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.
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.
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.
Matthew Reno is a Principal Member of Technical Staff in the Electric Power Systems Research Department at Sandia National Laboratories. His research focuses on distribution system modeling and analysis with big data and high penetrations of photovoltaics by applying cutting edge machine learning algorithms to power system problems. Matthew is also involved with the IEEE Power System Relaying Committee for developing guides and standards for protection of microgrids and systems with high penetrations of inverter-based resources. He received his Ph.D. in electrical engineering from Georgia Institute of Technology.
Frances Chance (a computational neuroscientist by training) has always been fascinated by how neural circuits compute information. She is intrigued by potential parallels between the operations of neural systems and the challenges faced by modern computers (for example analysis of large datasets). Her research program at Sandia Labs applies knowledge of neural systems towards the development of novel neuro-inspired algorithms and brain-based architectures to improve the performance of computing systems and other engineered systems. She received her PhD and MS from Brandeis University and her BS from the California Institute of Technology.