Daniele Catanzaro

Short Bio
I am Professor of Discrete Optimization and Operations Research, currently serving as President of the Center for Operations Research and Econometrics (CORE) of the Université Catholique de Louvain.

I graduated summa cum laude in Computer Science Engineering at the University of Palermo, Italy, in 2003. In 2008, I was awarded a Ph.D. in Computer Science from the Université Libre de Bruxelles for my research in discrete optimization, network design, and computational phylogenetics, conducted at the Computer Science Department, the Institute of Biology and Molecular Medicine (IBMM), and the Hospital Erasme of the same university. Before joining the Université Catholique de Louvain in 2014, I was a Chargé de Recherches of the Belgian National Fund for Scientific Research (2009-2013), Visiting Scholar at the Tepper School of Business and the Computational Biology Department of Carnegie Mellon University(2010-2012), and Assistant Professor of Discrete Optimization at the Faculty of Economics and Business of the Rijksuniversiteit Groningen (2013-2014). More recently, I have been invited as a Visiting Professor at the Department of Management at the University Ca’ Foscari of Venice, Italy, in 2018 and again in 2024.

I recently served as Vice-Chair for the ERC Horizon calls HORIZON-MSCA-2025-PF-01 and as Panel Member for the Canadian NSERC/CRSNG (calls 2022-2025).

Research
I am a computer scientist and an applied mathematician. My research focuses on the foundational theory and algorithmic development for combinatorial optimization and integer programming. I am particularly interested in advancing both exact and approximate methods for optimization over integers, aiming to deepen theoretical insights while designing effective and scalable algorithms for NP-hard problems arising in real-world applications. My work has addressed a range of topics so far, including linear, nonlinear, and uncertain network design; coloring, covering, and partitioning; routing; (versions of) the traveling salesman and the quadratic assignment problems; and the optimization of exponential functions over discrete domains (e.g., entropy-based models, optimal embedding trees). I have been interested in the applications of discrete optimization to bioinformatics, with particular focus on tumorigenesis and genome-wide association studies. In recent years, my research has focused intensively on the combinatorial and optimization aspects of phylogenetics, contributing to the development of distance-based methods, specifically minimum evolution and balanced minimum evolution.

Throughout my career, my research activities have been supported by several competitive funding programs, including the European Marie Curie Fellowship Program (grant HPRN-CT-1999-00106); the Belgian National Fund for Scientific Research (FNRS) , through the Aspirant and Chargé de Recherches mandates, the CDR grant S/25-MCF/OL J.0026.17, and PDR grant 2021-40007831; the Fonds Brachet (IBMM Prize 2007); the Louvain Foundation (grant Coalesces ); the U.S. National Institutes of Health (NIH) (grants 1R01CA140214 and 1R01AI076318); and the Belgian American Educational Foundation (BAEF) , through the Honorary Fellowship 2010–2011 for biomedical engineering research in the United States.

Research Keywords: combinatorial optimization, integer programming, polyhedral combinatorics, decomposition algorithms, branch-&-cut, branch-price-&-cut, computational complexity, routing, network design, coloring, covering, partitioning, location, games on graphs, constructive characterizations, high performance computing, massive parallel enumeration, enumeration of trees, lattices of unrooted binary trees, tree coding, Huffman coding, information entropy, entropy encoding, cross-entropy encoding, Kullback–Leibler divergence, encryption schemes, tree metric, submodular functions, convexity, Schur convexity, mathematics of evolution, phylogenetics, phylodynamics, mathematical modeling of tumor progression, genome-wide association studies, medical bioinformatics, deep reinforcement learning, hierarchical clustering, optimization aspects of machine learning.

Personal webpage: https://danielecatanzaro.github.io