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