I design new machine learning approaches for modeling biological systems. In particular, I'm interested in how artificial intelligence can leverage functional genomics epxeriments 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
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]
Google Scholar page
bioRxiv  [Code] Roohani, Y., Huang, K., Leskovec J.
GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations
arXiv  Bommasani, R., Hudson, D. A., ... Roohani, Y., ... Liang, P.
On the opportunities and risks of foundation models
NeurIPS  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  Roohani Y., Sajid N., Hope T., Price C., Madhyastha P., Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI.
Atmospheric Environment  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.