Explainable AI with Statistical Models

Deep learning models are typically considered as a black-box while explainability is important for critical applications such as autonomous driving and medicine. Explainable AI has thus become an important topic in research and industry. Various methods for explaining deep learning models have been developed, including visualisation-based techniques and feature relevance evaluation.

In this project, we will investigate the relationship between deep learning and statistical models. Current research has demonstrated that it is possible to use deep learning to simulate statistical models or vice versa. However, we are interested in further investigation of the underlying formulations and mathematical equivalence between these models.

This project will involve extensive literature review and in-depth analysis of various deep learning and statistical models, and conduct experiments on classical benchmarks to derive fundamental understanding of the model formulations. 

How to Apply

Express your interest in this project by emailing Professor Maurice Pagnucco. Include a copy of your CV and your academic transcript(s).