Trained as an engineer, statistician, and computational biologist, I am passionate about providing principled solutions to analyzing complex data sets. 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

About Me

I graduated with a Ph.D. in Electrical Engineering from Stanford in April 2020. I worked on statistics and machine learning with applications in biology and medicine, supervised by Professor Chiara Sabatti in the Departments of Statistics and Biomedical Data Science.

My projects have been 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 Professor Siddhartan Govindasamy, Professor Sarah Spence Adams, and Professor 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.