Bio

Mouhssine Rifaki is a graduate visiting student in Electrical Engineering at Stanford University. As part of his position at the Arbabian Lab working with Amin Arbabian, he focuses on developing state-of-the-art algorithms that enable adaptive sensing and inference within Physical AI systems. He is the founder of Guardal, a counter-swarm defense company building software that lets small teams defeat swarms of uncrewed systems in communications-limited, contested environments.

Professional Education

  • PhD, Imperial College London, Electrical and Electronic Engineering - 2029 (Expected)
  • MS, ENS Paris-Saclay, Applied Mathematics (MVA) (2026)
  • BS, Sorbonne University, Mathematics (2023)

Contact

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

Additional Info

Links

Current Research and Scholarly Interests

My current research utilizes RL in order to train embodied agents whose perception and foveation adapt while being deployed. I have made the claim that all of an agent's perception, decision-making, and physical actions should be designed simultaneously, as opposed to sequentially. The signal that connects each of the aforementioned components is prediction errors derived from a learned forward model.

In my work, I examine how adaptive sensor systems dynamically transition among different modalities based upon failures within a lightweight world model predicting what will be observed in the near-term future. I also explore foveated perception that devotes its high-resolution attention resources to those regions of space most likely to yield returns under conditions of distribution shift. Lastly, I investigate the design of real-time closed-loop control policies that utilize their dynamic sensing capabilities as input sources for their subsequent actions taken via the same senses.

Projects

  • Adaptive Sensing for Physical AI, Stanford University (4/1/2026 - Present)

    Using fast–slow hierarchical models that use lightweight prediction errors to select modalities and resolutions used by a high-fidelity model. Developed proof-of-concept using multimodal gesture recognition; developed toward tactile sensing as well for robotic manipulation.

    Location

    Stanford, CA

    Collaborators

    • Amin Arbabian, Associate Professor, Stanford University
  • Partner UED for Cooperative Multi-Agent Learning, New York University (3/1/2026 - 9/30/2026)

    PAIRED-style unsupervised environment design over partner policies in cooperative multi-agent learning, rather than over level layouts: the adversary samples co-players from a learned population, and the ego learns to coordinate with whatever partner it draws.

    Location

    New York, NY

    Collaborators

    • Eugene Vinitsky, Assistant Professor, New York University