Samuel Koovely

Office Y27/G25

Winterthurerstrasse 190, 8057 Zurich, Switzerland

I am a doctoral student in the group of Prof. Dr. Alexandre Bovet, at the Department of Mathematical Modeling and Machine Learning of the University of Zurich (see here). I work on various aspects of complex network theory. Currently, I am studying diffusion processes over graphs and using tools from information theory to understand the structural properties of complex networks. Moreover, I am interested in topics at the intersection of probability theory and statistics; in particular Markov processes, graphical models, machine learning, and causal inference.

Selected Literature

Entropy-Informed Detection of Change Points in Dynamic Networks

Samuel Koovely and Alexandre Bovet

2026
Evolution of Conditional Entropy for Diffusion Dynamics on Graphs

Samuel Koovely and Alexandre Bovet

2026
A mathematical framework for COMIC-Tree: an undirected graphical model for T-cell receptors specificity

Samuel Koovely

Jul 2021

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T-cells are a core component of the adaptive immune system: they play a major role in mounting an effective and tailored response to foreign pathogens, and they are also relevant in the context of cancer and cer- tain autoimmune diseases. T-cells receptors are protein complexes present on T-cells’ surface that are responsible for identifying foreign and own antigens. Given the complexity of protein-protein interactions, this identification process exhibits a quasi-stochastic behaviour that can be modeled with probabilistic and statistical models. Graphical models can represent a multivariate distribution in a convenient and transparent way as a graph. In this paper we introduce COMIC-Tree, an undirected graphical model for protein-protein interactions, and DrawCOMIC-Tree, a greedy algorithm based on conditional mutual information for learning COMIC-Tree structures. We provide a solid mathematical foundation for them, highlight some theoretical aspects, and test them empirically on a dataset of T-cell receptors.
Overview and empirical evaluation of some variations of the PC-algorithm

Samuel Koovely

Jul 2020

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The output of the original PC-algorithm depends on the order of the evaluated variables, and the separating sets used during its pruning process might be inconsistent with the final result. We give an overview of some variations of this method that address these shortcomings and perform a simulationstudy of these algorithms.