Abstract: AI and machine learning are opening up many new avenues in fundamental science. We at NERSC, the mission HPC center for the Department of Energy, are involved in many collaborations to push the state of the art in AI techniques and applications. In this talk I will first give an overview of some activities at NERSC before describing in more detail a specific project to bring probabilistic programming to existing scientific simulators at supercomputing scale. Full details about this meeting will be posted here: https://bids.github.io/MLStatsForum/.
The Machine Learning and Science Forum (formerly the Berkeley Statistics and Machine Learning Forum) meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. To receive email notifications about upcoming meetings, or to request more information, please contact firstname.lastname@example.org. All interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend.
Wahid Bhimji is a Big Data Architect in the Data and Analytics Services team at NERSC. His current interests include machine learning, databases and data management. He currently leads several machine learning projects, particularly those related to High-Energy Physics; coordinates aspects of deep learning deployment for NERSC and the CS-Area; is the primary user point of contact for NERSC databases; is workflow and data lead for the upcoming Perlmutter machine; and he is the engagement lead for the HEP-CCE. Perviously, he was the user lead for the commissioning of Cori Phase 1, particularly data services, and for the Burst Buffer.