Lab Partnering Service Discovery
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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.
Lawrence Berkeley National Laboratory (Berkeley Lab), a U. S. Department of Energy Office of Science national lab managed by the University of California, delivers science solutions to the world â solutions derived from hundreds of patented and patent pending technologies plus scores of copyrighted software tools and published, peer-reviewed manuscripts.
Berkeley Lab has more than one hundred cutting-edge research projects using AI to find new scientific solutions to national problems. Through this effort, computer scientists, mathematicians, and domain scientists are collaborating to turn burgeoning datasets into scientific insights. Visit Berkeley Labâs Machine Learning for Science site for more information.
Berkeley Labâs advanced materials expertise is applied to innovation in batteries and other energy storage technologies, semiconductors, and photovoltaics. Additional energy-related areas of expertise include grid modernization and security, bio-based fuels and chemicals and building energy and demand response. Several National User Facilities are available for collaborative engagement: the Advanced Light Source, Molecular Foundry, National Energy Research Scientific Computing Center (NERSC), Energy Sciences Network, and the Joint Genome Institute. Other specialized facilities include FLEXLAB for building energy research and the Advanced Biofuels Process Demonstration Unit.
Ernest Orlando Lawrence, the lab's founder, believed team science yielded the greatest discoveries. That belief is reflected today in interdisciplinary teams and collaborative projects connecting Berkeley Lab, industry, and other research organizations. Berkeley Lab's Intellectual Property Office, connects industry partners with lab innovations and unique facilities to enable lab-to-market transition.
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.
- Basic science: seeks to understand how nature works. This research includes experimental and theoretical work in materials science, physics, chemistry, biology, high-energy physics, and mathematics and computer science, including high performance computing.
- Applied science and engineering helps to find practical solutions to society’s problems. These programs focus primarily on energy resources, environmental management and national security.