Yusuf Roohani
Yusuf Roohani
About:
I design new machine learning approaches for modeling biological systems. In particular, I'm interested in how artificial intelligence can inform better experiment design for biological discovery. My recent work has focused on computationally guiding the engineering of cells using genetic perturbations.
Currently, I lead a machine learning group at the Arc Institute, where we are working on building a virtual cell by combining large AI models with massively scaled data generation. Previously, I completed my PhD at Stanford University under the guidance of Jure Leskovec and Stephen Quake. I retain a visiting affiliation with the Stanford Department of Computer Science.
firstname.lastname@arcinstitute.org
Curriculum Vitae:
[CV]
Arc Machine Learning Group Members
- Rohan Shah (UPenn CS undergraduate)
- Dhruv Gautam (UC Berkeley EECS MS student)
- Mingze Dong (Yale Comp Bio PhD student)
- Abhinav Adduri (Arc ML Research Scientist)
- Beatrice Bevilacqua (Arc ML Research Scientist)
- Hiring ML Research Scientists and Research Fellows! Email me
Past Members
- Alishba Imran (UC Berkeley EECS undergraduate, now at EvolutionaryScale)
- Andrew Lee (Stanford CS MS student)
- Sanjay Nagaraj (Stanford CS MS student)
Featured Publications:
For complete list: Google Scholar
Journal Publications
Cell [2025]
Virtual Cell Challenge: Toward a Turing test for the virtual cell
Roohani, Y., Hua, T., ... Goodarzi, H., Burke D..
Cell [2024]
How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
Bunne, C.*, Roohani, Y.*, Rosen, Y.*, ... Regev, A., Lundberg, E., Lekovec, J., Quake S..
Nature Methods [2023] Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN
Rosen Y.*, Brbic M.*, Roohani, Y.*, Swanson K., Li Z., Leskovec, J..
Nature Biotechnology [2023] [Code]
GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations
Roohani, Y., Huang, K., Leskovec J.
    Oral presentations and Awards:
     [Best Poster] Intelligent Systems For Molecular Biology (ISMB 2022)
     [Innovation Award] Society for Lab Automation and Screening (SLAS 2023)
     [GitHub 200+ Stars]
(* = equal contribution)
Preprints
bioRxiv [2025]
Predicting cellular responses to perturbation across diverse contexts with STATE
Adduri, A., Gautam, D., ... , Goodarzi, H., Roohani, Y..
bioRxiv [2025]
scBaseCamp: An AI agent-curated, uniformly processed, and continually expanding single cell data repository
Youngblut, N., Carpenter, C., ... , Goodarzi, H.*, Roohani, Y.*.
bioRxiv [2024] [Website]
PreciCE: Precision engineering of cell fates via data-driven multi-gene control of transcriptional networks
Magnusson, J.*, Roohani, Y.*, Stauber, D., ... Sandberg, R., Lekovec, J., Lei, S. Qi.
bioRxiv [2023] [Code] Universal Cell Embeddings: A Foundation Model for Cell Biology
Rosen Y.*, Roohani Y.*, Agrawal A., Samotorcan L., Quake S., Leskovec J..
     [Media coverage] New York Times
     [Stanford BioX Interdiscplinary Initiatives Poster Award]
Conference Papers
ICLR [2025] BioDiscoveryAgent: An AI agent for designing genetic
perturbation experiments
Roohani Y.*, Vora J.*, Huang Q.*, Steinhart Z., Marson A., Liang P., Leskovec J..
     [Best Poster] ICLR 2024 MLGenX Workshop
CLeaR [2025], Nat. Comms. Biology[2025]
CausalBench: A Large-Scale Benchmark for Network Inference from Single-Cell Perturbation Data
Chevalley, M., Roohani, Y., Mehrjou, A., Leskovec, J., Schwab, P..
NeurIPS [2023] Zero-shot causal learning
Nilforoshan H.*, Moor M.*, Roohani Y., Chen Y., Surina A., Yasunaga M., Oblak S., Leskovec J..
     [Spotlight Presentation]