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.
Title: Assistant Computational Scientist
- Scientific literature processing to rapidly find protein-protein interaction candidates
- AI-based platform to accelerate discovery of novel drug compounds
- Querying and filtering interface for users to efficiently scan tens of thousands of sources
Since joining Brookhaven National Laboratory in 2018, Carlos X. Soto has been an active member of the Machine Learning Group, where he has contributed extensively toward large-scale scientific data extraction from published literature using natural language processing (NLP) techniques applied to areas such as functional genomics, drug discovery, and government reports. His contributions to machine learning for the integration of biological genomics data helped prompt an ongoing partnership with Oak Ridge National Laboratory to use NLP techniques to accelerate COVID-19 drug discovery. In 2019, Soto was part of the Brookhaven team awarded one of only two Nuclear Threat Initiative (NTI) Nuclear Security Index Challenge grants. The work, Towards a Predictive Nuclear Security Threat Model, aims to create a predictive model by integrating NTI Index data into machine learning and sentiment analysis. Presently, his work on COVID-related projects focuses on providing domain scientists with powerful new computational tools to identify patterns and insights in large volumes of documents, in particular relating to potential drug compound candidates.
Title: Chair, Computation and Data-Driven Discovery
- High-performance and Distributed Computing
- High-throughput and scalable infrastructure for drug discovery
- Machine learning enhanced high-performance computing
Shantenu Jha’s research interests converge at the intersection of high-performance distributed computing and computational and data-driven science. His current interests include system software, algorithms and methods to enhance the performance of HPC computations using machine learning methods. Dr. Jha leads the RADICAL-Cybertools project, a suite of middleware building blocks used to support large-scale science and engineering applications. In addition to applying these advances for drug discovery and design for COVID-19, Jha leads Brookhaven Lab’s efforts on two Exascale computing projects -- CANDLE and ExaWorks.
Title: Computational Scientist
- AI driven multi-omics data analysis including deep sequence analysis and multi-modal clustering
- AI driven material design using graph representation
- Causal analysis and uncertainty quantification using deep learning
- Extreme scale AI using high performance computing and streaming analysis
Shinjae Yoo has developed various fundamental AI technologies including novel machine learning algorithm design and applied them to a broad spectrum of scientific application area including medical and computational chemistry. Yoo also has been doing not only prototype demonstration but also bring such prototype into the production deployment using cloud and HPC systems. Yoo has published over 100 papers including top tier conferences and journals and is interested in social impact research.
Title: Physicist, Collider-Accelerator Department Control Systems Head
Expertise: Particle Accelerator Physics and Technology, Computational Accelerator Physics, Particle Accelerator Control Systems, Data Science and Machine Learning in Accelerator Science, Quantum Information Science (QIS), Storage Rings for Quantum Computing
As an accelerator physicist in the Collider-Accelerator Department at Brookhaven National Laboratory (BNL), Kevin has spent over 35 years working in accelerator physics where he has gained expertise and experience in accelerator design, particle beam simulations, processing and analysis of data, particle accelerator-based data science and machine learning, as well as ion trap dynamics, crystalline beams for quantum information sciences (QIS), and ion trap-based quantum computing.
Kevin has broad experience, as a designer of the NASA Space Radiation Laboratory, a member of the RHIC design and commissioning team, and most recently as a member of the electron ion collider (EIC) project at BNL. His work extends internationally, with collaborations with researchers at CERN, Fermilab, J-PARC & KEK in Japan, as well as domestically with Stony Brook University, the University of New Mexico, and Cornell University.
Kevin and Dr. Thomas Roser are the inventors of the storage ring quantum computer, a new kind of quantum information system that utilizes a circular radio-frequency quadrupole to create an unbounded ion trap. Kevin is the principle investigator for the Storage Ring Quantum Computer project, which offers a pathway to large scale QIS.
Kevin is an author on over thirty peer reviewed publications, co-author on a book chapter in “Challenges and Goals for Accelerators in the XXI Century” (2016), and an author on over 150 conference publications. Kevin has mentored many students in his career, including three Ph.D. students from Stony Brook University.
Energy research represents a major focus for BNL over the next decade. We are using a multifaceted approach driven by the unique state-of-the art laboratory facilities and the inter-disciplinary expertise of our scientific staff to solve fundamental questions regarding U.S. energy independence and to translate discoveries into deployable technologies. The laboratory has identified several energy focus areas – including biofuels, complex materials, catalysis, and solar energy.
BNL's one-of-kind user facilities include the National Synchrotron Light Source II NSLS-II, which produces extremely bright beams of x-ray, ultraviolet, and infrared light for scientists exploring materials—including superconductors, catalysts, geological samples, and proteins—to accelerate advances in energy, environmental science, and medicine. Scientists at our Center for Functional Nanomaterials create materials and explore their unique structure and properties at the nanoscale, with a focus on more efficient solar and energy storage materials. And at BNL's Northeast Solar Energy Research Center, where researchers from labs, academia, and industry study test new solar technologies, working to make solar "power plants" more efficient and economical
In addition to fundamental research, the laboratory actively collaborates with industry and other academic institutions to bring the benefits of scientific discoveries to the marketplace. Brookhaven's Office of Strategic Partnerships integrates Brookhaven Lab's industry engagement, technology licensing, and economic development functions to expand the impact of collaborative research and technology commercialization. Strategic Partnerships supports the Laboratory's science mission through identifying, pursuing and managing partnerships with a broad set of private-sector companies, federal agencies, and non-federal entities. For information on licensing and industry.
Title: Assistant Computational Scientist, Quantum Computing Group, Computational Science Initiative, Brookhaven National Laboratory
- High-performance Computing and Software Optimization
Yao-Lung (Leo) Fang is a theoretical physicist. His past work includes exploring quantum nonlinear optics, open quantum systems, and quantum Monte Carlo. His current research interests are quantum software optimization, quantum machine learning, and high-performance computing applied to quantum simulation and other scientific challenges, such as image reconstruction.