I'm a PhD student at the Stanford AI Lab (SAIL), advised by Ron Dror, where I develop ML methods for molecular biology applications. Previously, I graduated from the University of California, Berkeley, where I earned my Bachelor's and Master's degrees in EECS.
At Berkeley, I was a member of the Yosef Lab, where I developed VAE models for single-cell genomics data as part of the scvi-tools team. Previously, I built RNA design algorithms and benchmarks in Rhiju Das's Lab. I've also interned at Microsoft Research (advised by Eric Horvitz and Bruce Wittmann), Google, and Atomic AI.
In my free time, I enjoy running, playing soccer, and speed cubing. I support the Golden State Warriors and SF Giants, and have recently gotten into Formula 1. I also have a blog.
Geometric deep learning for structure-based ligand design
Alexander S. Powers, Helen H. Yu, Patricia Suriana, Rohan V. Koodli, Tianyu Lu, Joseph M. Paggi, and Ron O. Dror
ACS Central Science, November 2023
MultiVI: deep generative model for the integration of multimodal data
Tal Ashuach*, Mariano I. Gabitto*, Rohan V. Koodli, Giuseppe-Antonio Saldi, Michael I. Jordan, Nir Yosef
Nature Methods, June 2023
PolyVI: Deep Generative Models for Gene Expression, Chromatin Accessibility, and Surface Protein Expression Data
Rohan V Koodli
Master's Thesis, May 2022
Redesigning the Eterna100 for the Vienna 2 folding engine
Rohan V Koodli, Boris Rudolfs, Hannah K Wayment-Steele, Eterna Structure Designers, Rhiju Das
bioRxiv, August 2021
EternaBrain: Automated RNA design through move sets and strategies from an Internet-scale RNA videogame
Rohan V Koodli, Benjamin Keep, Katherine R Coppess, Fernando Portela, Eterna participants, Rhiju Das
PLOS Computational Biology, June 2019
Paper Code Cover art
A Guide to the Berkeley EECS 5th Year Master's Program