Mikael Henaff was a Research Scientist at Facebook AI, where he focused on machine learning, deep learning, and reinforcement learning. His research explores how intelligent systems can learn from sequential data, maintain and use memory, reason about changing environments, and make decisions through model-based learning. His publications include work on model-predictive policy learning, recurrent entity networks, neural-network loss surfaces, and exploration in continuous state spaces.

Before joining Facebook AI as a research scientist, Mikael was a postdoctoral researcher at Microsoft, conducting research in deep reinforcement learning and imitation learning. He completed his doctoral research at New York University and held several research internships with Facebook AI. Earlier in his career, he worked as a scientific programmer at NYU Langone Medical Center, where he helped design large-scale computational experiments involving biomedical data, causal discovery, classification, and feature selection, and co-authored five peer-reviewed research publications.

Mikael holds a PhD in computer science and a master’s degree in mathematics from New York University, as well as a bachelor’s degree in mathematics from the University of Texas at Austin. His combination of mathematical training, academic research, and applied experience provides a strong foundation for developing AI systems that can reason, learn from experience, and act effectively under uncertainty.