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.
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