I work at the intersection of statistics, machine learning, optimization, etc. Some of my current research interests are:

• High-dimensional data analysis
• Nonparametric estimation
• Inference on networks
• Optimization esp. convex relaxations
• Functional data analysis
• Graphical models
• Quantitative Finance

I did my PhD at UC Berkeley with Martin Wainwright and a post-doc at University of Michigan with Liza Levina and Long Nguyen. You can learn more about me from my CV.

My office is at 8105F Math Sciences Building.
Office Hours: Tu/Th 11:30am–12:30pm, virtual on Zoom (Spring 2022)

Email:

## news

Mar 4, 2022 The bcdc package for Bayesian community detection with covariates is out. Check out the paper for more details. Check out the hsbm package for hierarchical network clustering. Check out the nett package for community detection! New paper out: Adjusted chi-square test for degree-corrected block models.

## selected publications

1. AOS
Concentration of kernel matrices with application to kernel spectral clustering
The Annals of Statistics 2021
2. NeurIPS
Label consistency in overfitted generalized k-means
Neural Information Processing Systems (NeurIPS) 2021
3. JMLR
Optimal bipartite network clustering
Journal of Machine Learning Research 2020
4. NeurIPS
Globally optimal score-based learning of directed acyclic graphs in high-dimensions
In Advances in Neural Information Processing Systems 2019
5. JMLR
Analysis of spectral clustering algorithms for community detection: the general bipartite setting
Journal of Machine Learning Research 2019
6. AOS
On semidefinite relaxations for the block model
The Annals of Statistics 2018