Samy-Melwan Vilhes · Ph.D student · LITIS Lab, INSA Rouen
Deep learning for time series: anomaly detection, forecasting, and foundation models for zero-shot forecasting.
Unsupervised learning, Principal Component Analysis and Correspondence Analysis; designed and evaluated student projects in data analysis and signal processing.
Jointly run by Paris-Saclay (Orsay Mathematics) and CentraleSupélec, with support from SaclAI-school.
Machine Learning (supervised, unsupervised, deep, reinforcement), Statistical Learning, High-Dimensional Modeling, Graphical Models, Optimization, Big Data Systems (SQL, HDFS), Online Learning, Conformal Prediction, Data Challenge.
Training-Free Metric for Neural Architecture Search. Explored the Neural Tangent Kernel as a training-free metric for NAS, and compared search strategies to optimize network performance.
Uncertainty Analysis in a Machine-Learning Forecasting Approach. Built a Gradient Boosting model to predict sales of promoted products, with emphasis on uncertainty analysis to improve forecast reliability.
Personalized mathematics tuition, helping students strengthen understanding and improve academic performance.