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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.
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
Daniel is Head of the Machine Learning (ML) Initiative at SLAC National Accelerator Laboratory. The ML initiative coordinates the application of ML techniques across the range of science at the lab, with special focus in autonomous facility and experimental control, edge-ML, sparse and irregular data sets, prognostics, and new approaches to data analysis. Previously, Daniel led the Accelerator Directorate’s ML department, as well as working in the Linac Coherent Light Source Laser Division. Prior to joining SLAC, Daniel worked as a conservation scientist at the Museum of Modern Art in New York, and as a data analyst for WhenU.com. Daniel received his PhD in Applied Physics from Stanford, and his AB in Physics from Harvard.
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.
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.
Dr. Marius Stan is the Intelligent Materials Design Lead in the Argonne National Laboratory’s Applied Materials division. Stan is a computational physicist and chemist interested in complexity, non-equilibrium thermodynamics, heterogeneity, and materials design for energy and electronics applications. He uses artificial intelligence, machine learning, and multi-scale computer simulations to understand and predict properties and evolution of complex physical systems.
Stan came to Argonne and the University of Chicago in 2010, from Los Alamos National Laboratory. He is a Senior Fellow at the University of Chicago’s Computation Institute (CI) and a senior Fellow of the Northwestern-Argonne Institute for Science and Engineering (NAISE).
The goal of Stan’s research is to discover or design materials, structures, and device architectures for energy applications, such as nuclear energy and energy storage, and for the new generation computers. To that end, he develops theory-based (as opposite to empirical) mathematical models of thermodynamic and chemical properties of imperfect materials. The imperfection comes from defects or deviations from stoichiometry (e.g., in battery electrodes), from irradiation (e.g. in nuclear fuels), or doping (e.g. computer memory devices). Then Stan uses the models in computer simulations of coupled heat and chemical transport, micro(nano)-structure evolution, phase-stability, and phase transformations. To analyze large and complex experimental and computational data sets, Stan uses Bayesian analysis and machine learning methods based on regression and evolutionary (genetic) algorithms that can produce robust data screening and sampling. In parallel, Stan designs experiments to validate the models and simulations.
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.
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.
Dr. Warren L. Davis IV is a Principal Member of Technical Staff in the Scalable Analysis and Visualization department in the Center for Computing Research at Sandia National Laboratories. He was the principal investigator for the Hybrid Methods for Cybersecurity Analysis LDRD and the Machine Learning in Adversarial Environments LDRD research projects which had significant impact on cyber operations at the lab. In addition, he is the principal investigator of the Machine Learning for Intelligent Data Capture on Exascale Platforms research project for the DOE ASCR program.
Warren joined the technical staff at Sandia in 2009. He received his Ph.D. in computer science from Florida State in 2006, gaining industry experience as a graduate intern at both the National Astronomical Observatory of Japan in Tokyo and the IBM Almaden Research Center, where he was hired as a Research Staff Member after graduation. Warren has published over 20 journal articles, conference publications, and peer-reviewed presentations, and has worked in the fields of cybersecurity, healthcare informatics, climate modeling, material science, text analytics, combustion, and fluid dynamics, to name a few. In addition, he was awarded the 2019 Black Engineer of the Year Award in Research Leadership.
Nhan Tran is a Wilson Fellow at Fermilab working on the Compact Muon Solenoid experiment at the Large Hadron Collider and is also developing new dark sector experimental initiatives. He is generally interested in deploying machine learning as a powerful tool across fundamental physics. His recent research focus is on the intersection of machine learning with real-time systems and embedded electronics as well as heterogeneous computing to improve experimental efficiency and sensitivity. He received his PhD from Johns Hopkins University in 2011 and was a postdoctoral researcher at Fermilab prior to joining in his current position.
Areas of expertise: ML Algorithms for Data Reconstruction and Pattern Recognition; Real-Time Low-Latency ML in Resource-Constrained Environments; Heterogeneous Computing