Rate My Professor Maria Pantoja

Maria Pantoja serves as an Associate Professor in the Computer Science and Software Engineering Department within the College of Engineering at California Polytechnic State University, San Luis Obispo. She earned her Doctor of Philosophy in Computer Engineering from Santa Clara University. Pantoja's academic interests and research specializations include High Performance Computing, Parallel Programming with OpenMP and GPU technologies, Distributed Systems utilizing MPI and GoLang, Reliability in Distributed Systems, and Acceleration of Deep Learning applications. Her expertise also covers Fault Tolerant Systems, Scientific and High Performance Computing, Computer Architecture, and probabilistic uncertainty quantification in Machine Learning.

In her tenure at Cal Poly, Pantoja has advised numerous master's theses, including "Wi-Fi Sensing: Device-Free In-Zone Object Movement Detection" (2021), "Accelerating Collaborative Awareness with Shelf CRDT" (2023), "A Study of Implementation Methodologies for Distributed Mutual Exclusion" (2021), "Lazy Fault Recovery for Redundant MPI" (2019), and "RISC-V GPU Acceleration on Low-Cost Embedded Systems" (2025). Key publications authored or co-authored by her encompass "An Introduction to Deep Learning" (2018), "Deep Learning for Agriculture" (2020), "Inter-Class Collaboration Project to Enhance Learning in Computer Science" (2018), "Peachy Parallel Assignments" (EduHPC 2023), and the poster "Optimizing Uncertainty Quantification of Vision Transformers for Deep Learning" (SC23). She was awarded the Lockheed Martin Endowed Professorship in 2019. Pantoja contributes to the high-performance computing community through presentations at Supercomputing conferences such as SC25 and SC23, participates in public AI debates, and develops inter-class projects promoting collaboration in deep learning and artificial intelligence education.