keywords: Integer programming, combinatorial optimization, computational complexity, and graph theory

I'm a mathematician with a passion for both the pure and applied sides of math. I like digging into the theory as well as exploit such a theory for solving real-world problems. My research is mostly centered around graph theory and polyhedral combinatorics — two worlds I find incredibly rich and deeply connected.

Most of my efforts go into understanding the deep structure of polyhedra that come up in discrete optimization. I'm especially interested in strong integrality properties, like the integer decomposition property (IDP) and box-total dual integrality (box-TDI). These might sound abstract, but they have very real implications for how we solve problems efficiently — or prove we can't.

I also work on complexity theory. Many of my results are about figuring out how hard it is to solve certain optimization problems. The kind of structures I work with are the classics: stable sets, (perfect) matchings, edge covers, vertex covers... all the greatest hits from graph theory, but seen through the lens of polyhedra and integer programming.

During my postdoc at CORE (UCLouvain), I also started working on network design problems, especially in the context of hierarchical clustering. That opened up new directions for me, where theoretical insights meet algorithmic needs in data science — a line of work I continue to pursue now as an MSCA Postdoctoral Researcher at the Venice School of Management, Ca' Foscari University of Venice.

Here's a more structured line of my principal contributions:

• Integer programming & polyhedral combinatorics: Studied integrality conditions like IDP and box-TDI, and how they relate to well-known combinatorial problems.
• Combinatorial optimization: Developed results around classic problems (stable sets, matchings, coverings) and how they behave under structural or degree constraints.
• Computational complexity: Proved hardness results and explored what makes a problem tractable or not, often via reductions and clever formulations.
• Graph theory: Worked on understanding how graph structure affects optimization and complexity, with connections to perfect graphs and decomposition methods.
• Applications & collaboration: I like teaming up with people who bring applications to the table — operations research, computer science, data clustering — and seeing how theory can help (or be challenged).

If there's a red thread in what I do, it's the belief that good theory should be elegant, but also grounded. Whether it's a nice clean polyhedron or a messy real-world problem, I'm in — especially if there's a mathematics involved.