teaching

Throughout my studies and PhD, I have been involved in several courses as teaching assistant, guest lecturer, and coordinator.

Workgroup Coordinator & Guest Lecturer

  • Bayesian Machine Learning (M.Sc. Artificial Intelligence, 2024-2025)
    • Modern probabilistic (in particular, Bayesian) inference in machine learning: students learn theory and practice about topics including Gaussian processes, Bayesian neural networks, MCMC, Bayesian optimization.
    • Coordination: Designed the complete practical track, including weekly assignments, practical sessions, and the final capstone project.
    • Lecturing: Delivered a guest lecture on Decision-making under Uncertainty, which received a student rating of 9.7/10.

Student Supervision

  • Marco Post (M.Sc.), main supervisor - Transfer Learning in High-Dimensional Bayesian Optimization
  • Luca Pattavina (M.Sc.), co-supervisor - Multi-objective Bayesian Optimization for Laser Dicing
  • Adem Kaya (M.Sc.), co-supervisor - Efficient Learning of Neural Policies under Partially Observability.
  • Bart van Hees (B.Sc), main supervisor. Dynamic Hyperparameter Adaptation in Reinforcement Learning.
  • Mark Looman (B.Sc), main supervisor. Safe Optimization of PID controllers using SafeOPT.
  • Konstantinos Konstantinou (B.Sc), main supervisor. Gaussian Process Classification for Shot-Make Probability in Basketball.
  • Kamil Kuit (B.Sc), main supervisor - LLM detection using Gaussian Processes.
  • Thijs van Schaik (B.Sc), main supervisor. Safe RL via Probabilistic Shielding with Gaussian Processes.

Guest Lectures

  • Deep Reinforcement Learning with Dynamical Models - Complex Adaptive Systems (M.Sc.), 2025
  • Model-based Control and Reinforcement Learning - Complex Adaptive Systems (M.Sc.), 2023, 2024
  • Building Smart Machines: A Dive Into Reinforcement Learning - Radboud College Experience, 2024, 2025
  • Introduction to Deep Reinforcement Learning - Radboud College Experience, 2023

Teaching Assistant

  • Neural Information Processing Systems (M.Sc. Artificial Intelligence; 2021-2022, 2023-2024, 2024-2025)
    Focus on dynamical systems, control theory, and deep learning.
    Contributions: guest lectures, designing weekly assignments and final projects.
  • Data Mining (B.Sc. Computing Science; 2019-2020, 2020-2021)
    Covering clustering, classification, and pattern mining algorithms.
  • Bayesian Statistics (B.Sc. Artificial Intelligence; 2019-2020)
    Introduction to Bayesian inference and probabilistic programming.
  • Academic and Professional Skills (B.Sc. Artificial Intelligence; 2018-2019)
    Mentoring first-year students in academic writing, presentation skills, and research ethics.