[PhD in CS @ NYU] [Doctorat info @ NYU]
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As of October 2014, I work as a research scientist at Google DeepMind, specializing in deep learning. Between September 2013 and October 2014, I was an applied data scientist and software engineer at Microsoft Bing in London, focusing on deep learning and statistical language models for search query formulation (AutoSuggest). Between January 2011 and August 2013, I worked as a research scientist in the Statistics and Learning Research Department of Bell Labs (Alcatel-Lucent).

I obtained my Ph.D. in Computer Science at the Courant Institute of Mathematical Sciences at New York University in November 2010, specializing in deep learning and graphical models. My thesis subject was "Time Series Modeling with Hidden Variables and Gradient-Based Algorithms" (defense slides, thesis). My scientific advisor Prof. Yann LeCun leads the Computational and Biological Learning Lab, part of Vision Learning Graphics (Yann LeCun has since become the founder and director of the NYU Center for Data Science and the director of AI at Facebook). My Ph.D. thesis has won the Janet Fabri award for outstanding dissertation in Computer Science.

My main research focused on machine learning for time series modeling using graphical models, dynamic factor graphs and convolutional networks.

I started working on the prediction and propagation analysis of epileptic seizures from EEG and neuronal data (Google Student Award at the 2008 Machine Learning Symposium of the New York Academy of Sciences, Young Investigator Award at the 2009 International Workshop on Seizure Prediction IWSP4, patent-pending). Among others, I collaborated with Dr. Ruben Kuzniecky, Dr. Nandor Ludvig and Dr. Deepak Madhavan from the Comprehensive Epilepsy Center of the NYU Medical Center. Part of my PhD research work was funded by faces (Finding A Cure for Epilepsy and Seizures).

Four additional projects I was involved with were the discovery of gene regulation networks from protein expression micro-array data, in collaboration with Prof. Dennis Shasha and the NYU Plant Biology Lab, the prediction of power transformer failures, in collaboration with NYU-Poly and Con Edison, dynamic topic models and information retrieval from time-stamped text, following a quantitative research internship at Standard & Poor's, and finally, statistical language modeling (this work, which has been conducted at AT&T; Labs, was presented at the IEEE Workshop on Spoken Language Technology).

As a PhD CS student representative at the Courant Student Organization and co-organizer of the first multidisciplinary Courant Student Conference (May 1st, 2009), I won the Henning Biermann award for exceptional contributions to the CS department.

During my studies (Fall 2005 - Fall 2010), in addition to being a Teaching Assistant for Unix Tools, Machine Learning and Honors Compilers, I took the following classes:
- Speech Recognition
- Machine Learning
- Mathematical Foundations of Machine Learning
- Advanced Machine Learning Seminar
- Information Theory and Predictability
- Computer Vision
- Advanced Vision and Augmented Reality
- Mobile Robotics
- Numerical Methods
- Honors Algorithms
- Mathematical Aspects of Neurophysiology
- Mathematical Neuroscience
- Computational Modeling of Neuronal Systems
- Computational Biology
- Time Series Analysis and Statistical Arbitrage
- Capital Markets and Portfolio Theory
- Stochastic Calculus
- Derivative Securities

Some food for thought:

"France's model healthcare system", Boston Globe, August 11, 2007

Rodwin V.G., "The Health Care System Under French National Health Insurance: Lessons for Health Reform in the United States", American Journal of Public Health, vol.93, n.1, 2003