Trained as an engineer, statistician, and computational biologist, I am passionate about providing principled solutions to analyzing complex data sets. In particular, I am interested in utilizing the power of mapping to understand large-scale biological systems and scientific knowledge bases.

About Me

I am a Ph.D. student at Stanford working in statistics and machine learning with applications in biology and medicine. My main interests are the computational and statistical challenges presented by high-throughput genomics data. I am supervised by Professor Chiara Sabatti in Statistics and Biomedical Data Science.

My expertise can be broadly categorized into three domains:

  • high-dimension statistics where the goal is to simultaneously test a large number of hypotheses
  • unsupervised learning where the goal is to cluster and visualize unlabeled data for pattern mining
  • network science where the goal is to generate and interpret relational structures from the data

My projects are highly interdisciplinary. I have also had multiple long-term collaborations with Professor Serafim Batzoglou in Computer Science and Professor Calvin Kuo in Medicine. I have been fortunate to apply my training in both engineering and statistics to new biotechnologies during my internships at 10x Genomics and Illumina.

I received my M.S. degree in Statistics from Stanford University and my B.S. degree in Electrical and Computer Engineering from Olin College of Engineering, where I worked with Siddhartan Govindasamy, Sarah Spence Adams, and Denise Troxell. During my undergraduate studies, I was also a visiting research student with Professor Matthew McKay at Hong Kong University of Science and Technology.