I am an Assistant Professor in the Department of Statistical Science at Duke University. My research focuses on statistical and computational methods for rigorous and efficient inference, with a focus on the adaptive nature of modeling and analysis in modern data science. I am particularly interested in sampling algorithms with applications to Bayesian inference, selective inference, and generative modeling. Before joining Duke, I was a Research Scientist at the Center for Computational Mathematics at the Flatiron Institute (2024–2025). I received my Ph.D. in Statistics from Stanford University in 2024 and my B.S. in Mathematics from Tsinghua University in 2019. Here is my CV. If you are interested in my research, feel free to get in touch! Email: sifan.liu@duke.edu

Publications

• The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler
  Nawaf Bou-Rabee, Bob Carpenter, Tore S. Kleppe, Sifan Liu
  Manuscript 2025

• Antithetic Noise in Diffusion Models
  Jing Jia, Sifan Liu, Bowen Song, Wei Yuan, Liyue Shen, Guanyang Wang
  Manuscript 2025

• Flexible Selective Inference with Flow-based Transport Maps
  Sifan Liu, Snigdha Panigrahi
  Manuscript 2025

• The No-Underrun Sampler: A Locally-Adaptive, Gradient-Free MCMC Method
  Nawaf Bou-Rabee, Bob Carpenter, Sifan Liu, Stefan Oberdörster
  Manuscript 2025

• Selective Inference with Distributed Data
  Sifan Liu, Snigdha Panigrahi
  Journal of Machine Learning Research, 2025

• Cross-Validation with Antithetic Gaussian Randomization
  Sifan Liu, Snigdha Panigrahi, Jake A. Soloff
  Manuscript 2024

• Transport Quasi-Monte Carlo
  Sifan Liu
  Manuscript 2024

• Conditional Quasi-Monte Carlo with Constrained Active Subspaces
  Sifan Liu
  SIAM Journal on Scientific Computing, 2024

• Langevin Quasi-Monte Carlo
  Sifan Liu
  NeurIPS 2024

• An Exact Sampler for Inference after Polyhedral Model Selection
  Sifan Liu
  Manuscript 2023

• Pre-integration via Active Subspaces
  Sifan Liu, Art B. Owen
  SIAM Journal on Numerical Analysis, 2023

• Black-box Selective Inference via Bootstrapping
  Sifan Liu, Jelena Markovic, Jonathan Taylor
  Manuscript 2022

• Global and Individualized Community Detection in Inhomogeneous Multilayer Networks
  Shuxiao Chen, Sifan Liu, Zongming Ma
  Annals of Statistics, 2022

• Statistical Challenges in Tracking the Evolution of SARS-CoV-2
  Lorenzo Cappello, Jaehee Kim, Sifan Liu, Julia A. Palacios
  Statistical Science, 2022

• Quasi-Monte Carlo Quasi-Newton for Variational Bayes
  Sifan Liu, Art B. Owen
  Journal of Machine Learning Research, 2021

• How to Reduce Dimension with PCA and Random Projections?
  Fan Yang, Sifan Liu, Edgar Dobriban, David P. Woodruff
  IEEE Transactions on Information Theory, 2021

• Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods
  Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci
  NeurIPS 2020

• Ridge Regression: Structure, Cross-validation, and Sketching
  Sifan Liu, Edgar Dobriban
  ICLR 2020, Spotlight.

• Asymptotics for Sketching in Least Squares Regression
  Edgar Dobriban, Sifan Liu
  NeurIPS 2019

Miscellaneous

I co-organize an online seminar on Monte Carlo methods. Check out the website and subscribe to the mailing list!