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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.
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.
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.
Rick Stevens is Argonne’s Associate Laboratory Director for Computing, Environment and Life Sciences.
Stevens has been at Argonne since 1982, and has served as director of the Mathematics and Computer Science Division and also as Acting Associate Laboratory Director for Physical, Biological and Computing Sciences. He is currently leader of Argonne’s Exascale Computing Initiative, and a Professor of Computer Science at the University of Chicago Physical Sciences Collegiate Division. From 2000-2004, Stevens served as Director of the National Science Foundation’s TeraGrid Project and from 1997-2001 as Chief Architect for the National Computational Science Alliance.
Stevens is interested in the development of innovative tools and techniques that enable computational scientists to solve important large-scale problems effectively on advanced scientific computers. Specifically, his research focuses on three principal areas: advanced collaboration and visualization environments, high-performance computer architectures (including Grids) and computational problems in the life sciences. In addition to his research work, Stevens teaches courses on computer architecture, collaboration technology, virtual reality, parallel computing and computational science.
Dr. Kiran Lakkaraju is a Senior Member of the Technical Staff at Sandia National Laboratories, California in the Systems Research & Analysis III group. Kiran’s research has been marked by extensive interdisciplinary efforts that bring together the social and computational sciences. Kiran has been investigating how games, including Massively Multiplayer Online Games and wargames can be used as a means to systematically and quantitatively study conflict escalation and global strategic stability. Kiran is a member of the Project on Nuclear Gaming which has developed one of the first experimental wargames, SIGNAL. Kiran has a background in artificial intelligence, multi-agent systems and computational social science. He holds a M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign.
Emily Donahue is a member of the technical staff at Sandia. She applies state-of-the-art machine learning innovations to novel applications for national security. She performs research in unsupervised learning, anomaly detection, and data-driven code acceleration. Emily earned her Master of Engineering at Cornell University with a focus in computer vision. While away from her computer, she enjoys landscape painting and rock climbing.
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.
Nicola Ferrier received her doctorate from Harvard University in 1992. After postdoctoral fellowships at Oxford University and Harvard, she joined the Department of Mechanical Engineering at the University of Wisconsin (UW)-Madison in 1996. She became an associate professor in 2003 and professor in 2009. She received the NSF CAREER award (1997) and the UW Vilas Associates Professorship (1999) and the UW Honored Instructor Award (2009). She joined the Mathematics and Computer Science Division at Argonne in 2013.
Ferrier’s research interests are in the use of computer vision (digital images) to control robots, machinery, and devices, with applications as diverse as medical systems, manufacturing, and projects that facilitate “scientific discovery” (such as her recent project using machine vision and robotics for plant phenotype studies).
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.