Probabilistic Model

Probabilistic models are mathematical frameworks used to represent and reason under uncertainty, aiming to quantify the likelihood of different outcomes. Current research focuses on improving the efficiency and accuracy of these models across diverse applications, including generative AI (e.g., diffusion models, sum-product networks), uncertainty quantification in large language models, and robust inference in Bayesian networks. This work is significant because it enhances the reliability and interpretability of AI systems, leading to improved decision-making in various fields such as healthcare, finance, and scientific discovery.