Rate My Professor Martine Ceberio

Ceberio's research focuses on reliable decision-making under uncertainty, global optimization, constraint solving, interval computations, bioinformatics, and computational science. Her work develops tools for uncertain situations, including reduced-order modeling for large dynamical systems, with applications in network security, biomedical engineering, mechanical engineering, and software engineering. Key publications include 'Towards combining probabilistic and interval uncertainty in engineering calculations: algorithms for computing statistics under interval uncertainty, and their computational complexity' (2006), 'Greedy algorithms for optimizing multivariate Horner schemes' (2004), 'Solving nonlinear systems by constraint inversion and interval arithmetic' (2000), and recent papers such as 'Why topology helps to detect cyber-intrusions' (2025), 'Uncertainty Quantification for Results of AI-Based Data Processing: Towards More Feasible Algorithms' (2025), and 'LLMs in qualitative research: Opportunities, limitations, and responsible integration' (2026). She has received major awards including the 2025 Minnie Stevens Piper Professor Award, K. S. Fu Award from the North American Fuzzy Information Processing Society (2023), AT&T Endowed Fellowship (2021), UT System Academy of Distinguished Teachers Fellow (2020), University of Texas Regents' Outstanding Teaching Award (2019), and UTEP Office of Research and Sponsored Projects Outstanding Performance Award (2010). Ceberio is dedicated to teaching and mentoring, having redesigned computer science curricula, founded the ACM-W student chapter at UTEP in 2012 and advised it until 2018, coordinated the El Paso NCWIT Aspirations in Computing competition from 2011 to 2018, and led Google-funded programs for undergraduate women in computing research since 2018. She participated in Google's Faculty in Residence program in 2018.