Research

We work in close collaboration with the Roussos Lab at the Center for Disease Neurogenomics. We are primarily focused on developing methodologies for understanding the genetic basis of neuropsychiatric and neurodegenerative disorders.

Single Cell Neurogenomics

Single-cell data is expanding in terms of depth and breadth; the current technology allows simultaneous measurement of multi-omic profiles from the same cell across a large breadth of samples. As a result, data analysis is becoming computationally demanding and requires more sophisticated methods such as machine learning to capture the underlying biological and pathological processes at the cellular level. The team will oversee the analysis of single-cell data, including but not limited to transcriptomic, regulatory, and proteomic profiles generated at the center. The team will build expertise in developing and adopting computationally efficient and effective algorithms to better understand the etiology and progression of neuropsychiatric and neurodegenerative disorders.

Machine Learning

We frame biological problems using mathematical and statistical tools. Leveraging the wealth of single-nucleus and functional genomic data, we apply interdisciplinary approaches like statistical modeling and machine learning to interpret disease genomes.

Regulatory Networks and Dynamics

Integrative approaches can uncover meaningful patterns and rules from large data. We identify cis-regulatory elements, build gene regulatory networks, and discover regulatory dynamics during transcription.