Nadine Hussami
 |
Advisor: Professors Robert Tibshirani and Anshul Kundaje You can find more information in my Resume. |
Research Interests
My main research interests include statistical learning and convex optimization, with applications in genomics. I'm interested in developing methods that are able to deal with correlated features. My work has been focused on exploiting sparsity and variable clustering in high-dimensional regression problems such as phenotypic expression modeling. Moreover, I worked on developing a hierarchical boosting method to model gene regulatory networks in different cell types.
Experience
Software and algorithm development engineer, Bina Technologies (Acquired by Roche, 2014).
Intern in the algorithms research group, Mitsubishi Electric Reasearch Laboratories (MERL).
Publications
Peyton Greenside, Nadine Hussami, Jessica Chang and Anshul Kundaje, PyBoost: A parallelized Python implementation of 2D boosting with hierarchies, BioArxiv, 2017.
Aaron B. Wagner, Amine Laourine and Nadine Hussami, Degradedness and secrecy in memoryless queues, Submitted to IEEE Transactions on Information Theory, 2016.
Nadine Hussami and Robert Tibshirani, A Component Lasso, The Canadian Journal of Statistics, 2015.
An integrated Encyclopedia of DNA Elements in the Human Genome, The ENCODE Project Consortium, Nature, 2012.
Nadine Hussami, Satish Korada and Rudiger Urbanke, Polar Codes for Channel and Source Coding, International Symposium on Information Theory (ISIT), 2009.
Teaching
Teaching Assistant at Stanford:- CS273B Deep Learning in Genomics and Biomedicine
- STATS 345 Statistical and Machine Learning Methods for Genomics
- EE278B Introduction to Statistical Signal Processing
- EE178/278A Probabilistic Systems Analysis
- CS262 Computational Genomics
- CS224W Social and Information Network Analysis