Towards Usable and Useful Explainable AI Lijie Hu, Ph.D. Student, Computer Science Jul 7, 17:00 - 19:00 B3 L5 R5220 explainable AI Large Language Models multimodal models This talk presents advancements in Explainable AI, spanning from classical deep learning to large language models, with contributions that enhance both the usability and usefulness of interpretability methods to improve trust, performance, and safety in AI systems.
Explainability and Efficiency in Spatio-Temporal Models: Applications to Traffic Forecasting Xiaochuan Gou, Ph.D. Student, Computer Science Jul 6, 15:00 - 18:00 B5 L5 R5209 traffic forecasting Graph Neural Networks model interpretability This dissertation addresses key challenges in deep learning-based traffic forecasting, including computational efficiency, model interpretability, and data limitations, despite recent progress in spatio-temporal modeling techniques.
Modern Privacy-preserving Machine Learning: Rigorous Approach for Data Privacy Zihang Xiang, Ph.D. Student, Computer Science Jul 6, 10:00 - 12:00 B3 L5 R5216 privacy-preserving machine learning Differential privacy Federated learning This dissertation centers around privacy-preserving technologies (differential privacy) in broad machine learning applications. This dissertation focuses on two sides of differential privacy: 1) designing privacy-preserving algorithms, 2) ensuring the falsifiability of privacy claims.
Spatiotemporal Machine Learning for Real-world Complex Systems: Enhancing Smart Transportation with Robust AI ZIyue Li, Assistant Professor, Information Systems Department, University of Cologne, Germany May 8, 13:00 - 14:00 B1 L4 R4214 This seminar introduces advanced machine learning approaches, including tensor methods for modeling high-dimensional mobility data and deep learning techniques designed to handle data corruptions, aiming to improve the reliability of spatiotemporal analytics for smarter transportation systems.
