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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).
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.
Gabriel Perdue is a Scientist in Fermilab’s Quantum Institute, where he works on quantum computing for simulation and machine learning, and more generally on machine learning in physics. He also has a long history at Fermilab in neutrino physics and spent the last decade working on the MINERvA experiment and on the GENIE MC event generator.
Areas of expertise: AI Algorithms for Data Analysis and Systems Control