Genetics and Machine Learning

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

Last updated: 10/9/2026

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:

• Mapping disease-critical cellular contexts through integrating GWAS with functional genomics, including scRNA-seq (Turcan 2026 medRxiv, Zhang*,Hou* 2022 Nat Genet), scATAC-seq, and spatial transcriptomics (Yasumizu 2024 Cell Rep).

• Resolving target genes and cell types at GWAS loci through integrating regulatory functional data, including building graph foundation models (Huang 2024 in revision at Nat Genet), TWAS fine-mapping (Strober 2025 Nat Genet), and analyzing case-control scRNA-seq data with causal inference.

• Understanding the genetic architecture of human diseases and the underlying evolutionary driving forces through analyzing biobank-scale genetics data and functional data (Zhang 2026 Nat Genet).

• Building multi-agent systems for automatic computational biology research (See Turcan et al. 2026 ICLR).

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.