PhD in Computer Science

ENS Diploma in Computer Science

The Ecole Normale Supérieure is a prestigious institution of higher education providing specialized training to students who will become researchers in their fields.

Master 2 Artificial Intelligence, Systems and Data (IASD)

The IASD Master program aims at training students with a strong theoretical knowledge and a practical experience in AI and Data Science. Its goal is to provide students with a solid and general understanding of modern artificial intelligence, so they can build robust and reliable AI systems. My coursework includes:

  • Advanced databases (non-classical DBMSs)
  • Machine learning fundamentals
  • Optimization for machine learning
  • Data science project
  • Deep learning
  • Knowledge representation, planning, and reasoning
  • Advanced machine learning
  • Computational social choice
  • Data wrangling, data quality
  • Deep learning for image analysis
  • Incremental learning and game theory
  • Knowledge graphs, description logics, reasoning on data
  • Natural language processing

The various projects done during this Master program can be found on this page.

Master 1 in Computer Science

I followed computer science courses during the first semester for this M1. I also followed some neurosciences courses. My coursework includes:

  • Introduction to statistical learning
  • Artificial vision
  • Network algorithms
  • Prediction of robotic movement
  • Information systems security
  • Introduction to linguistic (neurosciences course)
  • descriptive and functional neuroanatomy (neurosciences course)

The various projects done during this program can be found on this page.

Bachelor degree (L3) in Computer Science

My coursework includes:

  • Algorithmic and programming
  • Programming languages ​​and compilation
  • Digital systems: from algorithm to circuit
  • Information theory and coding
  • Introduction to databases
  • Operating systems
  • Random structures and algorithms
  • Formal languages, computability and complexity
  • Introduction to cryptology
  • Introduction to research in computer science
  • Introduction to decision science (neurosciences course)
  • Introduction to computational neurosciences (neurosciences course)

The various projects done during this program can be found on this page.

Classe Préparatoires in Mathematics and Physics

  • Admitted to Ecole Polytechnique
  • Admitted to Ecole Normale Supérieure Paris (4th)


Ranked Delegation in Liquid Democracy, Proportional Rankings

Advisor: Pr. Markus Brill.

Elections with embedded voters

During this internship, I proposed a new model for voting, in which we associate each voter to an embedding that ideally captures similarities between voters. Then, I defined rules that used the geometry of the embeddings, in order to make the election more fair or more proportionnal for instance. I studied the manipulability of the rules, and how this model can be used in practice for algorithm aggregation. See more details on the readthedocs webpage.
Advisors: François Durand and Fabien Mathieu.

A Knowledge Base of Mathematical Results

The basic unit of information of use by researchers in theoretical fields are the mathematical results. We aim to build a knowledge base of these results, using information extraction techniques on scholarly documents. We present an algorithm which extracts mathematical results and references to mathematical results from scientific papers, using their PDF or LaTeX sources. We analyse the results of our algorithm on the whole arXiv database of scientific papers and explore the resulting graph of mathematical results, which contains more than 6 million results and 4.5 million edges. We present attempts to link theorems of different papers using a TFIDF vectorizer or an autoencoder.
Advisor: Pr. Pierre Senellart.

Algorithms and Systems for Computational Social Choice: Incorporating Context into Preference Aggregation

The DBCOMSOC project is the meeting between Databases and Computational social choice. I implemented and optimized algorithms to solve necessary and possible winner problems in the context of partial preferences. I also ran an experiment to collect partial preferences data and proposed a new method to generate artificial data for partial preferences called Random Selection Model.
Advisor: Pr. Julia Stoyanovich.

Make understandable the access controls of social networks.

I built a formal model for access controls on social networks and solve complexity problems on this model. I also created a tool/compiler which enable to do huge queries on the Facebook database. See the dedicated page of this tool.
Advisors: Pierre Bourhis, Romain Rouvoy, Walter Rudametkin.



Tutorials of Algorithm and programmation 3 (Bachelor 2)
Tutorials of Relational databases (Bachelor 3)


Tutorials of Functionnal programming (Bachelor 3)


Tutorials of Functionnal programming (Bachelor 3)