Mateo Diaz is an assistant professor in the Department of Applied Mathematics and Statistics. Mateo Díaz‘s interests span continuous optimization, computation, and statistics, with a particular focus on designing and analyzing practical large-scale algorithms for applications in data science, machine learning, operations research, and signal processing. Mateo is currently a postdoctoral scholar in the Department of Computing and Mathematical Sciences at Caltech hosted by Venkat Chandrasekaran and Joel Tropp. Before Caltech, he received a PhD in applied mathematics and an MSc in computer science from Cornell University, mentored by Damek Davis. Mateo obtained two undergraduate degrees in mathematics and in systems and computing engineering from Universidad de los Andes in Colombia. While earning his PhD, he worked as a student researcher at Rappi in Colombia and Google Research.
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Mateo Díaz, new assistant professor in the Department of Mathematics and Statistics at Johns Hopkins, discusses his background, research interests, and enthusiasm for joining the university. Originally from Colombia, Díaz's research focuses on the intersection of continuous optimization, geometry, and statistics, with applications in data science and machine learning. In this Q&A, he shares insights into his research, real-world applications, and what drew him to this field.