PhD
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]).