The Lin lab focuses on the development and application of scalable statistical and machine learning methods for the analysis of massive data from the genome, exposome and phenome, including big and complex genetic and genomic, epidemiological and health data. Some examples of the lab's current research areas include analytic methods and applications for large scale Whole Genome Sequencing studies, biobanks and electronic health records, techniques and tools for whole genome variant functional annotations, analysis of the interplay of genes and environment, multiple phenotype analysis, and polygenic risk prediction and heritability estimation. Additional examples include integrative analysis of different types of data, Mendelian Randomization, causal mediation analysis and causal inference, federated and transferred learning, single cell genomics, analysis of epidemiological and complex observational studies, and analysis of COVID-19 epidemic data. The Lin lab's theoretical and computational statistical research includes statistical methods for testing a large number of complex hypotheses, causal inference, statistical and ML methods for large matrices, prediction models using high-dimensional data, federated and transferred learning, cloud-based statistical computing, and statistical methods for complex epidemiological studies.

Dr. Lin's current methodological work is funded by the National Cancer Institute (NCI)'s Outstanding Investigator Award (R35, 2015-2029) and U19 , the National Human Genome Research Institute (NHGRI)'s Impact of Genomic Variation on Function (IGVF) Program U01 , and and the National Heart, Lung and Blood Institute's R01 grant. Her past work was supported by the MERIT award (R37, 2007-2015) and the National Cancer Institute (NCI)'s P01 grant, and the National Human Genome Research Institute's U01 grant.

Statistical Areas of Interest

• Statistical genetics and genomics
• Scalable statistical inference and machine learning for massive data
• Rare variant analysis of whole genome sequencing studies
• Analysis of biobanks
• Federated and transferred learning and meta-analysis
• Pathway and network analysis
• Integrative analysis of different types of data
• Causal inference
• Mendelian Randomization and causal mediation Analysis
• Polygenic risk prediction
• Single cell genomics
• Correlated data (clustered/longitudinal and spatial data)
• Case-control and cohort data
• Nonparametric and semiparametric regression
• Estimating equations and mixed models
• Measurement error

Domain Areas of Interest:

• Genetic epidemiology and environmental genetics and genomics
• Epidemiology, environmental health and population sciences
• Common diseases
• Genes and Environment
• Lung cancer and lung diseases
• Cardiovascular diseases
• Sleep apnea
• Epigenetics

• Principal Investigator (2015-2029), R35 (Outstanding Investigator Award), Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research .
• Principal Investigator (2022-2026), R01, NHLBI Statistical Methods for Integrative Analysis of Large-Scale Multi-Ethnic Whole Genome Sequencing Studies and Biobanks of Common Diseases.
• Multiple Principal Investigator (2021-2026), U01, NHGRI Predictive Modeling of the Functional and Phenotypic Impacts of Genetic Variants.
• Multiple Principal Investigator (2017-2023), U19, NCI Integrative analysis of lung cancer etiology and risk.
• Principal Investigator (2009-2025), T32, NIGMS, Interdisciplinary training: Statistical Genetics/Genomics and Computational Biology.

• Contact Principal Investigator (2008-2018), PO1, NCI, Statistical Informatics in Cancer Research.
• Contact Principal Investigator (2016-2022), U01, NHGRI, Harvard Analysis Center of the Genome Sequencing Program of the National Human Genome Research Institute (NHGRI) "Powering whole genome sequence-based genetic discovery for common human diseases."
• Principal Investigator (2009-2014), T32, NIGMS, Joint Interdisciplinary Training in Biostatistics and Computational Biology.
• Principal Investigator (2007-2015), R37 (MERIT Award), NCI, Statistical Methods for Correlated and High-Dimensional Biomedical Data.
• Principal Investigator (2002-2007), R01, NCI, Statistical Methods for Correlated Biomedical Data.
• Principal Investigator (1997-2002), R29 (FIRST Award), NCI, New Mixed Effects Models for Correlated Biomedical Data.
• Principal Investigator (2001-2008)/co-PI (2008-2013), R13, NCI, Workshop for Junior Biostatisticians in Cancer Research.
• Principal Investigator (2006-2016), R13, NCI, Conferences on Emerging Statistical Issues in Biomedical Research.