Ph.D. Project: Field-Programmable Computing for Bayesian Network Structure Learning

A Bayesian network is a powerful and versatile framework for modeling uncertainty in data, providing trans-parent and interpretable inferences. To maximize the potential of Bayesian networks, it is crucial to accurately learn the graph structure that captures the interrelations among variables within data. However, the number of potential graphs grows exponentially with the number of variables, making the structure learning of large Bayesian networks challenging. Our project aims to establish scalable acceleration in structure learning while demonstrating the potential of field-programmable custom computing to achieve energy efficiency and high performance.