CEMSE Weekly Updates - January 27, 2026 Tue, Jan 27 2026 Newsletter Upcoming Events Stay informed about the upcoming events and the latest news from CEMSE. Uncertainty-Aware Learning: From Bayesian Neural Networks to Agentic Decision Making Theodore Papamarkou, Founder, PolyShape; Visiting Professor, School of Applied Mathematical and Physical Sciences (SEMFE), National Technical University of Athens (NTUA) Feb 1, 12:00 - 13:00 B4/5 L0 A0215 uncertainty quantification neural network Bayesian modeling AI This talk points out that uncertainty quantification is important for reliable AI, and that modern machine learning should be viewed through the lens of probabilistic decision making. Uncertainty Quantification with Conformal Prediction in Energy Data Tarek AlSkaif, Associate Professor, Energy Informatics, Wageningen University (WUR) Feb 1, 12:00 - 13:00 B9 L2 R2325 conformal prediction machine learning uncertainty quantification The talk will introduce the fundamentals of conformal prediction (CP) - a flexible, model-agnostic uncertainty quantification framework for generating statistically valid uncertainty estimates in energy applications - and demonstrate how it can be layered on top of machine learning models to produce reliable prediction intervals. From Dialects to Peptides: Scalable and Efficient AI for People Muhammad Abdul-Mageed, Canada Research Chair, Natural Language Processing and Machine Learning; Associate Professor, School of Information, Department of Linguistics, The University of British Columbia Feb 2, 12:00 - 13:00 B9 L2 R2325 AI Efficient Efficient Machine Learning This talk presents a unified AI framework for decoding complex human and biological signals - spanning African and Arabic dialects to proteomics - by prioritizing rigorous measurement, cultural competence, and computational efficiency to ensure global scalability and accessibility. Efficient Machine Learning for Scientific and Medical Applications Yasir Ghunaim, Ph.D. Student, Computer Science Feb 4, 18:00 - 20:00 B4/5 L0 A0215 Efficient Machine Learning machine learning Graph Neural Networks This dissertation addresses key challenges of machine learning in scientific and medical domains by developing methods that improve model efficiency, data efficiency, and learning under real-world constraints. On the Modeling and Approximation of Phase Transitions in Elasticity Georgios Grekas, Postdoctoral Research Fellow, Applied Mathematics and Computational Science Feb 5, 12:00 - 13:00 B9 L2 R2325 Phase transitions elasticity mathematical modelling This talk explores the mathematical modeling of phase transitions in elasticity, drawing motivation from observed phenomena in crystalline solids and biomaterials.
