CEMSE Weekly Updates - February 17, 2026 Tue, Feb 17 2026 Newsletter Upcoming Events Stay informed about the upcoming events and the latest news from CEMSE. Computing Heteroclinic Orbits for Dynamical Systems Theodoros Katsaounis, Professor, Department of Mathematics and Applied Mathematics, University of Crete (UoC) Feb 23, 11:00 - 12:30 B1 L3 R3119 Dynamical Systems heteroclinic orbits Numerical Modeling This course provides an overview of the most well known methods for computing heteroclinic orbits for dynamical systems. Towards Scalable and Structured Understanding in Visual LLMs Mohamed Elhoseiny, Associate Professor, Computer Science Feb 23, 12:00 - 13:00 B9 L2 R2325 LLM Visual Language Models VLMs visual computing In this talk, we explore a suite of recent advances toward scalable, structured video comprehension using Large Vision Language Models (Video LLMs). Graphpcor: Prior for Correlation Matrices Elias Teixeira Krainski, Research Scientist, Statistics Feb 26, 12:00 - 13:00 B9 L2 R2325 correlation Bayesian Estimation expert knowledge integration This talk introduces a scalable, graph-based framework for modeling correlation matrices that integrate expert-informed priors. Rigorous Model-Constrained Scientific Machine Learning for Digital Twins: A Computational Mathematics Perspective Tan Bui-Thanh, Professor, Endowed William J. Murray, Jr. Fellow in Engineering No. 4, Oden Institute for Computational Engineering & Sciences, Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin (UT Austin) Feb 26, 14:30 - 15:30 B1 L3 R3119 Scientific Machine Learning SciML Scientific Deep Learning SciDL deep learning machine learning digital twins uncertainty quantification Computational mathematics This talk will outline a principled pathway from traditional computational mathematics to rigorously grounded Scientific Machine Learning (SciML) and present recent Scientific Deep Learning (SciDL) methods for forward modeling, inverse and calibration problems, and uncertainty quantification, emphasizing mathematical structure, stability, and generalization.
