Xihong Lin

Professor

Harvard University

United States

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Xihong Lin is a Professor at Harvard University, based in the United States. His research focuses on statistical methodologies for health data, infectious disease forecasting, and the application of machine learning techniques in genetics and genomics. Recent articles authored by Professor Lin explore topics such as the effects of gratitude practices during the COVID-19 pandemic and innovative approaches for estimating local heritability in population biobanks.

Articles (17)

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2025

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UNITED STATES

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Ryan Sun

Assistant Professor

The University of Texas MD Anderson Cancer Center

UNITED STATES

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Javier Rangel-Moreno

Research Associate Professor

University of Rochester

UNITED STATES

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Hae-Ryung Park

Assistant Professor

University of Rochester

UNITED STATES

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Maria de la luz Garcia-Hernandez

Research Associated professor

University of Rochester

UNITED STATES

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Zhonghua Liu

Assistant Professor

Columbia University

UNITED STATES

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Xihong Lin

Research Interests

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Articles (17)

Effects of an app-based brief gratitude practice on positive and negative daily affect during the COVID-19 pandemic

Advancing real-time infectious disease forecasting using large language models

LACE-UP: An ensemble machine-learning method for health subtype classification on multidimensional binary data

Single-cell RNA sequencing reveals placental response under environmental stress

Ensemble methods for testing a global null

Fast and scalable ensemble learning method for versatile polygenic risk prediction

Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks

Caught in the crossfire: Fears of Chinese–American scientists

Estimation of the Number of Spiked Eigenvalues in a Covariance Matrix by Bulk Eigenvalue Matching Analysis

Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix

Differences in set-based tests for sparse alternatives when testing sets of outcomes compared to sets of explanatory factors in genetic association studies

STAAR workflow: a cloud-based workflow for scalable and reproducible rare variant analysis

COVID-19 Spread Mapper: a multi-resolution, unified framework and open-source tool

Group testing via hypergraph factorization applied to COVID-19

A Minimax Optimal Ridge-Type Set Test for Global Hypothesis With Applications in Whole Genome Sequencing Association Studies

Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies

Smoking History as a Potential Predictor of Immune Checkpoint Inhibitor Efficacy in Metastatic Non-Small Cell Lung Cancer

Collaborators (10)

Hao (Frank) Yang

Yiran Chen

Xihao Li

David Hong

Yu Xie

Ryan Sun

Javier Rangel-Moreno

Hae-Ryung Park

Maria de la luz Garcia-Hernandez

Zhonghua Liu

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