Lili Zheng

I am currently an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign. Before joining the University of Illinois, I was a postdoctoral researcher at Rice University, advised by Prof. Genevera Allen. I completed my Ph.D. in the Department of Statistics at the University of Wisconsin-Madison, under the supervision of Prof. Garvesh Raskutti. I received my bachelor's degree at the University of Science and Technology of China (USTC) in 2016. Research interest: I am broadly interested in statistical machine learning, where I develop reliable methods and theory for large-scale and complex statistical problems. In particular, I have been working on the following topics: graphical models, interpretable machine learning, selective inference, missing data, time series. E-mail: llzheng [AT] illinois [DOT] edu Google Scholar CV

Preprints

(*: equal contribution)

• Cluster Quilting: Spectral Clustering for Patchwork Learning
  Lili Zheng*, Andersen Chang*, Genevera I. Allen
  Submitted

• Joint Semi-Symmetric Tensor PCA for Integrating Multi-modal Populations of Networks
  Jiaming Liu*, Lili Zheng*, Zhengwu Zhang, Genevera I. Allen
  Submitted

• High-dimensional Multi-class Classification with Presence-only Data
  Lili Zheng, Garvesh Raskutti
  Under revision at Electronic Journal of Statistics

• Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles
  Luqin Gan*, Lili Zheng*, Genevera I. Allen
  

• Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity
  Andersen Chang, Lili Zheng, Genevera I. Allen
  Under revision at Journal of the American Statistical Association, Applications and Case Studies

Selected Publications

(*: equal contribution)

• Graphical Model Inference with Erosely Measured Data
  Lili Zheng, Genevera I. Allen
  Journal of the American Statistical Association, Theory and Methods. 2023 [Code]

• Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
  Hao Chen*, Lili Zheng*, Raed Al Kontar, Garvesh Raskutti (*: equal contribution)
  Journal of Machine Learning Research. 2022
  

• Context-dependent Self-exciting Point Processes: Models, Methods, and Risk Bounds in High Dimensions
  Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark
  Journal of Machine Learning Research. 2021 [Slides][Code]
  

• Testing for High-dimensional Network Parameters in Auto-regressive Models
  Lili Zheng, Garvesh Raskutti
  Electronic Journal of Statistics. 2019 [Code]