Kathy Yelick: Machine Learning in Science - Applications, Algorithms & Architectures | IACS Seminar

Speaker: Katherine Yelick, Robert S. Pepper Distinguished Professor of Electrical Engineering and Computer Sciences, Executive Associate Dean, Division of Computing, Data Science, and Society, UC Berkeley Senior Faculty Scientist, Lawrence Berkeley National Laboratory 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 experim