD student in biostatistics where he works with Alan Hubbard, and is an independent data science consultant. He is also a D-Lab instructor and consultant, a biomedical big data fellow, and an Open Insulin researcher.
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.