Yusuf Roohani

About:

I design new ways of using high-dimensional datasets for modeling and perturbing biological systems. In particular, I'm interested in how machine learning and graph based data representations 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 Stanford 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]

Publications:

Google Scholar page

Journal Publications

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.

Conference Papers

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