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.

Curriculum Vitae:

[CV]

Current Team Members

Michal Gerasimiuk (Stanford Computer Science PhD student)
Ayush Agrawal (Stanford Computer Science undegraduate)
Sanjay Nagaraj (Stanford Computer Science undergraduate)
Kamal Obbad (Stanford MD-PhD student)
Mohsen Naghipourfar (UC Berkeley Bioengineering PhD student)
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Featured Publications:

For complete list: Google Scholar

Journal Publications

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)
     [Best Poster] [Video] Single-Cell Genomics Meets Data Science 2022
     [Video] Machine Learning for Computational Biology 2022 (MLCB2022)
     CRISPR Perturbations and Beyond 2022 (Wellcome Sanger Institute)
     [GitHub 150+ Stars]

(* = equal contribution)

Preprints

arXiv [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]

arXiv [2022] CausalBench: A Large-Scale Benchmark for Network Inference from Single-Cell Perturbation Data
Chevalley, M., Roohani, Y., Mehrjou, A., Leskovec, J., Schwab, P..

Conference Papers

ICLR [2024]: LLMs for Agents Workshop, MLGenX Workshop
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

NeurIPS [2023] Zero-shot causal learning
Nilforoshan H.*, Moor M.*, Roohani Y., Chen Y., Surina A., Yasunaga M., Oblak S., Leskovec J..
[Spotlight Presentation]