Rohan Subramanian

I'm a first-year PhD student at NYU Courant advised by Prof. Romain Lopez in the BioML Group and CILVR Lab. My interests lie at the intersection of machine learning and biology. I'm grateful to be supported by the NSF GRFP.

I graduated in 2025 from Harvey Mudd College in Claremont, where I studied computer science with a humanities concentration in music.

I spent a summer at Memorial Sloan Kettering Cancer Center working with Wesley Tansey in Computational Oncology. At Harvey Mudd, I've had the privilege to work with Gabriel Hope on deep generative models and Danae Schulz on parasite gene regulation. Previously, I worked with Debashis Sahoo at UC San Diego, where I investigated Boolean network models of biological systems.

Email  /  CV  /  Scholar  /  Linkedin  /  Github

profile photo

Research Interests

I am broadly interested in machine learning for science. On the machine learning side, I aim to make graphical models and deep generative models more robust and interpretable. In terms of science, I'm interested in applications to single-cell biology, particularly in understanding cancer and inflammatory diseases.

My Work

Publications, presentations, and other research projects.

Probabilistic time series modeling of tumor growth in patient-derived xenografts
Rohan Subramanian, Wesley Tansey
Computational Biology Summer Program Final Symposium, 2024
Poster / Abstract

Semi-supervised learning with constrained deep generative models
Rohan Subramanian, Sahil Rane, Saheli Patel, Gabriel Hope
January 2024-present
Presentation Notes / Code
Boolean networks reveal opposing roles of SLC22A5 and SLC22A15 in inflammatory bowel disease
Rohan Subramanian, Sanjay Nigam, Debashis Sahoo
Cold Spring Harbor Biological Data Science Meeting, 2024
Related manuscript in preparation for JCI submission
Poster / Abstract
Boolean implication analysis of single-cell data predicts retinal cell type markers
Rohan Subramanian, Debashis Sahoo
BMC Bioinformatics, 2022
Paper


Miscellanea

Hacks

 MuddBots: talk to your textbooks (Track Winner, LAHacks 2023)
Breeezy: connect with outdoor enthusiasts (Finalist, HackTech 2024)

Teaching

 Academic Excellence Tutor (2023-present): Introduction to Biology, Molecular Genetics
Teaching Assistant: Principles of CS (FA23), Algorithms (FA24), Neural Networks (SP25)

Inspired by a template from Jon Barron.