Lab Partnering Service Discovery
Use the LPS faceted search filters, or search by keywords, to narrow your results.
Ben Brown is a statistical scientist in the Environmental Genomics and Systems Biology division within Berkeley Labâs Biosciences Area. He specializes in the development of novel machine algorithms, usually for the biological and environmental sciences at Berkeley Lab. His group develops âthird-waveâ 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.
Haruko Wainwright received her MS in nuclear engineering (2006), MA in statistics (2010), and PhD in nuclear engineering (2010) at University of California, Berkeley. Her initial research interest was to investigate the environmental impact of nuclear waste and nuclear weapon productions. Her PhD dissertation focused on Bayesian geostatistical inverse modeling for subsurface characterization at the uranium-contaminated DOE Hanford site. Since then, she has broadened her research interest to various environmental problems, including Arctic ecosystem responses to climate change, groundwater contamination, and deep-subsurface CO2 storage. In addition to working in many interdisciplinary projects, she is a deputy lead of the site application thrust in the Advanced Simulation Capability for Environmental Management project, leading the site application at the Savannah River Site F-Area. She is also on the leadership team of Institute for Resilient Communities, which aims to prepare communities for radiological and other disasters through research, education and outreach activities.
For more information: https://eesa.lbl.gov/profiles/haruko-murakami-wainwright/
COVID-19-related research: "Using Machine Learning to Estimate COVID-19âs Seasonal Cycle"
Eoin Brodie is a Senior Scientist in Berkeley Labâs Earth and Environmental Sciences Area (EESA). Dr. Brodie serves as the Deputy Director of the Climate and Ecosystem Sciences Division, Program Domain Lead for Environmental and Biological Systems Sciences and co-lead of the labwide Microbes-to-Biomes initiative. At the University of California, Berkeley, Dr. Brodie is an Associate Adjunct Professor in the Department of Environmental Science, Policy and Management. His research group develops approaches to observe, sense and simulate the distribution and activities of microorganisms in natural and managed ecosystems.
For more information: https://eesa.lbl.gov/profiles/eoin-brodie/
COVID-19-related research: "Using Machine Learning to Estimate COVID-19's Seasonal Cycle". Other principal investigators include: Ben Brown, Nicola Falco, Dan Feldman, Zhao Hao, Chaincy Kuo, Joshua Ladau, and Haruko Wainwright.
Title: Deputy Director
- Optimal Design of Experiments under Uncertainty
- Machine Learning
- Computational Physics
- Statistical Physics
Prior to joining Brookhaven National Laboratory in 2017, Francis “Frank” Alexander spent nearly 20 years at Los Alamos National Laboratory, finishing his tenure as the acting division leader of the lab’s Computer, Computational, and Statistical Sciences (CCS) Division. At Los Alamos, he grew in several leadership roles, including serving as deputy leader of CCS Division’s Information Sciences Group and leader of the Information Science and Technology Institute. Alexander was introduced to the DOE national laboratory complex during his postdoctoral work with Los Alamos’ Center for Nonlinear Studies and the Institute for Scientific Computing Research at Lawrence Livermore National Laboratory. He also was a research assistant professor at Boston University’s Center for Computational Science. Alexander has led many research projects and has published more than 50 papers in peer-reviewed journals. In addition to leading Brookhaven’s artificial intelligence and machine learning strategy effort, Alexander currently serves as project director of the multi-laboratory ExaLearn Co-design Center for Exascale Machine Learning Technologies, part of the Exascale Computing Project. He also leads various projects involving optimal experimental design, including for biological systems.
During his career with NETL, U.S. Army veteran Jimmy Thornton has worked tirelessly to advance new technology development for Fossil Energy (FE), and that remains true today with current efforts to investigate uses for artificial intelligence (AI) and machine learning (ML) for FE technology development.
Born in Kentucky and growing up in Campbells Creek, Thornton joined the U.S. Army at the encouragement of his high school baseball coach who was an Army Reserve drill instructor. Trained as an infantryman and entering service in early 1983, Thornton was stationed in Germany, where he completed French Commando School in Givet, France.
Leaving active service in 1987, Thornton joined the Kentucky National Guard while studying at Eastern Kentucky University, and he later transferred to the West Virginia National Guard after accepting a professional internship with the U.S. Department of Energy (DOE) in Morgantown in 1988. Commissioned as an officer in 1992, he served with the 201st Field Artillery and was deployed to Iraq in 2004 during Operation Iraqi Freedom.
Achieving the rank of major, Thornton retired in 2010 with more than 27 years of service and continues to serve the 201st as an active member of the 201st Association. He said many of the skills and life lessons the army taught him continue to guide him at NETL, where he started working in 1991 when it was known as the Morgantown Energy Technology Center.
Thornton’s work at the Lab as associate director for the Computational Science and Engineering directorate includes advances in applied artificial intelligence, and machine learning, which he said have great potential to benefit the country’s energy industries, especially the existing fleet of coal-fired power plants and the subsurface. He noted that increased data availability and the use of supercomputers can speed up the development cycle of new tools for decision making because machine learning techniques can provide insights beyond our current understanding.
Title: Assistant Computational Scientist
- Scientific literature processing to rapidly find protein-protein interaction candidates
- AI-based platform to accelerate discovery of novel drug compounds
- Querying and filtering interface for users to efficiently scan tens of thousands of sources
Since joining Brookhaven National Laboratory in 2018, Carlos X. Soto has been an active member of the Machine Learning Group, where he has contributed extensively toward large-scale scientific data extraction from published literature using natural language processing (NLP) techniques applied to areas such as functional genomics, drug discovery, and government reports. His contributions to machine learning for the integration of biological genomics data helped prompt an ongoing partnership with Oak Ridge National Laboratory to use NLP techniques to accelerate COVID-19 drug discovery. In 2019, Soto was part of the Brookhaven team awarded one of only two Nuclear Threat Initiative (NTI) Nuclear Security Index Challenge grants. The work, Towards a Predictive Nuclear Security Threat Model, aims to create a predictive model by integrating NTI Index data into machine learning and sentiment analysis. Presently, his work on COVID-related projects focuses on providing domain scientists with powerful new computational tools to identify patterns and insights in large volumes of documents, in particular relating to potential drug compound candidates.
Thomas is the Director of Energy Analysis and Environmental Impacts for Berkeley Labâs 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"