Coverage Path-Planning
This project aimed at developping optimal coverage path-planning algorithms. As futur work, we also plan on developping a CPP planner that considers energetic constraints, …
Jaël Champagne Gareau
Postdoctoral Researcher in Computer Science
I am currently a postdoctoral researcher in computer science at Université
TÉLUQ, where my research focuses on speeding up the conversion of integer and
floating-point numbers into decimal strings. During my doctoral studies, I
designed algorithms and data structures that leverage modern computer
architectures to solve large instances of Markov decision processes (MDPs). In
my master’s research, I developed routing algorithms for electric vehicles
aimed at determining the optimal path between two points while minimizing
travel time (including driving, charging, and expected waiting time at
charging stations).
Download CVInterests
- High-Performance Computing
- Multi-level Parallelism (ILP, DLP, TLP)
- Number to String Conversion Algorithms
- Data Structures and Algorithms
- Artificial Intelligence
- Automated Planning
Education
- Postdoctoral Research in Computer ScienceUniversité TÉLUQ
- PhD Computer Science (AI Focus)Université du Québec à Montréal
- MS Computer Science (AI Focus)Université du Québec à Montréal
- Certificate in software developmentUniversité du Québec à Montréal
- BSc in Pure MathematicsUniversité du Québec à Montréal
Projects
Markov Decision Processes
This project aimed at finding different ways to improve (SSP-)MDP planners performance when considering computer architectures (e.g., cache-memory, parallelism)
Electric Vehicles Planning
This project aimed at creating an EV planner that considers not only the travel time and charging time, but also the waiting time at the charging stations.
Recent Publications
(2026).
Converting an Integer to a Decimal String in Under Two Nanoseconds.
Software: Practice and Experience.
(under review)
(2026).
Converting Binary Floating-Point Numbers to Shortest Decimal Strings: An Experimental Review.
Software: Practice and Experience.
(2025).
Topology-Driven Solver Selection for Stochastic Shortest Path MDPs via Explainable Machine Learning.
Canadian AI 2025.
(2024).
Résolution efficace de processus décisionnels de Markov par l'exploitation d'approches structurelles et algorithmiques tirant parti de l'architecture moderne des ordinateurs.
Université du Québec à Montréal.
(2024).
Increased Plan Stability in Cooperative Electric Vehicles Path-Planning.
ICAPS'24 HAXP Workshop.
(2023).
Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function.
Frontiers in Robotics and AI.