RESEARCH PROJECTS
SPIEL: Statistics- and Physics-Inspired methodologies for Equilibrium Learning
Despite the recent spectacular successes of artificial intelligence (AI) – from generative models to self-driving cars – the machine learning (ML) algorithms that power them remain poorly understood, especially when interacting with each other.
Neuromorphic Perception
Modern vision systems are powered by standard frame-based cameras and deep learning architectures with enormous capacity and sample complexity.
DRNL – Deductive Reasoning in Natural Language
We will combine deep learning with deductive reasoning from the symbolic Artificial Intelligence tradition, to produce new algorithms capable of reaching conclusions requiring multiple inference steps, using premises and conclusions expressed in natural language (NL).
Latent Markov Decision Processes and Reinforcement Learning
We propose to study sequential decision making problems where the environment is unknown, and moreover, where the environment can only be partially observed.
