AI, ML & Data
Build predictive models and turn complex datasets into actionable insights.
Sports analytics · Survival prediction · Dashboards
Computer Science Student · AI & Software Development
I build intelligent software, from machine-learning systems to polished mobile products.
Engineering student at Centrale Lyon and Xi'an Jiaotong University, pursuing a dual degree in computer science. Passionate about artificial intelligence, machine learning, and data analysis, I enjoy designing efficient algorithms and developing applications that bridge research and real-world use.
Revelto is a workout tracking app designed for people who take their training seriously. Log every set, follow structured programs, and watch your strength, volume, and personal records climb over time — all backed by an 800+ exercise library and detailed progress analytics. Available now on the App Store and Google Play.
Analyzed professional table-tennis match data to understand how players adapt their returns to incoming serves. Developed machine-learning workflows, performed statistical analysis, and delivered an interactive Plotly Dash dashboard. Presented at MLSA 2025 and published by Springer in CCIS volume 2833 in 2026.
Led the development of a playable Go application with a complete Pygame interface. Built its AI opponent using Minimax, alpha-beta pruning, and Principal Variation Search, alongside tutorial, automatic scoring, and human-versus-computer modes.
Built a machine-learning pipeline for the QRT 2024–2025 Data Challenge to predict overall survival from clinical data. Explored survival-analysis methods and gradient-boosted trees, including LightGBM, for a challenge organized with Gustave Roussy.
Implemented Ant Colony Optimization for graph-based optimization problems. Modeled pheromone updates and probabilistic path selection, then studied how the algorithm converges toward stronger solutions across successive iterations.
Authors: Riad Attou, Marin Mathé, Aymeric Eradès, Romain Vuillemot
Venue: Machine Learning and Data Mining for Sports Analytics (MLSA 2025), Communications in Computer and Information Science, vol. 2833, Springer
This project analyzes table tennis match data with a focus on return shots. The goal is to cluster returns using machine learning techniques and interpret those clusters to better understand players' strategies and behaviors depending on the incoming serve type.
Double degree — M.Sc. Computer Science
Xi'an Jiaotong University — Xi'an, China
General Engineering — Computer Science
Centrale Lyon — Lyon, France
French Preparatory Classes for Grandes Écoles — MPI* track (Mathematics, Physics, Computer Science)
Lycée Janson de Sailly — Paris, France
AI Researcher — Sports Data Analysis
Centrale Lyon & French Table Tennis Federation
Research project to model and predict table tennis return shots from real match data. Designed supervised learning models, performed advanced statistical analysis of professional players, and developed an interactive Plotly Dash dashboard.
Lead Developer & Project Manager
Centrale Lyon
Led a project to design software and an AI for the game of Go. Built an AI based on Minimax with alpha-beta pruning (PVS) and implemented a graphical interface using Pygame.
Build predictive models and turn complex datasets into actionable insights.
Sports analytics · Survival prediction · Dashboards
Ship polished web and mobile products, from interface to backend.
Revelto · Available on iOS & Android
Use agents and model-connected tooling to accelerate engineering workflows.
Context engineering · Implementation · Code review
A solid base in systems, programming languages, and technical workflows.
Algorithms · Systems · Scientific computing
Feel free to reach out — I'm always open to research collaborations, internship opportunities, or just a conversation.