Lin Liu’s website

I am an Assistant Professor at the Institute of Natural Sciences (INS) at Shanghai Jiao Tong University (SJTU). I am also affiliated with the School of Mathematical Sciences and the SJTU-YALE Joint Center for Biostatistics and Data Science.

I graduated from the Department of Biostatistics at Harvard University in 2018. My advisors are Professor Franziska Michor and Professor James M. Robins. My current research lies in nonparametric, semiparametric and high-dimensional statistics, robust statistical methods, causal inference, computational and mathematical biology.

I am also interested in the theory of deep learning, estimation and inference in inverse problems and applying causal inference tools in biomedical research.

I obtained my undergraduate degree from the School of Life Sciences at Tongji University, under the supervision of Professor Yong Zhang.

You can reach me by email: linliu@alumni.tongji.edu.cn or linliu@sjtu.edu.cn

Selected Papers

(You can find all my papers on my Google Scholar profile.) (Italic: co-first authorship; #: (co-)corresponding authorship; $: student mentee)

Working papers:

Zixin Wang$, Qinshuo Liu, Xi-An Li, LL#, Zhonghua Liu#, and Lei Zhang#. Towards neural semiparametric statistics and causal inference. In preparation.

LL# and Chang Li$. New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators. Working draft.

James M. Robins, Lingling Li, LL, Rajarshi Mukherjee, Eric Tchetgen Tchetgen, and Aad van der Vaart. Corrigenda to “Minimax estimation of a functional on a structured high-dimensional model” by James M. Robins, Lingling Li, Rajarshi Mukherjee, Eric Tchetgen Tchetgen, and Aad van der Vaart. Available upon request.

LL, Rajarshi Mukherjee, Whitney Newey, and James M. Robins. Semiparametric efficient empirical higher-order influence function estimators. Under review.

LL, Rajarshi Mukherjee, and James M Robins. Can we tell if the justification of the validity of Wald confidence intervals of doubly robust functionals may be incorrect, without assumptions? Under revision.

Statistical and Learning Theory:

LL, Rajarshi Mukherjee, James M Robins, and Eric Tchetgen Tchetgen. Adaptive estimation of nonparametric functionals. (2021). Journal of Machine Learning Research, 22 (99): 1-66.

LL, Rajarshi Mukherjee, and James M Robins. On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning. (2020). Statistical Science, 35 (3): 518-539. (arXiv: 1904.04276)

       See the Discussion (arXiv: 2006.09613) of our paper by Edward H. Kennedy, Siva Balakrishnan, and Larry Wasserman and our Rejoinder (arXiv: 2008.03288).

Statistical and Causal Inference Methodology:

Siqi Xu, LL#, and Zhonghua Liu#. DeepMed: Semiparametric causal mediation analysis with debiased deep learning. NeurIPS 2022. (arXiv: 2210.04389)

       DeepMed: software link.

LL, Zach Shahn, James M Robins and Andrea Rotnitzky. Efficient estimation of optimal regimes under a no direct effect assumption. (2021). Journal of the American Statistical Association: Theory and Methods, 116 (533): 224-239.

Statistical Computing:

Lei Li, LL, Yuzhou Peng$. A splitting Hamiltonian Monte Carlo method for efficient sampling. (2023). CSIAM Transactions on Applied Mathematics, 4 (1): 41-73. (arXiv: 2105.14406)

Mathematical and Computational Biology:

Nana Wei, Yating Nie, LL#, Xiaoqi Zheng# and Hua-Jun Wu#. Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data. (2022). PLOS Computational Biology, 18 (12): e1010753. arXiv: 2205.12432.

       Secuer: software link

Sheng’en S. Hu, LL, Qi Li, Wenjing Ma, Michael J. Guertin, Clifford A. Meyer, Ke Deng, Tingting Zhang, Chongzhi Zang. Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA. (2022). Nature Communications, 13: 5533.

       SELMA: software link

Kyle S Smith, LLL, Shridar Ganesan, Franziska Michor, and Subhajyoti De. Nuclear topology modulates the mutational landscapes of cancer genomes. (2017). Nature Structural & Molecular Biology, 24 (11): 1000-1006.

LLL, Justin Brumbaugh, Ori Bar-Nur, Zachary Smith, Matthias Stadtfeld, Alexander Meissner, Konrad Hochedlinger, and Franziska Michor. Probabilistic modeling of reprogramming to induced pluripotent stem cells. (2016). Cell Reports, 17 (12): 3395-3406.

Philipp M Altrock, LLL, and Franziska Michor. The mathematics of cancer: integrating quantitative models. (2015). Nature Reviews Cancer, 15 (12): 730-745.

Jasmine Foo, LLL, Kevin Leder, Markus Riester, Yoh Iwasa, Christoph Lengauer, and Franziska Michor. An evolutionary approach for identifying driver mutations in colorectal cancer. (2015). PLOS Computational Biology, 11 (9): e1004350.

LL, Subhajyoti De, and Franziska Michor. DNA replication timing and higher-order nuclear organization determine single-nucleotide substitution patterns in cancer genomes. (2013). Nature Communications, 4: 1502.

Statistical and Machine Learning Applications:

Jeremy R. Glissen Brown, Nabil M. Mansour, Pu Wang, Maria Aguilera Chuchuca, Scott B. Minchenberg, Madhuri Chandnani, LL, Seth A. Gross, Neil Sengupta, Tyler M. Berzin. Deep learning computer-aided polyp detection reduces Adenoma Miss Rate: A U.S. multi-center randomized tandem colonoscopy study (CADeT-CS Trial). (2022). Clinical Gastroenterology and Hepatology, 20 (7): 1499-1507.e4.

Michalina Janiszewska, LL, Vanessa Almendro, Yanan Kuang, Cloud Paweletz, Rita A Sakr, Britta Weigelt, Ariella B Hanker, Sarat Chandarlapaty, Tari A King, Jorge S Reis-Filho, Carlos L Arteaga, So Yeon Park, Franziska Michor, and Kornelia Polyak. In situ single-cell analysis identifies heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast cancer. (2015). Nature Genetics, 47 (10): 1212-1219.

Miscellaneous:

Zixiao Wang$, Yi Feng$, LL#. Book Review: Semiparametric regression in R by Jaroslaw Harezlak, David Ruppert, and Matt P. Wand. (2022). Journal of the American Statistical Association: Theory and Methods, 117 (540): 2283-2287.

LL. Book Review: Matrix-Based Introduction to Multivariate Data Analysis, 2nd Edition by Kohei Adachi. (2021). Biometrics, 77 (4): 1498-1500.

Teaching

Fall 2020: Computational methods (undergraduate students in engineering or economics @ SJTU)

Fall 2020 – now: Advanced mathematical statistics (graduate students in statistics @ SJTU)

Summer 2021 – now: Causal inference methods in data science (graduate students in statistics, biostatistics, applied mathematics, and life sciences @ SJTU)

Spring 2023 – now: Bayesian statistics (undergraduate students in statistics, (applied) mathematics and economics @ SJTU)

Service

Area Chair (AC) for CLeaR 2023.

Links

A reading group on interacting particle systems organized by Lei Li