Clément Tamines

Clément Tamines

PhD Candidate in Computer Science

University of Mons

Biography

I am a PhD student in the Computer Science department at the University of Mons (UMONS) in Belgium. I am co-supervised by Véronique Bruyère (UMONS) and Jean-François Raskin (Université libre de Bruxelles). My research interests regard solving parity and generalized parity games efficiently and notions of equilibria in non-zero-sum multiplayer games. I am also very interested in the field of machine learning.

Interests
  • Algorithmic Game Theory
  • Equilibria Notions in Games
  • Reactive System Synthesis
  • Formal Verification
  • Machine Learning
Education
  • PhD in Computer Science, 2022

    University of Mons

  • University Certificate in AI, 2020

    University of Mons

  • MSc in Computer Science, 2018

    University of Mons

  • BSc in Computer Science, 2016

    University of Mons

Experience

 
 
 
 
 
PhD in Computer Science
Oct 2018 – Present Mons, Belgium
  • PhD thesis on equilibira in non-zero-sum games.
 
 
 
 
 
Teaching Assistant
Sep 2018 – Present Mons, Belgium
  • Directed the project for the course Algorithmics and Bioinformatics (Java 8 program for DNA fragment assembly).
  • Obtained very positive student evaluation (good to excellent).
  • Supervised 2 research internships on symbolic solving of games using Binary Decision Diagrams.
  • Advised a Master student for a project on window techniques for solving parity games.
 
 
 
 
 
Software Developer Intern
Sep 2017 – Nov 2017 Tubize, Belgium
  • Designed and implemented a constraint programming model for network generation in a radio configuration tool (using Java 8 and the Choco Solver constraint programming library).
 
 
 
 
 
Student Teaching Assistant
Sep 2016 – May 2018 Mons, Belgium
  • Tutored students for the Programming and Algorithmics I & II courses (Python 3 & Java 8).

Publications

(2021). Stackelberg-Pareto Synthesis. CONCUR 2021.

PDF Cite

(2021). Stackelberg-Pareto Synthesis (Full Version). CoRR.

PDF Cite

(2019). Partial Solvers for Generalized Parity Games. RP 2019.

PDF Cite DOI

(2019). Partial Solvers for Generalized Parity Games (Full Version). CoRR.

PDF Cite

Talks

Stackelberg-Pareto Synthesis
Stackelberg-Pareto Synthesis
Stackelberg-Pareto Synthesis

Projects

.js-id-Deep-Learning
SPORE (Symbolic Partial sOlvers for REalizability)

SPORE (Symbolic Partial sOlvers for REalizability)

A prototype symbolic implementation of partial solvers for (generalized) parity games applied to LTL realizability.

Time Series Forecasting Using Neural Networks and Statistical Models

Time Series Forecasting Using Neural Networks and Statistical Models

The goal of this project is to forecast future web traffic for Wikipedia articles using different techniques ranging from statistical models to deep neural networks.

Forest fire detection using CNN

Forest fire detection using CNN

Using convolutional neural networks (CNN) to detect the presence or the start of a forest fire in an image. This model could be applied to detect a fire or a start of a fire from (aerial) surveillance footage of a forest.

Game solver

Game solver

Python implementation of reachability/safety games solver as well as weak parity and strong parity games solver.