3G: Computational, Machine Learning, and Artificial Intelligence Approaches in Biomaterials

Computational modeling and machine learning can enhance our ability to design and evaluate biomaterials for a variety of applications. Artificial intelligence has also been playing an increasingly important role in the development of biomaterials and in disease treatment. This session will explore computational approaches and machine learning/artificial intelligence tools for designing and classifying biomaterials for tissue engineering and other applications, evaluating complex data from in vitro and in vivo studies, and predicting biomaterial performance in different microenvironments. Examples of these may include fluid mechanics and bio-transport models of drug/protein delivery, models of protein-protein and protein-material interactions, statistical modeling for biomaterial optimization, machine learning for biomaterial design and analysis, and bioinformatics-based platforms for analyzing complex data, including RNA sequencing and other -omics approaches. Progress and challenges in applying artificial intelligence in developing biomaterials and in disease treatment will also be discussed.

Moderators:

Adam Gormley
Associate Professor | Biomedical Engineering
Rutgers