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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.
Lawrence Berkeley National Laboratory (Berkeley Lab), a U. S. Department of Energy Office of Science national lab managed by the University of California, delivers science solutions to the world – solutions derived from hundreds of patented and patent pending technologies plus scores of copyrighted software tools and published, peer-reviewed manuscripts.
Berkeley Lab has more than one hundred cutting-edge research projects using AI to find new scientific solutions to national problems. Through this effort, computer scientists, mathematicians, and domain scientists are collaborating to turn burgeoning datasets into scientific insights. Visit Berkeley Lab’s Machine Learning for Science site for more information.
Berkeley Lab’s advanced materials expertise is applied to innovation in batteries and other energy storage technologies, semiconductors, and photovoltaics. Additional energy-related areas of expertise include grid modernization and security, bio-based fuels and chemicals and building energy and demand response. Several National User Facilities are available for collaborative engagement: the Advanced Light Source, Molecular Foundry, National Energy Research Scientific Computing Center (NERSC), Energy Sciences Network, and the Joint Genome Institute. Other specialized facilities include FLEXLAB for building energy research and the Advanced Biofuels Process Demonstration Unit.
Ernest Orlando Lawrence, the lab's founder, believed team science yielded the greatest discoveries. That belief is reflected today in interdisciplinary teams and collaborative projects connecting Berkeley Lab, industry, and other research organizations. Berkeley Lab's Intellectual Property Office, connects industry partners with lab innovations and unique facilities to enable lab-to-market transition.
Dani Ushizima PhD, is a Staff Scientist at Lawrence Berkeley National Laboratory, a Data Scientist at UC Berkeley and an Affiliate Faculty at UC San Francisco. More than a decade at LBNL, her research in image analysis and pattern recognition has impacted a broad array of scientific investigation that depends on experimental data, particularly images. In 2015, Ushizima received the U.S. Department of Energy Early Career award to focus on pattern recognition applied to diverse scientific domains, such as structural analysis of materials science samples. She is also recipient of the Science without Borders Researcher award (CNPq/Brazil) for her work on machine learning applied to cytology, as part of an initiative focused on public healthcare. She has also led the Image Processing team for the Center for Advanced Mathematics for Energy Related Applications (CAMERA). Recently, she's been investigating lung scans for COVID-19 screening as part of initiatives related to the National Virtual Biotechnology Laboratory (NVBL).
COVID-19-related research: "Can CT Scans Be Used to Quickly and Accurately Diagnose COVID-19?"
Heather Gray is an experimental particle physicist working on the ATLAS experiment at the Large Hadron Collider (LHC) just outside Geneva in Switzerland. She has broad interests in particle physics, but the primary focus of her research is the Higgs boson -- the most recently discovered elementary particle, the only known elementary scalar of nature and the final piece of the remarkably successful Standard Model. She studies the properties of the Higgs boson and, in particular, how it interacts with different types of quarks, including top, bottom and charm quarks. Other research interests include the development of track reconstruction algorithms, silicon detectors and algorithms for quantum computers. A theme throughout her research is applications of machine learning.
Areas of expertise: physics/astrophysics, quantum computing, AI/machine learning, accelerators, dark energy/dark matter, particle physics, Higgs boson
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"
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.