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Machine Learning Researcher - Ph.D. in Applied Mathematics
Machine learning researcher with a particular interest in deep reinforcement learning -- Ph.D in Mathematics. Most of my fundamental research in machine learning focused on first order optimization, generalization and large scale learning. I worked on the connection between continuous first order optimization algorithms and dynamical system in order to analyze the fine properties of these algorithms. I also studied the generalization properties of stochastic gradient Langevin dynamics in non convex learning. From a more applied perspective, I worked on implementing efficient and scalable hyper-parameter optimization algorithms. More recently I have been working on controlling oscillatory systems using deep reinforcement learning.
École Polytechnique
Doctor of Philosophy (Ph.D.), Computational and Applied Mathematics
January 1, 2009 – January 1, 2012
Pierre and Marie Curie University
M2, Analyse numérique et équation aux dérivées partielles
January 1, 2008 – January 1, 2009
City University of Hong Kong
M2, Mathematics for Finance and Actuarial Science
January 1, 2007 – January 1, 2008
DeepMind
Research Engineer
June 1, 2022 – Present
Toronto, Ontario, Canada
LG Electronics
Senior Machine Learning Researcher
January 1, 2020 – June 1, 2022
Toronto, Ontario, Canada
Borealis AI
Machine Learning Researcher
April 1, 2017 – September 1, 2019
Toronto, Canada Area
University of Toronto
Postdoctoral research fellow
July 1, 2014 – April 1, 2017
Région de Toronto, Canada
Inria
Postdoctoral research fellow
February 1, 2013 – June 1, 2014
Villeneuve d'Ascq (59), France
Ecole Polytechnique
Doctor of Philosophy (Ph.D) in Applied Mathematics
January 1, 2009 – January 1, 2012
Palaiseau (91), France
Statistical Inference
Coursera Course Certificates
June 24, 2026 – Present
Getting and Cleaning Data
Coursera Course Certificates
June 24, 2026 – Present
The Data Scientist’s Toolbox
Coursera Course Certificates
June 24, 2026 – Present
Reproducible Research
Coursera Course Certificates
June 24, 2026 – Present
R Programming
Coursera Course Certificates
June 24, 2026 – Present
Exploratory Data Analysis
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily skewed towards academic research and advanced machine learning engineering, which may not directly align with a typical 'Data Analyst' role focused on business intelligence, reporting, and stakeholder communication. While the analytical rigor is high, the project diversity and explicit experience in standard data analysis tools (e.g., SQL, Tableau, Power BI) are not evident. The certifications in R programming and data science fundamentals are positive but recent, suggesting a potential transition towards more applied data analysis. The fit for a pure Data Analyst role is moderate, leaning towards a more research-oriented or advanced analytics position.
Soft Skills & Operational Fit
The candidate's extensive research and academic background suggests strong analytical thinking, problem-solving, and independent work capabilities. Experience in lecturing and coordinating courses indicates good communication and organizational skills. However, the resume lacks explicit descriptions of collaborative project work or leadership in a typical corporate data analyst setting, making it difficult to fully assess operational fit beyond research environments.