Photo of Robbert van der Burg

Robbert van der Burg

PhD candidate

VU University Amsterdam

I am a first-year PhD candidate in the Department of Mathematics at the Vrije Universiteit Amsterdam. My research lies at the intersection of probability theory, network science, and applied statistics, under the supervision of Alessandro Zocca and Frank van der Meulen.

My PhD research focuses on probabilistic modeling of weather-driven failures in critical infrastructure networks, such as power grids, as part of the VIDI "Power Network Optimization in the Age of Climate Extremes" (https://doi.org/10.61686/GOOEL09973). These networks are inherently spatially distributed, and when subjected to extreme environmental stressors, such as wind storms, floods, or heatwaves, failures rarely occur in isolation. Instead, extreme weather manifests as a spatial field with a characteristic footprint, simultaneously affecting multiple components of the network. As a result, the vulnerability of nodes is strongly shaped by spatial dependence and correlation structures.

In addition to my main PhD topic, I maintain a strong interest in Markov chain theory and network analysis. Related side projects include the study of hitting times on graph models, first passage times, and the Kemeny constant, as well as applications of these concepts in social network analysis. I frequently work with Bernd Heidergott and Thao Le on projects spanning random walks on networks, opinion dynamics, and infrastructure reliability.

Research Interests

Markov Chains
Stochastic Processes
Network Science
Random Walks
Opinion Dynamics
Quasi-Stationary Distributions
Infrastructure Reliability
Applied Statistics

Research Experience

Random Walks on Networks

Non-standard random walks with edge weights, optimization problems, and connections to Kemeny constants. Collaborators: Thao Le, Alessandro Zocca, Bernd Heidergott. Paper submitted to Operations Research.

Infrastructure Failure Modeling

Comparing probabilistic models (Ising, Bayesian hierarchical, independent Bernoulli, autologistic regression) for analyzing cascading failures in infrastructure networks.

Opinion Dynamics & QSD Theory

Connecting quasi-stationary distribution theory to opinion dynamics models (DeGroot, Friedkin-Johnsen), extending BSc thesis work on metastable behavior.

Latest News

Mar 2026

New preprint on non-standard random walks with edge weights available on arXiv.

Oct 2025

Began PhD in Mathematics at VU University Amsterdam, working on probability theory, network science, and applied statistics.