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Dr. Mark Bryden is the founding director of the Simulation, Modeling and Decision Science program at Ames Laboratory and is a professor of mechanical engineering at Iowa State University. Dr. Bryden’s research is focused on the federation of information from disparate sources (e.g., models, data, and other information elements) to create detailed models of engineered, human, and natural systems that enable engineering decision making for these complex systems. Dr. Bryden has published more than 180 peer-reviewed articles and co-authored the textbook Combustion Engineering. He has founded two successful startups based on his research work, and he has founded the nonprofit ETHOS, a community of 150+ researchers focused on meeting the needs for clean village energy in the developing world. He has received three patents, three R&D 100 awards, two Regional Excellence in Technology Transfer awards, and a National Excellence in Technology Transfer award. In 2013 he and his coauthors received the ASME Melville Medal. His professional experience includes three years as an engineer and 11 years as a manager at Westinghouse Electric in Idaho Falls, Idaho, and Pittsburgh, Pennsylvania. In addition, for more than 15 years Professor Bryden has worked on energy systems for the poor in a number of developing countries.
Energy research represents a major focus for BNL over the next decade. We are using a multifaceted approach driven by the unique state-of-the art laboratory facilities and the inter-disciplinary expertise of our scientific staff to solve fundamental questions regarding U.S. energy independence and to translate discoveries into deployable technologies. The laboratory has identified several energy focus areas – including biofuels, complex materials, catalysis, and solar energy.
BNL's one-of-kind user facilities include the National Synchrotron Light Source II NSLS-II, which produces extremely bright beams of x-ray, ultraviolet, and infrared light for scientists exploring materials—including superconductors, catalysts, geological samples, and proteins—to accelerate advances in energy, environmental science, and medicine. Scientists at our Center for Functional Nanomaterials create materials and explore their unique structure and properties at the nanoscale, with a focus on more efficient solar and energy storage materials. And at BNL's Northeast Solar Energy Research Center, where researchers from labs, academia, and industry study test new solar technologies, working to make solar "power plants" more efficient and economical
In addition to fundamental research, the laboratory actively collaborates with industry and other academic institutions to bring the benefits of scientific discoveries to the marketplace. Brookhaven's Office of Strategic Partnerships integrates Brookhaven Lab's industry engagement, technology licensing, and economic development functions to expand the impact of collaborative research and technology commercialization. Strategic Partnerships supports the Laboratory's science mission through identifying, pursuing and managing partnerships with a broad set of private-sector companies, federal agencies, and non-federal entities. For information on licensing and industry.
- Basic science: seeks to understand how nature works. This research includes experimental and theoretical work in materials science, physics, chemistry, biology, high-energy physics, and mathematics and computer science, including high performance computing.
- Applied science and engineering helps to find practical solutions to society’s problems. These programs focus primarily on energy resources, environmental management and national security.
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.
Dr. David Stracuzzi is a Principal Member of Technical Staff at Sandia National Laboratories and has been studying machine learning and artificial intelligence for 20 years. He currently leads several projects that apply data-driven modeling and uncertainty analysis methods to tasks related to remote sensing data, pattern-of-life data, geophysical data, and data related to physics-based simulations. Prior to joining Sandia in 2010, Dr. Stracuzzi was a member of the research faculty at Arizona State University working on computational cognitive architectures for developing intelligent agents.