Victoria Crawford

Victoria Crawford is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, where she has held this position since 2022. She holds a Ph.D. in Computer Engineering (2022), an M.S. in Mathematics (2016), and a B.S. in Mathematics (2012), all from the University of Florida. Her research focuses on approximation algorithms, optimization, machine learning, and submodular optimization, with an emphasis on scalable solutions for large datasets and theoretical guarantees.

Her educational background includes advanced studies in mathematics and computer engineering, complemented by teaching experience in algorithms and theory courses at Texas A&M. Crawford has received prestigious awards such as the Gartner Group Graduate Fellowship (2019) and the Harris Fellowship (2017), and her work includes high-impact publications in venues like IJCAI, ICML, and NeurIPS.

Crawford actively contributes to academic service, serving on committees including the Texas A&M Graduate Admissions Committee and the Data Science Hiring Committee. She also chairs and advises doctoral students such as Wenjing Chen and oversees research projects funded through initiatives like the Texas A&M Targeted Proposal Teams and the TAMIDS Seed Program for AI/Computing/Data Science.

Her research spans theoretical and applied domains, including algorithm design for submodular functions, bandit optimization, and network resilience. Recent work emphasizes fairness in algorithmic design and scalable solutions for billion-scale networks.