David Mongeau is the Executive Director of BIDS, an early influencer and leader among data science institutes across the globe, both in research and training. He is honored to work with the diverse BIDS faculty, researchers, students, and staff – together, developing talent, theory and tools for data-driven discovery that benefits all.
David became Executive Director in April 2018. Before joining the data science community at Berkeley, he co-led the data analytics institute at Ohio State; worked for a few years at Battelle, where he championed the original proposal for the Columbus Collaboratory, a new venture in advanced analytics and cybersecurity formed by seven corporations; and worked for many years at Bell Labs – starting on the team that introduced the first C++ compiler and commercial release of Unix System V, and leaving after building a team that delivers network technology and business consulting in Asia and Europe.
He earned his undergraduate degree at Carnegie Mellon, and later earned a graduate degree at Rensselaer Polytechnic Institute and an MBA from Purdue University. Many of his interests embrace the humanities and arts, along with STEM. He will organize Cordial Poetry in 2019, an open source poetry event at Berkeley. Please contact him if you’re interested in more details.
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.
Maryam Vareth leads BIDS’ data science research in the Health & Life Sciences. She is also a Co-Director of the Innovate For Health initiative, a collaboration among UC Berkeley, UCSF, and the Janssen Pharmaceutical Companies of Johnson & Johnson. As an experienced researcher, engineer, and data scientist, she applies mathematics, statistics and physics to solve unmet needs in healthcare and to enhance patients’ experience during their medical journey. She is an advocate for “data-driven” medicine, and in particular for linking large-scale medical imaging data with medical diagnostics and therapeutics to extract clinically-relevant insights through the use of open source and open research practices.
Dr. Vareth received her BS and MS training in Electrical Engineering and Computer Science (EECS) from UC Berkeley, where she was awarded the prestigious Regent’s and Chancellor’s Scholarship. She completed her PhD through the joint UC Berkeley-UCSF Bioengineering program as a National Science Foundation Fellow, where she was awarded the Margaret Hart Surbeck Endowed Fellowship for Interdisciplinary Research for her work on developing new techniques and algorithms for the acquisition, reconstruction and quantitative analysis of Magnetic Resonance Spectroscopy Imaging (MRSI), with the goal of improving its speed, sensitivity and specificity to improve the management of patients with brain tumors. She conducted her post-doctoral fellowship at UCSF, combining structural, physiological and metabolic imaging data from large clinical trials to quantitatively characterize heterogeneity within malignant brain tumors.