His research interests lie broadly in statistical methodology and machine learning algorithms, as well as applied statistics in various domains. His research seeks to meet challenges from modern data, including dimensionality, scalability, structural complexity and varying data quality. His past research discovered connections between statistical perturbation analysis and the approximate computation of a broad class of problems such as large-scale computation in kernel-based learning. His previous work also characterizes the impact of data quality on a number of machine learning tasks such as classification and clustering. He received PhD in statistics from the University of California, Berkeley and was a Postdoctoral Researcher at the Fred Hutchinson Cancer Research Center in Seattle, WA. Before that, in 2008 he was a Research Scientist at the Intel Research Lab in Berkeley, CA.