Biography

My research is centered around the study of complex networked systems in which randomness plays a crucial role. More specifically, I study dynamics and rare events in networks affected by uncertainty, drawing motivation from real-world applications in power systems. My work lies mostly in the area of applied probability but has deep ramifications in areas as diverse as operations research, graph theory, and optimization.

My long-term goal as a researcher is twofold. First, I aim to quantify and analyze the randomness emerging in these complex systems using both rigorous mathematical tools and data-driven learning methods. Second, I plan to develop adaptive algorithms and reinforcement learning control strategies to mitigate the impact of high-impact, low-probability events and enhance network robustness.

As the climate crisis exacerbates the frequency and severity of extreme weather events, my research aims to develop a novel and rigorous mathematical understanding of power systems’ resilience against such phenomena, which naturally exhibit pronounced spatial and temporal correlations.

More broadly, I am interested in stochastic dynamics on networks, especially when a non-trivial interplay emerges between the network structure and the system’s randomness, a setting where applied probability, learning, and optimization naturally meet.

I currently (co)supervise several PhD students:

• Erica van der Sar at VU (2021-present) on “Multi-agent RL for power system topology control”
• Chris Franssen at VU (2022-present) on “First-order methods for network optimization”
• Berend Markhorst at VU (2022-present) on “Stochastic and robust optimization for pipe routing design”
• Jobke Janssen at CWI (2023-present) on “Stochastic optimization of frequency reserve markets”

I am currently writing a textbook titled Hands-on Mathematical Optimization in Python together with K. Postek, J. Gromicho, and J. Kantor. Resources and companion code are available here .