Pamela Alejandra Bustamante Faúndez, Phd

Pamela Bustamante holds a Ph.D. in Engineering Sciences from Pontificia Universidad Católica de Chile (PUC), with doctoral research focused on Stackelberg Security Games and polyhedral structures. She also holds a Master's degree and a Professional Degree in Industrial Engineering from Universidad del Bío-Bío. She completed research internships at INRIA Lille-Nord (France) and GERAD (Montreal, Canada).

She is currently an Assistant Professor in the Department of Industrial Engineering at Universidad de los Andes, teaching Mathematical Programming and Reinforcement Learning.

She co-founded Python Chile and PyLadies Santiago de Chile, non-profit organizations promoting Python and women's participation in technology. She has published in top-ranked journals including Omega and Computers & Operations Research, and has presented at conferences such as EURO, IFORS, and EuroSciPy.

Her research interests lie at the intersection of Game Theory, Mathematical Optimization, and Artificial Intelligence, with applications in security and critical infrastructure protection.

She investigates Stackelberg Security Games (SSGs), where a resource-constrained defender protects targets against a rational attacker. Her doctoral thesis demonstrated that the polyhedral structure of the defender's strategy space determines computational complexity and enables efficient algorithm design through Branch-and-Price and Branch-and-Cut, achieving up to 95% reductions in computational time versus the state of the art.

She is also interested in machine learning for industrial applications (accident prediction, churn analysis), facility location, graph partitioning, and routing. She has a growing interest in AI alignment and governance, participating in international initiatives such as ENAIS and AI Safety Hungary.

As an Assistant Professor at Universidad de los Andes, Pamela Bustamante teaches Mathematical Programming (2024, 2025) and Reinforcement Learning (2025) at both undergraduate and graduate levels.

Her methodology integrates modern computational tools such as Python and Julia, applying real-world optimization and AI cases to promote active learning.