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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.
- 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.
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.