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I have recently completed an internship as data scientist at Destygo - Chatbots for travel companies, working on natural language understanding models for conversational agents, specially for text classification and named entity recognition. I also worked on approaches using word and sentence embeddings and on data programming techniques. I have previously worked at J.P.Morgan in their Credit Quantitative Research team and at Amazon Web Services in their AWS EC2 team. I am currently studying at École Normale Supérieure Paris-Saclay and have studied at École Polytechnique and Instituto Tecnológico de Aeronáutica. |
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I'm interested in using machine learning algorithms to solve challenging real-world problems. In Natural Language Understanding, I want to understand how to make computers learn complex new problems from relatively few examples. Key areas of interest include deep learning, meta learning, probabilistic graphical models, and reinforcement learning. I usually publish my projects on Github and some of them are discussed in the blog section below in the page. |
 
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Used the Mapper algorithm and other machine learning techniques to visualize NBA player's data, in order to understand playing styles and identify new field positions, following a previous work of M. Alagappan. |
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ML: Statistical Learning, Reinforcement Learning, Convex Optimization, Probabilistic Graphical Models, Kernel Methods, Deep Learning, Face à la malédiction de la dimensionnalité Data/Modeling: ML for text and graph data, Object recognition and Computer Vision, Audio signal processing |
 
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Graduate coursework: Machine Learning, Computer Vision, Natural Language Processing, Database Management Systems, Topological Data Analysis |
 
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In 2017-2018, I took a sabbatical year to do internships in quantitative fields at J.P.Morgan and Destygo. Before that, I had worked as software engineer at Amazon and Keyrus for 5 and 3 months, respectively. |
 
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Key machine learning areas: intent classification, named entity recognition, word and sentence embeddings, data programming Key technical skills: Python, TensorFlow, Keras, Flask, PostgreSQL, Ruby on Rails |
 
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Key areas: time series analysis, unsupervised learning, Corporate bonds and Credit Default Swaps Key technical skills: Python, Statsmodels, PostgreSQL |
 
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Key areas: web development, resource automation Key technical skills: Java, Spring, Ruby on Rails, DynamoDB |
 
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Key areas: data engineering, machine learning as a service Key technical skills: Python, Scala, Spark |
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I have long participated in competitions that made me learn more about interesting areas and pushed me to go further than basic understanding. In particular, I have competed in Mathematics Olympiads in high school and as undergraduate, as well as in Algorithms and Programming competitions, achieving some very good results. |
 
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Google Code Jam is an international programming competition in which each round consists of a set of algorithmic problems |
 
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SWERC is a 5-hour on-site programming contest for teams of three students (see regulations), focused on algorithmic problem solving and practical coding. It is open to teams from France, Israel, Italy, Portugal, Spain, and Switzerland |
 
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The IMC is an annual mathematics competition open to all undergraduate students of mathematics, organized by University College London usually taking place in Blagoevgrad, Bulgaria |
 
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The International Mathematical Olympiad (IMO) is the World Championship Mathematics Competition for High School students and is held annually in a different country. The first IMO was held in 1959 in Romania, with 7 countries participating. It has gradually expanded to over 100 countries from 5 continents. |
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Dancing Links: Donald Knuth's Algorithm X for the exact cover problem |
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