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Ben Brown is a statistical scientist in the Environmental Genomics and Systems Biology division within Berkeley Lab’s Biosciences Area. He specializes in the development of novel machine algorithms, usually for the biological and environmental sciences at Berkeley Lab. His group develops “third-wave” learning algorithms that combine the interpretability and reliability of classical statistics with the predictive performance of deep learning. They specialize in designing learning paradigms for complex, high-dimensional systems that enable accurate uncertainty quantification, model discovery, feature selection, and inference. Dr. Brown's expertise include statistics, machine learning, deep learning, and artificial intelligence.
COVID-19-related research: "Using Machine Learning to Estimate COVID-19's Seasonal Cycle". Other principal investigators include: Eoin Brodie, Nicola Falco, Dan Feldman, Zhao Hao, Chaincy Kuo, Joshua Ladau, and Haruko Wainwright.
She leads the Data Science Engagement Group at the National Energy Research Scientific Computing Center (NERSC) at Berkeley National Lab. A native of the U.K., her career spans research in particle physics, cosmology, and computing on both sides of the Atlantic. She obtained her Ph.D. at Edinburgh University, and worked at Imperial College London and SLAC National Accelerator Laboratory before joining NERSC. Her group leads the support of supercomputing for experimental science, and her work focuses on data-intensive computing and research. This includes using high-performance computing (HPC) to scale up machine-learning algorithms that can tackle new, larger scientific problems; and leveraging artificial intelligence to gain insight from cosmological data.
He is a computational biologist staff scientist in the Biological Systems and Engineering Division within Berkeley Lab’s Biosciences Area. The goal of his research is to understand the functional organization and dynamic coordination of sensorimotor networks underlying learned, skilled behaviors. His lab’s research will address the question of how distributed neural circuits gives rise to coordinated behaviors. To address this question, his lab combines multi-scale electrophysiology with optical manipulations in reaching rodents and advanced computational methods to investigate the functional organization and dynamic coordination of sensorimotor networks. He also develop software to analyze this data, including novel machine learning algorithms and deep learning approaches.
Thomas is the Director of Energy Analysis and Environmental Impacts for Berkeley Lab’s Energy Technologies Area as well as the Head of the Sustainable Energy and Environmental Systems department. His expertise lies in developing air pollution sensors and evaluating impacts of emission treatment and control technologies. He serves as the editor of the Aerosol Science and Technology journal.
COVID-19-related research: "New Research Launched on Airborne Virus Transmission in Buildings"
Dr. Agarwal's research focuses on scientific tools that enable sharing of scientific experiments, advanced networking infrastructure to support sharing of scientific data, data analysis support infrastructure for eco-science, and cybersecurity infrastructure to secure collaborative environments. Dr. Agarwal is the coordinator for ML4Sci, the Lab-wide machine learning initiative. Dr. Agarwal is a Senior Fellow at the Berkeley Institute for Data Science and an Inria International Chair. Dr. Agarwal also leads teams developing data server infrastructure to significantly enhance data browsing and analysis capabilities and enable eco-science synthesis at the watershed-scale to understand hydrologic and conservation questions and at the global-scale to understand carbon flux. Some of the projects Dr. Agarwal is working on include: Enviromental Systems Science Digital Infrastructure for a Virtual Ecosystem (ESS-DIVE), Watershed Function SFA, AmeriFlux Management Project, FLUXNET, NGEE Tropics, and International Soil Carbon Network. Dr. Agarwal received her Ph.D. in electrical and computer engineering from University of California, Santa Barbara and a B.S. in Mechanical Engineering from Purdue University.
Nugent is the Department Head for Computational Science and the Division Deputy for Scientific Engagement in the Computational Research Division. Nugent attended Bowdoin College and received his M.S. and Ph.D. in physics with a concentration in astronomy from the University of Oklahoma. He joined LBNL in 1996 as a postdoctoral fellow working with Saul Perlmutter on the measurement of the accelerating universe with Type Ia Supernova, for which Dr. Perlmutter received the Nobel Prize in Physics in 2011. In 2008, he co-founded the Computational Cosmology Center and became their first Group Leader. He was promoted to Senior Staff Scientist at LBNL in 2010 and the same year joined the faculty in the Astronomy Department at UC Berkeley.
Nugent is an author on over 300 refereed publications and has received numerous awards in his career including LBNL's Director’s Award for Exceptional Scientific Achievement and NERSC's Award for Innovative Use of High-Performance Computing in 2013, SuperComputing's 2009 Storage Challenge Award, the 2007 Gruber Prize in Cosmology and the 2015 Breakthrough Prize in Physics. Nugent has presented his work as a participant on PBS News Hour, NASA's Space Science Update program, CNN, NOVA, NPR, and the BBC. His work has been featured in Time Magazine, Newsweek, Science, and Nature.