Biostatistics · Causal Inference · Statistical Learning and Computing

Hi, I'm Yushi

Transforming genomic, multimodal biomedical, and environmental data into reliable biomedical insights through principled statistical methods.

Postdoct in Biostatistics Princeton PhD '26 Harvard MS '19 Peking University '17
Explore my research Google Scholar ↗ Teaching Software
01 / About

I am a biostatistics researcher who develops principled and scalable methods in causal inference, statistical learning, computational biology, 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 / Professional Experience

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

Statistical Genetics and Genomics

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

View paper ↗
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 ↗


Biomedical Informatics

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 ↗


Environmental Statistics

2024

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

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

View paper ↗
2023

Screening of priority antibiotics in Chinese seawater based on the persistence, bioaccumulation, toxicity and resistance

Li F, Bao Y, Chen L, Su Z, Tang Y, and Wen D · Environmental International 179: 108140

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2023

Industrial effluents boosted antibiotic resistome risk in coastal environments

Su Z, Wen D, Gu AZ, Zheng Y, Tang Y, and Chen L · Environmental International 171: 107714

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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

View paper ↗
2018

Dynamics of sediment microbial functional capacity and community interaction networks in an urbanized coastal estuary

Dai T, Zhang Y, Ning D, Su Z, Tang Y, Huang B, Mu Q, and Wen D · Frontiers in Microbiology 9: 2731

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2018

Sediment bacterial community structures and their predicted functions implied the impacts from natural processes and anthropogenic activities in coastal area

Su Z, Dai T, Tang Y, Tao Y, Huang B, Mu Q, and Wen D · Marine Pollution Bulletin 131: 481–495

View paper ↗
2016

Identifying the key taxonomic categories that characterize microbial community diversity using full-scale classification: a case study of microbial community in the sediments of Hangzhou Bay

Dai T, Zhang Y, Tang Y, Bai Y, Tao, Y, Huang B, and Wen D · FEMS Microbiology Ecology 92(10): fiw150

View paper ↗
View all publications and preprints →
04 / Software and beyond
R packageR

geneticTMT

Implements the Transmission Mean Test and generalized Transmission Mean Test for causal inference of genotype–phenotype relationships in population-sampled parent–child trios and nuclear families.

View repository ↗
Reproducible analysisHTML

causal-trio

Code and reproducible analyses accompanying the TMT framework for identifying causal genotype–phenotype relationships in population-sampled parent–child trios.

View repository ↗
Research codeTeX + code

causal-nuclear-family

Research materials for generalized family-based causal inference with multiple offspring, shared family structure, and a broad range of outcome types.

View repository ↗
Open methods · Reproducible workflows · Documented research codeAll GitHub projects ↗
05 / 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 ScholarshipMinistry of Education of China · 2015 & 2016

Let’s connect

You can find me on

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