Carlos Cardonha

Carlos Cardonha is an Assistant Professor in the Department of Operations and Information Management at the University of Connecticut School of Business, a position he has held since August 2019. He earned his Ph.D. in Mathematics from the Technische Universität Berlin in 2011, with a thesis titled 'Applied Methods for the Vehicle Positioning Problem' advised by Prof. Dr. Martin Grötschel. He also holds an M.Sc. in Computer Science from the University of São Paulo in 2006 and a B.Sc. in Computer Science from the same institution in 2004, graduating with honors. Prior to academia, Cardonha served as a Research Staff Member in the Natural Resources Optimization Group at IBM Research Brazil from January 2012 to August 2019, developing methodologies in data science, discrete optimization, and algorithms for applications in natural resources, transportation, agriculture, education, and accessibility. Earlier roles include Visiting Researcher at the Zuse Institute Berlin from 2007 to 2011 and Coach for Programming Competitions at the University of São Paulo from 2002 to 2007, leading teams to multiple medals in the ACM International Collegiate Programming Contest, including a gold medal and 26th place in the 2007 World Finals.

Cardonha's research focuses on discrete optimization, mathematical programming, mixed-integer linear programming, combinatorial optimization, approximation algorithms, online optimization, and their applications to operations research and management problems. His influential publications include 'Recursive McCormick linearization of multilinear programs' (INFORMS Journal on Computing, 2025, with A. Raghunathan, C. Nohra, D. Bergman), 'Design and analysis of efficient sequencing policies for linear cold storage devices' (Production and Operations Management, 2025, with A. Cire, L. Villa Real), 'Optimizing over an ensemble of trained neural networks' (INFORMS Journal on Computing, 2023, with K. Wang, L. Lozano, D. Bergman), 'Network models for multiobjective discrete optimization' (INFORMS Journal on Computing, 2022, with D. Bergman, M. Bodur, A.A. Cire), and 'On the minimum chordal completion polytope' (Operations Research, 2019, with D. Bergman, A.A. Cire, A.U. Raghunathan). He has received the IBM Research Outstanding Technical Achievement Award (2015, 2019), IBM Research Accomplishment in Science Awards (2017, 2018), UConn School of Business Innovation in Teaching Award (2022), Scholarship Facilitation Funds from UConn OVPR (2020, 2024), and conference paper awards such as the John Slatin Award (2013). Cardonha has delivered invited talks and presentations at INFORMS Annual Meetings, ISMP, POMS, and SIAM Optimization Conference. He serves on UConn OPIM doctoral committees and coordinates the OPIM Research Seminar.