Welcome to Lin Liu (刘林)’s homepage

I am an Assistant Professor at the Institute of Natural Sciences (INS) at Shanghai Jiao Tong University (SJTU) in Fall 2020. 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 also find my articles on my Google Scholar profile.) (Italic: co-first authorship)

Statistical 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).

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 methodology and causal inference:

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 116 (533), 224-239.

Haoqi Sun, Michael Leon, LL, Shabani S Mukerji, Gregory K Robbins, M Brandon Westover. Clinically Relevant Mediation Analysis using Controlled Indirect Effect. (2021+). Under review.

Statistical Computing:

Lei Li, LL, Yuzhou Peng. A splitting Hamiltonian Monte Carlo method for efficient sampling. (2021+). Submitted.

Mathematical biology:

Kimiyo N Yamamoto, LLL, Akira Nakamura, Hiroshi Haeno, and Franziska Michor. Stochastic evolution of pancreatic cancer metastases during logistic clonal expansion. (2019). JCO Clinical Cancer Informatics 3: 1-11.

Helena A Yu, Camelia Sima, Daniel Feldman, LLL, Bhavapriya Vaitheesvaran, Justin Cross, Charles M Rudin, Mark G Kris, William Pao, Franziska Michor, and Gregory J Riely. Phase 1 study of twice weekly pulse dose and daily low-dose erlotinib as initial treatment for patients with EGFR-mutant lung cancers. (2019). Annals of Oncology 28 (2): 278-284.

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.

Statistical and Machine Learning Applications:

Sheng’en S. Hu, LL, Qi Li, Wenjing Ma, Michael J. Guertin, Clifford A. Meyer, Ke Deng, Tingting Zhang, Chongzhi Zang (2021+). Accurate estimation of intrinsic biases for improved analysis of chromatin accessibility sequencing data using SELMA. Under review. bioRxiv link.

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). (2021+). Accepted to Clinical Gastroenterology and Hepatology.

Michael J Leone, Haoqi Sun, Christine L Boutros, LL, Elissa Ye, Lee Sullivan, Robert J Thomas, Gregory K Robbins, Shibani S Mukerji, M Brandon Westover. HIV Increases Sleep-based Brain Age Despite Antiretroviral Therapy. (2021). Sleep.

Sheng’en Hu, Dawei Huo, Zhaowei Yu, Yujie Chen, Jing Liu, LL, Xudong Wu, and Yong Zhang. ncHMR detector: a computational framework to systematically reveal non-classical functions of histone modification regulators. (2020). Genome Biology 21 (1): 48.

       ncHMR detector: software link; webserver 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.

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.

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.

LL, Yiqian Zhang, Jianxing Feng, Ning Zheng, Junfeng Yin, and Yong Zhang. GeSICA: Genome segmentation from intra-chromosomal associations. (2012). BMC Genomics 13 (1): 164.

       GeSICA: software link

Qi Liu, Han Zhou, LL, Xi Chen, Ruixin Zhu, and Zhiwei Cao. Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study. (2011). BMC Bioinformatics 12 (1): 294.

Miscellaneous:

LL. Book Review: Matrix-Based Introduction to Multivariate Data Analysis, 2nd Edition by Kohei Adachi. (2021). In press in Biometrics.