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Daniel is Head of the Machine Learning (ML) Initiative at SLAC National Accelerator Laboratory. The ML initiative coordinates the application of ML techniques across the range of science at the lab, with special focus in autonomous facility and experimental control, edge-ML, sparse and irregular data sets, prognostics, and new approaches to data analysis. Previously, Daniel led the Accelerator Directorate’s ML department, as well as working in the Linac Coherent Light Source Laser Division. Prior to joining SLAC, Daniel worked as a conservation scientist at the Museum of Modern Art in New York, and as a data analyst for WhenU.com. Daniel received his PhD in Applied Physics from Stanford, and his AB in Physics from Harvard.
Dr. Ian Foster is the Director of Argonne’s Data Science and Learning Division, Argonne Senior Scientist and Distinguished Fellow and the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago. He was the Director of Argonne’s Computation Institute from 2006 to 2016.
Foster’s research contributions span high-performance computing, distributed systems, and data-driven discovery. He has published hundreds of scientific papers and eight books on these and other topics. Methods and software developed under his leadership underpin many large national and international cyberinfrastructures.
Foster received a BSc (Hons I) degree from the University of Canterbury, New Zealand, and a PhD from Imperial College, United Kingdom, both in computer science. His awards include the Global Information Infrastructure (GII) Next Generation award, the British Computer Society’s Lovelace Medal, R&D Magazine’s Innovator of the Year, the IEEE Tsutomu Kanai award, and honorary doctorates from the University of Canterbury, New Zealand and CINVESTAV, Mexico.
He is an elected Fellow of the American Association for the Advancement of Science, the Association for Computing Machinery, and British Computer Society.
- Distributed, parallel, and data-intensive computing technologies
- Innovative applications of computing technologies to scientific problems in such domains as climate change and biomedicine