explainable AI

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

Xiang Chen, Ph.D. Student, Statistics

Apr 30, 13:00 - 16:00

B5 R5209

spatio-temporal modeling geospatial statistics infectious disease explainable AI Public Health climate data deep learning computational predictions

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

KAUST-CEMSE-AMCS-PhD-Dissertation-Defense-Lijie-Hu-Towards-Usable-and-Useful-Explainable-A

Towards Usable and Useful Explainable AI

Lijie Hu, Ph.D. Student, Computer Science

Jul 7, 17:00 - 19:00

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.

Xiang Chen

Ph.D. Student, Statistics

spatio-temporal modeling infectious disease geospatial statistics Spatial epidemiology Public Health Time Series Analysis deep learning explainable AI

Xiang Chen's research focuses on spatio-temporal modeling of climate-sensitive infectious diseases, integrating large-scale health, climate, and human mobility data to improve epidemiological forecasting and support public health decision-making.