team

Director of CSML. Assistant Professor in the Department of Computer Engineering and Computer Science at California State University, Long Beach. Research interests include scientific machine learning, computational science, and uncertainty quantification.

Gabriel Lucero is currently pursuing a PhD in Computational Mathematics and Engineering. Previously, he has worked on projects in NMR, signal processing, and biology. His current research interests include uncertainty quantification in machine learning and using AI/ML in science and engineering.

Sakol Bun is currently pursuing a Master’s degree in Computer Science at California State University, Long Beach (CSULB). As an undergraduate student at CSULB, he has gained experience in machine learning, backend development, and cloud computing. His academic pursuits are deeply rooted in artificial intelligence, with a specific focus on uncertainty quantification for data-driven predictive models.

Amogh Raj is currently pursuing his final year in MSc Computer Science. His educational background encompasses an undergraduate degree in Computer Science, complemented by a cumulative 4-year professional experience in Data Engineering, Backend Application development, and Software Engineering. At CSML, Amogh is working on design and implementation of Physics-Informed Neural Networks (PINNs) using a Bayesian approach to solve complex Partial Differential Equations (PDEs).

Carol Gudumotu is a graduate computer science student at CSULB. Her Master’s thesis focuses on Uncertainty Quantification in deep learning neural networks. Her academic interests include Machine Learning and Software development.

Keerthana Srinivasa holds a bachelor’s degree in computer science and is currently pursuing a master’s degree in the same field. Over the course of four years at Honeywell, Keerthana has accumulated extensive experience working with databases, systems, and data engineering in various capacities. Her research at CSML is dedicated to solving inverse problems for chaotic flows, specifically concentrating on predictive models for fire detection.