Genetics and Machine Learning

We are looking for postdoctoral researchers, PhD students, and research interns (see openings) !

Last updated: 6/23/2024

We design statistically principled methods, develop user-friendly software, and study the genetic basis of human diseases. We currently focus on integrative analysis of genetics and functional genomics data. Topics of interest include:

• Identifying disease-critical cellular contexts through integrating GWAS and functional genomics.

• Understanding the genetic architecture of human diseases and the underlying evolutionary driving forces through analyzing biobank-scale genetics data.

• Understanding the causal relationship between genes and disease through analyzing GWAS, eQTL, and perturbation data.

• Foundation models for DNA sequences and single cells.

We also develop general statistical and machine learning algorithms motivated by applications in genetics; topics include multiple hypotheses testing, multi-armed bandits, dimensionality reduction, empirical Bayes, and causal inference.