Lab Partnering Service Discovery
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Title: Deputy Director
- Optimal Design of Experiments under Uncertainty
- Machine Learning
- Computational Physics
- Statistical Physics
Prior to joining Brookhaven National Laboratory in 2017, Francis “Frank” Alexander spent nearly 20 years at Los Alamos National Laboratory, finishing his tenure as the acting division leader of the lab’s Computer, Computational, and Statistical Sciences (CCS) Division. At Los Alamos, he grew in several leadership roles, including serving as deputy leader of CCS Division’s Information Sciences Group and leader of the Information Science and Technology Institute. Alexander was introduced to the DOE national laboratory complex during his postdoctoral work with Los Alamos’ Center for Nonlinear Studies and the Institute for Scientific Computing Research at Lawrence Livermore National Laboratory. He also was a research assistant professor at Boston University’s Center for Computational Science. Alexander has led many research projects and has published more than 50 papers in peer-reviewed journals. In addition to leading Brookhaven’s artificial intelligence and machine learning strategy effort, Alexander currently serves as project director of the multi-laboratory ExaLearn Co-design Center for Exascale Machine Learning Technologies, part of the Exascale Computing Project. He also leads various projects involving optimal experimental design, including for biological systems.
Dani Ushizima PhD, is a Staff Scientist at Lawrence Berkeley National Laboratory, a Data Scientist at UC Berkeley and an Affiliate Faculty at UC San Francisco. More than a decade at LBNL, her research in image analysis and pattern recognition has impacted a broad array of scientific investigation that depends on experimental data, particularly images. In 2015, Ushizima received the U.S. Department of Energy Early Career award to focus on pattern recognition applied to diverse scientific domains, such as structural analysis of materials science samples. She is also recipient of the Science without Borders Researcher award (CNPq/Brazil) for her work on machine learning applied to cytology, as part of an initiative focused on public healthcare. She has also led the Image Processing team for the Center for Advanced Mathematics for Energy Related Applications (CAMERA). Recently, she's been investigating lung scans for COVID-19 screening as part of initiatives related to the National Virtual Biotechnology Laboratory (NVBL).
COVID-19-related research: "Can CT Scans Be Used to Quickly and Accurately Diagnose COVID-19?"
During his career with NETL, U.S. Army veteran Jimmy Thornton has worked tirelessly to advance new technology development for Fossil Energy (FE), and that remains true today with current efforts to investigate uses for artificial intelligence (AI) and machine learning (ML) for FE technology development.
Born in Kentucky and growing up in Campbells Creek, Thornton joined the U.S. Army at the encouragement of his high school baseball coach who was an Army Reserve drill instructor. Trained as an infantryman and entering service in early 1983, Thornton was stationed in Germany, where he completed French Commando School in Givet, France.
Leaving active service in 1987, Thornton joined the Kentucky National Guard while studying at Eastern Kentucky University, and he later transferred to the West Virginia National Guard after accepting a professional internship with the U.S. Department of Energy (DOE) in Morgantown in 1988. Commissioned as an officer in 1992, he served with the 201st Field Artillery and was deployed to Iraq in 2004 during Operation Iraqi Freedom.
Achieving the rank of major, Thornton retired in 2010 with more than 27 years of service and continues to serve the 201st as an active member of the 201st Association. He said many of the skills and life lessons the army taught him continue to guide him at NETL, where he started working in 1991 when it was known as the Morgantown Energy Technology Center.
Thornton’s work at the Lab as associate director for the Computational Science and Engineering directorate includes advances in applied artificial intelligence, and machine learning, which he said have great potential to benefit the country’s energy industries, especially the existing fleet of coal-fired power plants and the subsurface. He noted that increased data availability and the use of supercomputers can speed up the development cycle of new tools for decision making because machine learning techniques can provide insights beyond our current understanding.
Heather Gray is an experimental particle physicist working on the ATLAS experiment at the Large Hadron Collider (LHC) just outside Geneva in Switzerland. She has broad interests in particle physics, but the primary focus of her research is the Higgs boson -- the most recently discovered elementary particle, the only known elementary scalar of nature and the final piece of the remarkably successful Standard Model. She studies the properties of the Higgs boson and, in particular, how it interacts with different types of quarks, including top, bottom and charm quarks. Other research interests include the development of track reconstruction algorithms, silicon detectors and algorithms for quantum computers. A theme throughout her research is applications of machine learning.
Areas of expertise: physics/astrophysics, quantum computing, AI/machine learning, accelerators, dark energy/dark matter, particle physics, Higgs boson