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He 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.
Eleanor A. Blakely, is a Lawrence Berkeley National Laboratory (LBNL) Senior Staff Biophysicist with 45 y of professional experience in molecular, cellular and animal radiobiological research directed at studying the basic mechanisms of radiation responses, with an emphasis on charged particle radiation effects. She is also a Clinical Professor of Radiation Medicine (nontenured) at Loma Linda University, School of Medicine, Loma Linda, California. Dr. Blakely earned a PhD in Physiology from the University of Illinois at Champaign-Urbana as a U.S. Atomic Energy Commission Special Fellow in Radiation Science and Protection.
Her professional activities have included service on advisory panels for several hospitals, universities, and numerous federal agencies including the U.S. Department of Energy (DOE), the National Institutes of Health (NIH), and the National Aeronautics and Space Administration (NASA); on Editorial Boards for several journals: Space Power, Radiation Research, Journal of Radiation Research, and NPJ Microgravity; Appointed Member, Diagnostic Radiology Study Section-Division of Research Grants, NIH; Advisory Committee Member, International Atomic Energy Agency; Scientific Director, NASA Space Research Summer School; and Elected Officer of the Radiation Research Society: Biology Councilor and Secretary-Treasurer.
In 2000 she was elected to NCRP, and has served on Scientific Committee (SC) 75 that produced NCRP Report No. 132, Radiation Protection Guidance for Activities in Low-Earth Orbit; and SC 1-7 that produced NCRP Report No. 153, Information Needed to Make Radiation Protection Recommendations for Space Missions Beyond Low-Earth Orbit. She has received several awards including the Robert Emerson Graduate Teaching Award, School of Life Sciences, University of Illinois, the Lawrence Berkeley Laboratory Outstanding Performance Award, the DOE Office of Science Outstanding Mentor Award, the Lawrence Berkeley Laboratory Technology Transfer Award, and an RD100 award from Research and Development Magazine.
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.