I’m a high-performance computing (HPC) person picking up machine learning (ML). My current research interest is applying HPC techniques to make ML computations faster. I'm also interested in using ML to help optimize programs. I work on improving TensorFlow's performance.
I received my Ph.D. in Computer Science from the University of California, Berkeley in 2017, advised by Professor Kathy Yelick. My dissertation focused on avoiding communication in large-scale, scientific applications such as N-body algorithms and matrix computations on supercomputers to achieve highly-scalable and energy-efficient implementations. I received a B.Eng. in Computer Engineering from Kasetsart University in Bangkok, Thailand. I came to the United States for my graduate study on the Fulbright Scholarship.
My most recent project (prior to joining Google) was on massively parallel sparse inverse covariance matrix estimation (ICM). Sparse ICM is a popular tool for capturing the underlying dependency relationships in multivariate data. Unfortunately, most estimators are not scalable enough to handle the sizes of modern high-dimensional data sets. Our parallel proximal gradient method implementation, HP-CONCORD, demonstrates parallel scalability on tens of thousands of cores for problems with millions of dimensions. HP-CONCORD can be used to analyze real datasets, e.g., identifying the functional regions of the human brain from fMRI data. See more details, including the open source code, on HP-CONCORD’s webpage.