Ph.D., Associate Laboratory Director, Lawrence Berkeley National Laboratory
Katherine Yelick is an expert on high performance computing (HPC), and is currently working on HPC solutions to large-scale data analysis problems in biology and medicine. She is leading the ExaBiome project, developing HPC algorithms and software for analysis of microbial DNA and communities, which includes de novo assembly of single genomes and metagenomes at unprecedented speed and size. Her prior research includes scalable algorithms for a variety of simulation and data analysis problems, as well as programming languages, compilers, and libraries for parallel computing. Yelick is a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley and the Associate Laboratory Director for Computing Sciences at Berkeley National Laboratory. At Berkeley Lab, she oversees the National Energy Research Scientific Computing Center (NERSC) and the Energy Sciences Network (ESnet) as well as the Computational Research Division, which includes research in mathematics, computer science, and science partnerships.
Session Abstract – PMWC 2019 Silicon Valley
Session Synopsis: We see an emerging era of potential explosive growth of AI applications in healthcare, but the implementation and access to AI is challenged by isolated data stores and the high degree of complexity of the available tools. Furthermore, deep learning requires lots of data to train and refine models which, in turn, complicates and burdens compute systems as well as data movement. This panel will discuss AI and machine learning systems requirements, and potential paths to overcoming the challenges of AI implementation.