Advances in sensing technologies, experimental techniques and scientific computing have led to an increasing accessibility of high-quality data. As such, statistical learning models have become an integral tool for investigating key scientific and industrial questions. There are, however, fundamental limitations. Existing models are often black-box and lack scientific interpretability. High-quality scientific data can also be expensive, resulting in small sample sizes for model training, and thus poor predictive models with high uncertainty. This session focuses on novel developments in science-integrated statistical learning theory and methods, which tackle these issues by integrating scientific domain knowledge (e.g., mechanistic models, differential equations, guiding principles) as prior information for statistical model building. The session will feature speakers who have worked in this area for a broad range of applications in science, engineering and industry, followed by a discussant.

Organiser: Dr Simon Mak 

Chair: Dr Simon Mak 

Speaker: Prof. Chih-Li Sung 

Speaker: Oksana Chkrebtii 

Speaker: Mike Grosskopf 

Discussant:  Daniel Williamson