About Me
Hi, I'm Igor Cardoso, a PhD student in Industrial Engineering at Texas Tech University. I have always been passionate about mathematics and problem-solving, which has driven my professional journey.
My research focuses on Integer Linear Programming and its applications in optimization. I am particularly interested in developing efficient algorithms and mathematical models to solve real-world industrial problems.
News
- May 2026 — Presented my poster as a finalist at the MIP Workshop 2026, receiving $750 in finalist travel support.
- Dec 2025 — Awarded the TTU Tye Industrial Endowment Engineering Scholarship for the Spring 2026 semester, recognizing academic excellence in the Industrial, Manufacturing, & Systems Engineering program.
- Sep 2025 — Our INFORMS Student Chapter at Texas Tech University was honored with the 2025 Student Chapter Annual Award (Cum Laude). I actively participate in seminars and collaborations to deepen my knowledge of optimization and operations research.
- May 2025 — Completed an aircraft scheduling optimization project for American Airlines as part of a graduate course at Texas Tech University, applying mixed-integer programming techniques to improve scheduling efficiency.
- Feb 2025 — Joined the INFORMS Student Chapter at Texas Tech University. Involved in seminars and collaborations to expand my knowledge of optimization and operations research.
Publications
Cardoso, Igor and Validi, Hamidreza (2026). On Solving the Network-Based Portfolio Optimization Problems. Manuscript in preparation.
Cardoso, Igor, Teodoro, Gabriel and Validi, Hamidreza (2026). Approximation Algorithms for Sequential Testing Games. Manuscript in preparation.
Gutierrez, Rogelio, Cardoso, Igor and Validi, Hamidreza (2026). Sport Scheduling to Minimize Travels at the FIFA World Cup 2026 . Optimization Letters.
Cardoso, Igor, Gomes, Carlos, Rosa, Paulo F.F. and da Fonseca, Vinicius Prado (2024). Comparing Pre-Trained Object Detection Models for Autonomous Grasp on Affordable Prosthetic Hands . MeMeA 2024 - IEEE Medical Measurements & Applications.
Apps & Tools
A GPU-accelerated decision tree solver achieving a theoretically optimal 3.42 average guesses. Play interactively to compare your intuition against the algorithm.
An interactive app using Mixed Integer Programming (MIP) to optimize match scheduling. Drag-and-drop matches to build and validate your own schedule.
A computer vision guide that scans your physical Rubik's Cube to create a 3D digital twin, providing step-by-step instructions using the CFOP method.
A utility tool designed to track and optimize shared expenses for fairness and minimal transactions.