About
I am broadly interested in network science and computational epidemiology, with a special focus on integrating social and biological data. As of September 2024, I am a FWF Cluster of Excellence: “Microbiomes Drive Planetary Health” postdoc at the Bergthaler Lab, Medical University of Vienna.
SLORA: Sick Leave On the Rise in Austria (2026-2028)
I am excited to be a co-PI on the Sick Leave On the Rise in Austria (SLORA) project, funded by the Data:Research:Austria (ÖAW) program, where we study respiratory disease-related sick leave in Austria using data-driven epidemic modelling. The project combines administrative and survey data from the Austrian Micro Data Center, vaccination data, sick leave data, and wastewater surveillance data.
SNSF Postdoc Mobility Fellow (2022-2024)
Previously, I worked on Awareness Modelling in Epidemics as an SNSF Postdoc Mobility Fellow hosted by Márton Karsai at DNDS, CEU, Vienna. Recent manuscripts on this topic include a genetic data-driven [1] and a network theoretic [2] approach.
PhD (2017-2022)
My thesis was on the source idenitfication problem; the goal is to design efficient algorithms that find the first infected individual (also called patient zero) during an epidemic, based on sparse measurements about who got infected and when. I was fortunate to be advised by Patrick Thiran at EPFL.
On the algorithmic side, I have worked on rigorously quantifying the role of adaptivity in source identification: the difference between the problem settings when the measurements are chosen adaptively vs non-adaptively. If the epidemic spreads very aggressively, the theoretical analysis ([3] and [4]) becomes equivalent to adaptive and non-adaptive versions of the metric dimension from the combinatorics literature. If the epidemic is less aggressive, then there is more stochasticity in the problem, making the analysis more challenging [5]. Interestingly, in some cases adaptivity only plays a small role [3], whereas in others, its role is very substantial [5].
More on the modelling (but still rigorous) side, I have worked on relaxing the assumption in the source identification problem that the underlying contact network is fully known to the algorithm [6]. Also in this direction, we studied the robustness of the metric dimension to single edge changes [7].
Slightly deviating from the algorithmic source identification problem, I have worked on understanding how the location of (multiple) seeds affect the outcome of an epidemic (modelling [8] and theory [9]).