Yusuf Roohani

About:

I design new machine learning approaches for modeling biological systems. In particular, I'm interested in how artificial intelligence can leverage functional genomics experiments for discovering new therapeutic interventions. My recent work has focused on computationally guiding experiments to reengineer cell identity.

I'm currently a PhD student at Stanford University advised by Jure Leskovec and Stephen Quake. I'm affiliated with the Department of Biomedical Data Science, the Stanford AI Lab and the Stanford Machine Learning Group.

I previously worked as a machine learning engineer at GSK where I led a cross-disciplinary team to biologically profile their 2M+ compound collection using complex multi-modal datasets and high throughput screening. I enjoyed the four years that I spent in industry designing robust machine learning systems that fit a healthcare context - from discovery all the way to diagnostics.

Curriculum Vitae:

[CV]

Publications:

Google Scholar page

Preprints

bioRxiv [2022] [Code] Roohani, Y., Huang, K., Leskovec J. GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations

arXiv [2021] Bommasani, R., Hudson, D. A., ... Roohani, Y., ... Liang, P. On the opportunities and risks of foundation models

Conference Papers

NeurIPS [2021] Huang K., Fu T., Gao W., Zhao Y., Roohani Y., Leskovec J., Coley C., Xiao C., Sun J., and Zitnik M.. Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development.

NeurIPS Machine Learning for Health [2018] Roohani Y., Sajid N., Hope T., Price C., Madhyastha P., Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI.

Journal Publications

Atmospheric Environment [2017] Roohani, Y., Roy, A., Heo, J., Robinson, A., Adams, P. Impact of natural gas development in the Marcellus and Utica Shales on regional ozone and fine particulate matter levels.