Fabien TEYTAUD

Fabien TEYTAUD is a Maître de Conférences (Associate Professor) with Habilitation à Diriger des Recherches (HDR) in computer science, indicating his qualification to supervise PhD students within the French academic system. His research spans multiple interconnected domains within artificial intelligence and computational optimization.

Dr. TEYTAUD's primary research interests include:

• Evolutionary algorithms and black-box optimization techniques
• Monte Carlo Tree Search and game AI applications
• Deep learning architectures, particularly Generative Adversarial Networks
• Multi-objective and multi-modal optimization approaches
• Applications of machine learning in agriculture and computer graphics

His recent publications demonstrate a strong focus on improving optimization techniques through novel approaches that combine evolutionary algorithms with Bayesian methods, bandit algorithms, and deep learning architectures. He has made significant contributions to noise characterization in Monte Carlo rendering, hyperparameter optimization, and diverse image generation.

Dr. TEYTAUD has an extensive publication record in top-tier conferences including GECCO, ICMLA, PPSN, and IEEE CEC, with consistent contributions from 2014 through 2022. His collaborative work spans multiple French institutions and international research teams, reflecting his active engagement in the global optimization and AI research community.