Teaching

At MBZUAI

• Spring 2026 — ML8103: Sequential Decision Making (PhD course). 14-week module (42h). Topics: reinforcement learning, generative flow networks.

• Fall 2025 — ML8101: Foundations of Machine Learning (PhD course). Co-taught with Tongliang Liu. Topics: information theory, variational inference, MCMC, Gaussian processes, Bayesian optimization, Bayesian neural networks.

• Spring 2025 — ML805: Advanced Machine Learning (PhD course). Co-taught with Michalis Vazirgiannis, Tongliang Liu, Yuanzhi Li. Topics: diffusion models, GFlowNets.

• Spring 2025 — ML702: Advanced Machine Learning (MSc course). Co-taught with Eric Moulines and Zhiqiang Shen. Topics: active learning, Bayesian optimization, reinforcement learning. [Interactive Markov Process / MDP simulations]

• Fall 2024 — ML801: Foundations and Advanced Topics in Machine Learning (PhD course). Co-taught with Martin Takac. Topics: reinforcement learning.

• Fall 2024 — MTH703: Mathematics for Theoretical Computer Science (MSc course). Co-taught with Tongliang Liu and Jin Tian. Topics: spectral graph theory, error-correcting codes, linear programming, optimization, information theory.

Intensive Courses and Schools

• November 2025 — Mathematical Foundations of Machine Learning (pre-doctoral, UM6P Morocco). Co-taught with Hachem Madmoun. Linear algebra, probability theory, probabilistic ML, neural networks.

• May 2025 — Deep Learning (Mastère Spécialisé Chef de Projet IA, SKEMA). Week-long course. [Interactive regression visualization]

• February 2025 — Mathematical Foundations of Machine Learning (pre-doctoral, UM6P Morocco). Co-taught with Hachem Madmoun.

• February 2024 — Mathematical Foundations of Machine Learning (pre-doctoral, UM6P Morocco). Co-taught with Hachem Madmoun.

Prior

• 2021 — Teaching Assistant, IVADO / Mila Deep Learning School.
• 2019 – 2020 — Teaching Assistant, IFT 6135 and IFT 6390 (Université de Montréal).
• 2012 – 2015 — Private Instructor for high-school and classes préparatoires (Mathematics, Physics).