I design new ways of using high-dimensional datasets for modeling biological systems. In particular, I'm interested in how machine learning can inform better experiment design for discovering new medicines.
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
Statistical Machine Learning Group.
I previously worked as a machine learning engineer at GSK in Cambridge, MA
where I led a cross-disciplinary team on a project 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
Link 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, 2021.
Link Roohani Y., Sajid N., Hope T., Price C., Madhyastha P., Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI, NeurIPS Machine Learning for Health, 2018
Link Roohani Y., Kiss E., Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability. In: Stoyanov D. et al. (eds) Computational Pathology and Ophthalmic Medical Image Analysis. MICCAI, COMPAY 2018. LNCS, vol 11039. Springer, 2018
Link Shokoohi H., LeSaux M., Roohani Y., Litepio A., Huang C., Blaivas M. Enhanced point-of-care ultrasound applications by integrating automated feature-learning systems using deep learning, J Ultrasound Med., 2018
Link 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. Atmospheric Environment, 2017.