Summary of Research Interests

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

Domain Areas of Interest:

Current Grants as PI and Multiple PI

Past Statistical Grants

Copyright © Xihong Lin, 2010-2023