Bio

Mouhssine Rifaki is a Graduate Visiting Researcher in Electrical Engineering at Stanford University, working in the Arbabian Lab with Amin Arbabian on algorithmic architectures for adaptive sensing and inference in physical AI systems. He holds an MSc from ENS Paris-Saclay (Mathematics, Vision, Learning – MVA) and a BSc in Mathematics from Sorbonne University. His research aims to show that exploiting latent structure in Markov decision processes can reduce the sample complexity of reinforcement learning by orders of magnitude, enabling agents that learn from minimal data.

Professional Education

  • MSc, ENS Paris-Saclay, Statistics and Machine Learning - MVA (2026)
  • BSc, Sorbonne University, Mathematics (2023)

Contact

  • Academic
    mrifaki@stanford.edu
    University - Student Department: Electrical Engineering Position: Graduate

Additional Info

Current Research and Scholarly Interests

My research aims to show that exploiting latent structure in Markov decision processes can reduce the sample complexity of reinforcement learning by orders of magnitude, enabling agents that learn from minimal data.

I focus on low-rank matrix completion to recover Q-functions from sparse samples via leveraged CUR decompositions; two-to-infinity subspace recovery for the row-wise error control required in policy extraction; score matching in learned latent spaces for diffusion world models with planning guarantees that scale with intrinsic dimension; kernel spectral methods for continuous block MDP decoding; and reducing the demonstration complexity of imitation learning when the expert’s Q-function has low-rank structure.