Lab Partnering Service Discovery
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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.
He 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.
Jayakar “Charles” Tobin Thangaraj is currently the Science and Technology Manager and the Deputy Director at the Illinois Accelerator Research Center (IARC). He works at the frontiers of accelerator science where bold ideas enable discoveries that transform our fundamental understanding of the universe. He is passionate about partnership between science, technology and startups to enable entrepreneurship and innovation to solve 21st century challenges in environment, medicine and society. He received both his M.S. and PhD from the University of Maryland. Charles joined Fermilab as a People’s Fellow in 2009.
Areas of expertise: Artificial Intelligence for Accelerators; Machine Learning for Accelerators
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
- 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.