Professor of Biostatistics

Adjunct Professor of Statistics

Contact | Scholar | GitHub

News and Updates

• I'm excited to start working on a new NIH XAI grant titled Explainable Machine Learning to Guide Prefrontal Brain Stimulation in collaboration with UW colleagues, Zaid Harchaoui, Azadeh Yazdan and Eric Shea-Brown. This project combines statistical learning and applied math with neurobiology and bioengineering to help understand how the brain’s rich internal dynamics can be harnessed to steer its plasticity. We'll have lots of interesting data to solve really cool statistical problems!
• CONGRATULATIONS to three graduating PhD students, Wenyu Chen, Xiudi Li and Kunhui Zhang! Having successfully defended their dissertations, Wenyu and Kunhui are staying local to join Facebook and Amazon, and Xiudi will be moving across the country to do a postdoc at Harvard. What an exciting summer!!
• CONGRATULATIONS to Xiudi Li for receiving the 2022 Thomas R. Fleming Excellence in Biostatistics Award, UW Biostatistics most prestigious student honor.
• I am honored to have been elected as Fellow of the American Statistical Association (ASA). Congratulations to all my fellow ASA fellows!
• CONGRATULATIONS to Byol Kim for being selected as a UW Data Sciene Postdoc.
• I am honored to receive the 2022 Leo Breiman Award from the American Statistical Association (ASA) Section on Statistical Learning and Data Science (SLDS). This award reflects the excellent work by my amazing team and would not have been possible without the guiadance of my mentors and collaborators over the years.
• We have two new papers accepted for publication, both related to estimating directed networks:
  - Shubhadeep Chakraborty's paper, Non-parametric causal structure learning in high dimensions, published in Entropy, provides non-parametric extensions of PC and FCI algorithms in high dimensions.
  - Kunhui Zhang's paper, Penalized Estimation of Threshold Auto-Regression Models with Many Threshold and Components, which extends the classic TAR model to high dimensions, is published in EJS.
• CONGRATULATIONS to Aaron Hudson for accepting an Assistant Professor position at the Vaccine and Infectious Disease Division of Fred Hutch Cancer Research Center.
• We closed 2021 with a few accepted papers:
  - Steven Wang's paper, Causal discovery in high-dimensional point process networks with hidden nodes, will appear in Entropy;
  - Sen Zhao's paper, Network differential connectivity analysis, will appear in the Annals of Applied Statistics (AOAS). The method is implemented in the DCA R-package;
  - Asad Haris's paper, Generalized Sparse Additive Models will appear in Journal of Machine Learning Research (JMLR). This paper provides a general framework for sparse additive modeling implemented in the GSAM R-package.
• CONGRATULATIONS to Michael Hellstern for winning the Donovan J. Thompson award. Great job, Mike!
• We have two new manuscripts from Steven Wang's dissertation on estimation and inference for networks of high-dimensional point processes:
  - X. Wang, M. Kolar and A. Shojaie (2020) Statistical inference for networks of high-dimensional point processes.
  - X. Wang and A. Shojaie (2021) Joint estimation and inference for multi-experiment networks of high- dimensional point processes.
  These papers have been implemented in a new Python package, NeuroNetLearn available on GitHub.
• Excited to have Byol Kim join the group as a new postdoctoral fellow. Welcome aboard, Byol!
• Check out Yue Wang's new manuscripts on direct estimation of differential Granger causality between two high dimensional time series.
• CONGRATULATIONS to Aaron Hudson for receiving the 2021 Thomas R. Fleming Excellence in Biostatistics Award, UW Biostatistics most prestigious student honor.
• CONGRATULATIONS to Kun Yue for receiving a best student paper award in the WNAR student paper competition for her recent work on estimating variance components in large linear mixed models.
• The newest version of the netgsa package offers considerable speedups and interactive network visualization. Check out the accompanying paper published in PLoS Comp Bio for details and the vignette for examples.
• Our paper, Neural Granger Causality, which develops a flexible framework for estimating Granger causality using sparse deep neural networks, has been published at IEEE-TPAMI.
• CONGRATULATIONS to our graduating class of 2021, Aaron Hudson and Xu (Steven) Wang. Having successfully passed their final exams, Aaron and Steven will soon start the next chapter of their journey: Aaron as a postdoc at UC-Berkeley and Steven as a data scientist at Facebook. Well done guys!
• In our recent paper, Emily Fox and I review the classical notion of Granger causality as well as recent developments based on statistical machine learning.
• CONGRATULATIONS to Aaron Hudson for winning the 2021 David P. Byar Early Career Award. You can read more about Aaron's great work and award here and find the award winning paper on arxiv.
• I am excited to start working on my new R01 grant focused on developing novel statistical inference procedures for biomedical big data.
• Our manuscript, In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference, has been accepted for publication in Statistical Science.
• In collaboration with Simge Küçükyavuz, I have recently revisited my optimization roots to develop mixed integer programming algorithms for learning directed acyclic graphs (DAGs) from observational data. Our first paper has been accepted in INFORMS Journal on Optimization and is available here. Our second paper, available on arXiv, develops a second order conic program for DAG learning with a data-driven early stopping criterion that guarantees a consistent estimate.
• Yue Wang has joined Arizona State University as an Assistant Professor of Statistics. CONGRATULATIONS, Yue!
• In collaboration with the Promislow Lab, we recently investigated changes in metabolic networks associated with the effect of dietary restrictions on longevity. The resulting paper, highlighted with a perspective article in PLoS Genetics, uses our recent tool for differential network analysis, available in the CorDiffViz package.
• Steven (Xu) Wang's paper on Inference for networks of high-dimensional point processes has won a Best Student Paper Award from ASA Statistical Learning and Data Science. CONGRATULATIONS, Steven!
• Our new manuscript, The Generalized Matrix Decomposition Biplot and Its Application to Microbiome Data has been published as Editor's Pick in mSystems.
• My new manuscripts, Differential Network Analysis: A Statistical Perspective, provides an overview of statistical methods for detecting changes in networks.