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Steve is Director of the Applied Energy Division at SLAC National Accelerator Laboratory. The Applied Energy Division conducts research on the electric grid, batteries, photovoltaics, and advanced manufacturing. The Applied Energy Division is part of the Energy Sciences Directorate, which conducts research in chemistry, materials, computer science, and applied energy. SLAC is operated by Stanford University for the U.S. Department of Energy's Office of Science. Previously, Steve developed and managed research programs at Stanford University in artificial intelligence, computer science, energy, and sustainability. Steve helped to create new programs at Stanford such as the Institute for Human-Centered AI, SAIL-Toyota Center for AI Research, Stanford Data Science Initiative, Bay Area PV Consortium, and Energy and Environment Affiliates Program. Prior to joining Stanford, Steve was president and CEO of solar energy company Cyrium Technologies, consultant for the National Renewable Energy Lab and US Department of Energy, venture capitalist at Worldview Technology Partners, vice president at SDL (JDSU), and member of the technical staff at MIT Lincoln Laboratory. Steve received a PhD and MS from Stanford and BS from UC Berkeley, all in electrical engineering. Steve is a Fellow of the SPIE, a former Board member of the MRS, and a former utilities commissioner for the City of Palo Alto.
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.
Brian Nord works at the intersection of artificial intelligence (AI), quantum computing, and astrophysics. Primarily, he uses big data sets from astronomy and AI techniques to learn about dark energy, dark matter and the very early universe. In particular, Brian uses deep neural networks to classify and measure large numbers of astronomical objects and remove noise from cosmic images. He applies generative modeling, like GANs, to produce fast simulations of the cosmos. He is also using deep reinforcement learning for the development of a self-driving telescope for automated astronomical observation and discovery.
On the other hand, Brian also uses big astronomical data sets to address key challenges with deep learning --- such as the interpretability of neural networks, integration of neural networks with statistics and integration of AI algorithms with physical models. In particular, he is developing techniques in Bayesian statistics to improve uncertainty quantification in deep learning models.
Brian is an Associate Scientist at Fermilab in Batavia, Illinois, and Senior Member of the Kavli Institute for Cosmological Physics. He is also the founder and Principal Investigator for the Deep Skies Lab (deepskieslab.ai), an international community of researchers who work in the space of artificial intelligence and astrophysics.
Areas of expertise: Artificial Intelligence; Cosmology; Astrophysics; Simulations; Deep Learning; Statistical Modeling