I am an assistant professor at UC Davis in the Statistics department. Before this I was a postdoctoral researcher and lecturer at UCSD in the Mathematics department. I hold a Ph.D in Machine Learning and Statistics from Carnegie Mellon University where my advisors were Aarti Singh and Alessandro Rinaldo. In my research, I develop and study computationally efficient statistical methodology for understanding complex phenomena in large datasets.
- Stats 290: Statistics Seminar Series
- Stats 141B: Data & Web Technologies for Data Analysis
- Stats 208: Statistical Machine Learning
- Liwei Wu, PhD Student
- Xiaoyue Li, PhD Student
- Yujun Chen, Masters Student
- Andrew Chin, Undergraduate Student
- Institute of Mathematical Statistics
- Bernoulli Society
- IEEE, Signal Processing Society
Graph Structured Signal Processing(6 publications)
Traditional signal processing techniques such as wavelet denoising and kernel smoothing, implicitly assume that the domain is homogeneous. Graph structure provides us with a more flexible framework for filters over heterogeneous media, social networks, or semantic information.
Scan Statistics over Networks(6 publications)
We develop novel methodology for detecting faint signals in sensor networks and images. In images we develop a precise asymptotic analysis for fast scanning tools based on convolutional feedforward neural nets. In general graphs, we look at approximation algorithms for the generalized likelihood ratio test.
Estimation in High-dimensions(3 publications)
Here we look at the fused lasso and trend filtering, which are locally adaptive non-linear filters that can outperform wavelet denoising. We also explore heteroscedasticity in high-dimensional models, for variance function estimation and locating master regulators in genomics data.
DSI Workshop on Classification: Github Repo
- Starting your data science portfolio: your workflow, git and GitHub
- Massive list of public datasets
- List of jupyter notebook projects
- Markdown cheatsheet
- LaTeX symbol cheatsheet