Thomas Lavastida

Thomas Lavastida is an Assistant Professor in the Information Systems department at the University of Texas at Dallas' Naveen Jindal School of Management. Previously, he earned his PhD in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University's Tepper School of Business, where he was advised by Benjamin Moseley.

• PhD - Algorithms, Combinatorics, and Optimization, Carnegie Mellon University (2022)
• MS - Algorithms, Combinatorics, and Optimization, Carnegie Mellon University (2019)
• BSEE - Computer Engineering, Louisiana State University (2016)

His research focuses on algorithm design and analysis, particularly in learning-augmented algorithms, resource allocation, and scheduling problems. He also explores connections between algorithms and machine learning, with particular emphasis on clustering and optimization applications.

Thomas's publications demonstrate expertise in online algorithms, predictive algorithm design, and scalable computing solutions for complex problems. His work spans theoretical foundations and practical applications across scheduling, matching, clustering, and ad delivery systems.

Before joining UTDallas, he served as an instructor for Optimization for Business at Carnegie Mellon University (2020) and tutored mathematics at Louisiana State University (2013-2016).