I graduated from Department of Statistics at UC Berkeley in May 2010 under supervision of Professor Peter Bickel. I worked at Google as a statistician for one year before I joined the Statistics Department at Carnegie Mellon University.
Statistics and machine learning theory and methodology
- Flexible inference tools under weak assumptions, including regression, classification, and cross-validation.
- High dimensional matrices with structured eigen-components, including sparse PCA, functional PCA, and network block models.
- Statistical learning with differential privacy, adaptive data analysis.
- Genetics, autism, sequencing data
- Traffic speed prediction
- Chromatin segmentation
Spring 2019: 36700, Probability and Mathematical Statistics.