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.
Raga is a member of the technical staff at Sandia. She is a molecular, developmental and, most recently, computational biologist with a background in regulation of gene expression and cell fates in mammalian systems. Her main area of focus is characterizing, monitoring, and engineering of molecular pathways within cells to alter their phenotypic outcomes. She combines the use of bioinformatics, modeling, and machine learning with experimental biology to dissect the mechanisms by which cellular responses can be programmed, both intrinsically and by external influences.
Raga’s current projects include enhancing antimicrobial and immunomodulatory activity of mesenchymal stromal cells through CRISPR-based gene modulation, prediction of CRISPR efficiency across cell types, and generating optogenetic (light-activatable) neurons and neuron-like cells for interfacing with low-power computing devices.
She received her Bachelor of Arts in Natural Sciences (Biochemistry) from the University of Cambridge, UK, in 2004. She then went on to receive her Ph.D. in Biochemistry, Cell and Molecular Biology (laboratory of Dr. W. Lee Kraus) at Cornell University, Ithaca, NY, in 2010.