Gonzalo Muñoz
I studied Mathematical Engineering at the Engineering School of Universidad de Chile, from where I graduated in 2012. My thesis work involved a research project on open-pit mine scheduling problems, which made me consider a Ph.D. in Operations Research and made me acquainted with Dan's work. Thus, Columbia became my main plan for pursuing a Ph.D.
I started my Ph.D. at the IEOR Department in the fall semester of 2012 and started working with Dan on optimization problems arising from power systems operations. We developed some computationally tractable techniques for approximating different electrical transmission problems. Afterward, and inspired by the sparsity of the power grid itself, we developed theoretical results regarding how structured sparsity in a polynomial optimization problem can be used in order to approximate it via moderate-sized linear programs. This work received the 2016 Student Paper Prize of the INFORMS Optimization Society. After my Ph.D. I spent 2 years as an IVADO Post-doctoral fellow at Polytechnique Montréal. During this period, I worked in the intersection of optimization and machine learning, as well as computational techniques for general MINLPs.
In August 2019, I joined the Engineering Institute of Universidad de O'Higgins in Rancagua, Chile, as an assistant professor. This university was created in 2017, and it is the very first public university in the VI Region of Chile. Since then, I have been mainly working on theoretical and computational aspects of quadratically-constrained optimization. One of my works on these lines was the winner of the 2023 Young Researchers Prize of the INFORMS Optimization Society. I have also been starting to work on bilevel optimization problems and optimization of trained neural networks.
Broadly speaking, my research interests fit into the category of Mixed-Integer Non-Linear Optimization, including both theoretical perspectives and implementation of efficient algorithms to address this type of problems. Other related topics that I am interested in are Combinatorial Optimization, Polynomial Optimization, and applications of these methodologies to Mining and Energy.