Mateo Díaz is an assistant professor in the Department of Applied Mathematics and Statistics and a member of the Data Science and AI Institute. His interests encompass continuous optimization, computation, and statistics, with a particular focus on designing and analyzing highly effective, large-scale algorithms for applications in data science, machine learning, operations research, and signal processing.
In 2025, Diaz received the NSF CAREER Award. In 2024, Diaz and his team were awarded the Beale—Orchard-Hays Prize for Excellence in Computational Mathematical Programming, which recognized the team’s groundbreaking research in Primal-Dual Linear Programming.
Before his time as a postdoctoral scholar at Caltech, he received a PhD in applied mathematics and an MSc in computer science from Cornell University, mentored by Damek Davis. During his doctoral studies, he worked as a student researcher at Rappi (Colombia) and Google Research. Díaz obtained two undergraduate degrees in mathematics and in systems and computing engineering from Universidad de los Andes (Colombia).
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NSF CAREER award to advance scalable optimization tools to solve complex data science challenges in health care, energy, and finance.
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Assistant Professor Mateo Diaz and his team win the 2024 Beale—Orchard-Hays Prize for their work on Primal-Dual Linear Programming.
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Breaking dimensional limits in AI
CategoriesJohns Hopkins team develops a dimension-agnostic method for machine learning.