Biostatistics · Causal Inference · Computational Biology

Hi, I'm Yushi

Developing statistically principled methods for scientific discovery across genetics, genomics, biomedical data, and environmental systems.

Postdoc at Harvard Princeton PhD '26 Harvard MS '19 Peking University '17
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Portrait of Yushi Tang
Untitled © 2019 Yushi Tang.
01 / About

I am a Postdoctoral Research Fellow in Biostatistics at Harvard T.H. Chan School of Public Health, where I develop rigorous and scalable methodologies in causal inference, statistical learning, and trustworthy AI.

My work is motivated by questions in genetics, genomics, and the integrative analysis of multimodal biomedical and environmental data. I am especially interested in methods that remain reliable under complex dependence, population heterogeneity, and real-world data constraints.

I received my PhD in Quantitative and Computational Biology with a Graduate Certificate in Statistics and Machine Learning from Princeton University.

02 / Path

Appointments

July 2026 — Present

Postdoctoral Research Fellow

Department of Biostatistics, Harvard University

January — June 2026

Postdoctoral Research Associate

Lewis-Sigler Institute for Integrative Genomics, Princeton University

Education

December 2025

PhD, Princeton University

May 2019

MSc, Harvard University

July 2017

BSc & BEc, Peking University

Teaching

Spring 2022, 2023, 2024

QCB 408 / 508 · Foundations of Statistical Genomics

Assistant in Instruction, Princeton University

Spring 2019

STAT 115 / 215 · BST 282 · Introduction to Computational Biology and Bioinformatics

Teaching Assistant, Harvard University

Summer 2018

BST 215 · Linear and Longitudinal Regression

Teaching Assistant, Harvard University

03 / Selected work
2026

A generalized test of genotype–phenotype causality in population-sampled nuclear families

Tang Y and Storey JD · PLOS Genetics 22(7): e1012231

View paper ↗
2026

Identifying causal genotype–phenotype relationships for population-sampled parent–child trios

Tang Y, Cabreros I, Storey JD · Genetic Epidemiology 50(1): e70027

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2026

Comparison of variant callers using 60,532 multi-ancestry whole-genome sequences

Zhou H, Li Z, Shyr D, Li X, Yang H, Dey R, Tang Y, Maier R, Boerwinkle E, Buyske S, Daly M, Felsenfeld A, Gibbs RA, Gupta N, Hall IM, Matise T, Metcalf GA, Smith A, Reeves C, Sofia HJ, Stitziel NO, Zody MC, NHGRI Genome Sequencing Program (GSP) Consortium, Neale B, Lin X · Briefings in Bioinformatics 27(2): bbag130

View paper ↗
2024

Global WWTP Microbiome-based Integrative Information Platform: From experience to intelligence

F. Xiong, Z. Su, Tang Y, T. Dai, and D. Wen · Environmental Science and Ecotechnology 20: 100370

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2020

A tripartite microbial-environment network indicates how crucial microbes influence the microbial community ecology

Tang Y, Dai T, Su Z, Hasegawa K, Tian J, Chen L, and Wen D · Microbial Ecology 79: 342–356

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2018

Machine learning with k-means dimensional reduction for predicting survival outcomes in patients with breast cancer.

Zhao M, Tang Y, Kim H, and Hasegawa K · Cancer Informatics 17: 1176935118810215

View paper ↗
View all publications by topics →
04 / Recognition
  • Reviewers’ Choice Abstract AwardAmerican Society of Human Genetics · 2022
  • King Peh Kwoh FellowshipPrinceton University · 2020
  • Lewis-Sigler Institute Scholars AwardPrinceton University · 2019
  • Outstanding Graduate of BeijingBeijing Municipal Commission of Education · 2017
  • UCLA Cross-disciplinary Scholars AwardUniversity of California, Los Angeles · 2016
  • National Scholarship, twiceMinistry of Education of China · 2015 & 2016

Let’s connect

You can find me on

GitHub ↗ LinkedIn ↗ Bluesky ↗