Assistant Professor of Biostatistics & Bioinformatics
Rohit Singh is an Assistant Professor in the Departments of Biostatistics & Bioinformatics and Cell Biology at Duke Univ. His research interests are broadly in computational biology, with a focus on using machine learning to make drug discovery more efficient. Currently, he's exploring how single-cell genomics and large language models can help decode disease mechanisms and aid in identifying new targets and drugs. He is the recipient of the Test of Time Award at RECOMB, MIT's George M. Sprowls Award for his PhD thesis in Computer Science, and Stanford's Christopher Stephenson Memorial Award for Masters Research in the same field. In addition to academia, he has experience in the industry.
Appointments and Affiliations
- Assistant Professor of Biostatistics & Bioinformatics
- Assistant Professor of Cell Biology
- Assistant Professor in the Department of Electrical and Computer Engineering
- Assistant Professor of Computer Science
- Member of the Duke Cancer Institute
Contact Information
- Email Address: rohit.singh@duke.edu
- Websites:
Education
- Ph.D. Massachusetts Institute of Technology, 2012
Research Interests
Drug discoveries have been instrumental in improving global health over the last century, but the median drug now takes about 10 years to bring to market and costs over a billion dollars to develop. My lab aims to expedite the development of precise diagnostics and therapeutics by applying machine learning. Our current work is broadly along two directions. Along the first direction, we use single-cell multiomics to discover regulatory mechanisms governing the interaction between the epigenome, transcription factors, and target genes. This approach relies on methodological innovation, developing new Granger causal inference techniques to capitalize on the “parallax” between simultaneous but separate measures of cell state. In the other direction, we apply large language models to model protein interaction and function. These protein language models enable powerful new approaches to predicting and understanding protein-protein and protein-drug interactions.
Courses Taught
- ECE 493: Projects in Electrical and Computer Engineering
- ECE 391: Projects in Electrical and Computer Engineering
- COMPSCI 394: Research Independent Study
- COMPSCI 393: Research Independent Study
- CELLBIO 493: Research Independent Study
In the News
- AI and Advanced Computing Accelerate Scientific Research at Duke (Aug 21, 2026)
- An AI Invention that Accelerates Drug Discovery (Apr 24, 2025)
Representative Publications
- Su, Jin, Zhikai Li, Tianli Tao, Chenchen Han, Yan He, Fengyuan Dai, Qingyan Yuan, et al. “Democratizing protein language model training, sharing and collaboration.” Nat Biotechnol 44, no. 10 (October 2026): 1628–34. https://doi.org/10.1038/s41587-025-02859-7.
- Liang, Huan, Bonnie Berger, and Rohit Singh. “Foundation model reveals the shared organization of transcription and topologically associating domains.” Cell Syst 17, no. 9 (September 16, 2026): 101675. https://doi.org/10.1016/j.cels.2026.101675.
- Devkota, Kapil, Daichi Shonai, Joey Mao, Young Su Ko, Wei Wang, Scott Soderling, and Rohit Singh. “Miniaturizing and modifying natural proteins with Raygun.” Nature 657, no. 8133 (September 2026): 1064–72. https://doi.org/10.1038/s41586-026-10842-8.
- Liang, Huan, and Rohit Singh. “COMPASS: Component-Wise Inference of Shared and Gene-Specific Perturbation Response.” OpenRxiv, August 6, 2026. https://doi.org/10.64898/2026.08.03.742643.
- Catrina, Darius, Christian Bepler, Samuel Sledzieski, and Rohit Singh. “Reverse Distillation: Consistently Scaling Protein Language Model Representations,” March 8, 2026.