This will be a virtual seminar

Abstract

Machine learning is being used in nearly every discipline in science, from biology and environmental science to chemistry, cosmology and particle physics. Scientific data sets continue to grow exponentially due to improvements in detectors, accelerators, imaging, and sequencing as well as networks of embedded sensors and personal devices. In some domains, large data sets are being constructed, curated, and shared with the scientific community and data may be reused for multiple problems using emerging algorithms and tools for new insights. Machine learning adds a powerful set of techniques to the scientific toolbox, used to analyze complex, high-dimensional data, automate and control experiments, approximate expensive experiments, and augment physical models with those learned from data.

On the systems side, scientists have always demanded some of the fastest computers for large and complex simulations and more recently for high throughput simulations that produce databases of annotated materials and more. Now the desire to train machine learning models on scientific data sets and for robotics, speech and vision, has created a new set of users and demands for high end computing. The changing architectural landscape has increased node level parallelism, added new forms of hardware specialization, and continued the ever-growing gap between the cost of computation and data movement at all levels. These changes are being reflected in both commercial clouds and HPC facilities--including upcoming exascale facilities--and also placing new requirements on scientific applications, whether they are performing physics-based simulations, traditional data analytics, or machine learning. Using examples from my own research in bioinformatics for the microbiome, I will describe some of the algorithmic challenges and how these machines are being used to deliver new scientific capabilities.

Biography

Katherine Yelick is the Robert S. Pepper Distinguished Professor of Electrical Engineering and Computer Sciences and the Executive Associate Dean in the Division of Computing, Data Science and Society (CDSS) at the University of California, Berkeley. Starting in January of 2022, she will be the Vice Chancellor for Research at there. She is also a Senior faculty Scientist at Lawrence Berkeley National Laboratory. Her research is in high performance computing, programming systems, parallel algorithms, and computational genomics and she currently leads the ExaBiome project on Exascale Solutions for Microbiome Analysis.

Yelick was Director of the National Energy Research Scientific Computing Center (NERSC) from 2008 to 2012 and the led the Computing Sciences Area at Berkeley Lab from 2010 through 2019, where she oversaw NERSC, the Energy Sciences Network (ESnet) and the Computational Research Division. She earned her Ph.D. in Electrical Engineering and Computer Science from MIT and has been a professor at UC Berkeley since 1991 with a joint research appointment at Berkeley Lab since 1996. Yelick is a member of the National Academy of Engineering and the American Academy of Arts and Sciences. She is a Fellow of the Association for Computing Machinery (ACM) and the American Association for the Advancement of Sciences (AAAS), and she is a recipient of the ACM/IEEE Ken Kennedy award and the ACM-W Athena award.