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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.
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.
Thomas is the Director of Energy Analysis and Environmental Impacts for Berkeley Lab’s Energy Technologies Area as well as the Head of the Sustainable Energy and Environmental Systems department. His expertise lies in developing air pollution sensors and evaluating impacts of emission treatment and control technologies. He serves as the editor of the Aerosol Science and Technology journal.
COVID-19-related research: "New Research Launched on Airborne Virus Transmission in Buildings"
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.
Dr. Marius Stan is the Intelligent Materials Design Lead in the Argonne National Laboratory’s Applied Materials division. Stan is a computational physicist and chemist interested in complexity, non-equilibrium thermodynamics, heterogeneity, and materials design for energy and electronics applications. He uses artificial intelligence, machine learning, and multi-scale computer simulations to understand and predict properties and evolution of complex physical systems.
Stan came to Argonne and the University of Chicago in 2010, from Los Alamos National Laboratory. He is a Senior Fellow at the University of Chicago’s Computation Institute (CI) and a senior Fellow of the Northwestern-Argonne Institute for Science and Engineering (NAISE).
The goal of Stan’s research is to discover or design materials, structures, and device architectures for energy applications, such as nuclear energy and energy storage, and for the new generation computers. To that end, he develops theory-based (as opposite to empirical) mathematical models of thermodynamic and chemical properties of imperfect materials. The imperfection comes from defects or deviations from stoichiometry (e.g., in battery electrodes), from irradiation (e.g. in nuclear fuels), or doping (e.g. computer memory devices). Then Stan uses the models in computer simulations of coupled heat and chemical transport, micro(nano)-structure evolution, phase-stability, and phase transformations. To analyze large and complex experimental and computational data sets, Stan uses Bayesian analysis and machine learning methods based on regression and evolutionary (genetic) algorithms that can produce robust data screening and sampling. In parallel, Stan designs experiments to validate the models and simulations.
He is a computational biologist staff scientist in the Biological Systems and Engineering Division within Berkeley Lab’s Biosciences Area. The goal of his research is to understand the functional organization and dynamic coordination of sensorimotor networks underlying learned, skilled behaviors. His lab’s research will address the question of how distributed neural circuits gives rise to coordinated behaviors. To address this question, his lab combines multi-scale electrophysiology with optical manipulations in reaching rodents and advanced computational methods to investigate the functional organization and dynamic coordination of sensorimotor networks. He also develop software to analyze this data, including novel machine learning algorithms and deep learning approaches.
A strong science, technology, and engineering foundation enables Sandia's mission through a capable research staff working at the forefront of innovation, collaborative research with universities and companies, and discretionary research projects with significant potential impact. Sandia is committed to hiring the nation’s best and brightest, equipping them with world class tools and facilities while providing opportunities to collaborate with technical experts from many different scientific disciplines. To ensure our fundamental science and engineering core is vibrant and cutting edge, Sandia has chosen to invest in the following research foundations: Bioscience, Computing and Information Science, Engineering Science, Geoscience, Materials Science, Nanodevices and Microsystems, Radiation Effects and High Energy Density Science. These diverse research areas enable a multidisciplinary approach to resolve emerging national security problems.
Oak Ridge National Laboratory is the largest U.S. Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security. ORNL's diverse capabilities span a broad range of scientific and engineering disciplines, enabling the Laboratory to explore fundamental science challenges and to carry out the research needed to accelerate the delivery of solutions to the marketplace. ORNL supports DOE's national missions of:
- Scientific discovery—We assemble teams of experts from diverse backgrounds, equip them with powerful instruments and research facilities, and address compelling national problems;
- Clean energy—We deliver energy technology solutions for energy-efficient buildings, transportation, and manufacturing, and we study biological, environmental, and climate systems in order to develop new biofuels and bioproducts and to explore the impacts of climate change;
- Security—We develop and deploy "first-of-a-kind" science-based security technologies to make the world a safer place.
ORNL supports these missions through leadership in four major areas of science and technology:
- Neutrons—We operate two of the world's leading neutron sources, which enable scientists and engineers to gain new insights into materials and biological systems;
- Computing—We accelerate scientific discovery through modeling and simulation on powerful supercomputers, advance data-intensive science, and sustain US leadership in high-performance computing;
- Materials—We integrate basic and applied research to develop advanced materials for energy applications;