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
.

- applied probability
- stochastic networks
- rare events analysis
- network optimization
- reinforcement learning
- power systems resilience

From March 2021, I have been an affiliate researcher in the Stochastic Group at
CWI Amsterdam
.

Funded by my own **NWO Rubicon postdoctoral grant**: *Renewables and uncertainty in future power systems: Mathematical challenges and solutions*

I worked with Adam Wierman and Steven Low at the Computing and Mathematical Sciences department. I also joined as an affiliate postdoctoral fellow the Resnick Institute for Sustainability .

Funded by the NWO VICI grant *Rare Events: Asymptotics, Algorithms, Applications* of
Bert Zwart

Supervised by
Sem Borst
and
Johan van Leeuwaarden

Adaptive topology control in power systems using reinforcement learning and spectral clustering.

Understanding the fundamental interplay between network topology and failure propagation in power systems using graph theory and spectral methods

Developing novel sampling methods, with applications to rare event sampling and stochastic optimization.

Unveiling how correlated noise and coupled network dynamics can increase the likelihood of rare events, such as blackouts in power systems with a large renewable penetration

Hitting and mixing times of finite volume interacting particle systems with stochastic evolutions

Quantifying throughput and delay performance of wireless networks protocols using Markov chains and heavy-traffic limits.