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Steve is Director of the Applied Energy Division at SLAC National Accelerator Laboratory. The Applied Energy Division conducts research on the electric grid, batteries, photovoltaics, and advanced manufacturing. The Applied Energy Division is part of the Energy Sciences Directorate, which conducts research in chemistry, materials, computer science, and applied energy. SLAC is operated by Stanford University for the U.S. Department of Energy's Office of Science. Previously, Steve developed and managed research programs at Stanford University in artificial intelligence, computer science, energy, and sustainability. Steve helped to create new programs at Stanford such as the Institute for Human-Centered AI, SAIL-Toyota Center for AI Research, Stanford Data Science Initiative, Bay Area PV Consortium, and Energy and Environment Affiliates Program. Prior to joining Stanford, Steve was president and CEO of solar energy company Cyrium Technologies, consultant for the National Renewable Energy Lab and US Department of Energy, venture capitalist at Worldview Technology Partners, vice president at SDL (JDSU), and member of the technical staff at MIT Lincoln Laboratory. Steve received a PhD and MS from Stanford and BS from UC Berkeley, all in electrical engineering. Steve is a Fellow of the SPIE, a former Board member of the MRS, and a former utilities commissioner for the City of Palo Alto.
Daniel is Head of the Machine Learning (ML) Initiative at SLAC National Accelerator Laboratory. The ML initiative coordinates the application of ML techniques across the range of science at the lab, with special focus in autonomous facility and experimental control, edge-ML, sparse and irregular data sets, prognostics, and new approaches to data analysis. Previously, Daniel led the Accelerator Directorate’s ML department, as well as working in the Linac Coherent Light Source Laser Division. Prior to joining SLAC, Daniel worked as a conservation scientist at the Museum of Modern Art in New York, and as a data analyst for WhenU.com. Daniel received his PhD in Applied Physics from Stanford, and his AB in Physics from Harvard.
Arvind Ramanathan is a computational biologist in the Data Science and Learning Division at Argonne National Laboratory and a senior scientist at the University of Chicago Consortium for Advanced Science and Engineering (CASE). His research interests are at the intersection of data science, high performance computing and biological/biomedical sciences.
His research focuses on three areas focusing on scalable statistical inference techniques: (1) for analysis and development of adaptive multi-scale molecular simulations for studying complex biological phenomena (such as how intrinsically disordered proteins self assemble, or how small molecules modulate disordered protein ensembles), (2) to integrate complex data for public health dynamics, and (3) for guiding design of CRISPR-Cas9 probes to modify microbial function(s).
He has published over 30 papers, and his work has been highlighted in the popular media, including NPR and NBC News. He obtained his Ph.D. in computational biology from Carnegie Mellon University, and was the team lead for integrative systems biology team within the Computational Science, Engineering and Division at Oak Ridge National Laboratory. More information about his group and research interests can be found at http://ramanathanlab.org.