Lab Partnering Service Discovery
Use the LPS faceted search filters, or search by keywords, to narrow your results.
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
Matthew Marinella is a Principal Member of the Technical Staff with Sandia National Labs. He is Principal Investigator for Sandia’s Nonvolatile Memory Program and leads research projects on neuromorphic, radiation hard, and energy efficient computing. Dr. Marinella chairs the Emerging Memory Devices Section for the IRDS Roadmap Beyond CMOS Chapter, serves on various technical program committees, and is a Senior Member of the IEEE. He received a PhD in electrical engineering from Arizona State University under Dieter K. Schroder in 2008.
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.