Xihong Lin, PhD, is Professor and former Chair of the Department of Biostatistics and Coordinating Director of Program in Quantitative Genomics at the Harvard TH Chan School of Public Health (SPH), and Professor of Statistics at Harvard University. She earned her BS in Applied Mathematics from Tsinghua University in China, and PhD in Biostatistics from the University of Washington. She was elected to the National Academy of Medicine in 2018 and the National Academy of Sciences in 2023. She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the International Statistical Institute. She is the former Chair of the Committee of Presidents of Statistical Societies (COPSS), and a former member of the Committee of Applied and Theoretical Statistics of NAS. She is the founding chair of the US Biostatistics Department Chair Group, and the former Coordinating Editor of Biometrics and the founding Co-Editor of Statistics in Biosciences. Dr. Lin is the recipient of the Mortimer Spiegelman Award from the American Public Health Association (2002), the COPSS Presidents’ Award (2006) and FN David Award (2017). She was recognized by the University of Washington as one of the 50 Changemakers in Public Health in 2020. She received the Jerome Sacks Award for Outstanding Cross-Disciplinary Research from the National Institute of Statistical Science (2022), the Marvin Zelen Leadership Award (2022) from Harvard SPH, and the MERIT Award (2007-2015) and the Outstanding Investigator Award (2015-2029) from the National Cancer Institute.

Research Interests

Dr. Lin’s research interests lie in the development and application of scalable statistical and machine learning methods for analyzing massive and complex genetic, genomic, epidemiological and health data. Dr. Lin is renowned for her analytic methods and tools employed in investigating the genetic underpinnings of large-scale Whole Genome Sequencing (WGS) studies, biobanks and Electronic Health Records. The software developed by her lab is widely used for WGS analysis. She gained recognition for her work on COVID-19 epidemic modeling during the early stages of the pandemic. She is also known for her work on parametric and non-/semi-parametric regression for longitudinal data, as well as statistical methods for complex epidemiological and environmental health studies. Dr. Lin’s other research interests in statistical genetics include studying the interplay of genes and the environment, polygenic risk prediction and heritability estimation. She also investigates integrative analysis of different types of data using Mendelian Randomization and causal mediation analysis, as well as studying the impact of variation on functions through single cell genomics and genetic perturbations. She develops methods for testing a large number of complex hypotheses, causal inference, large matrix analysis, prediction models using high-dimensional data. Additionally, she focuses on federated and transferred learning and cloud-based scalable statistical and machine learning analysis.

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Primary Section

Section 32: Applied Mathematical Sciences

Secondary Section

Section 34: Computer and Information Sciences