Ashley S. Dale earned three sets of dual degrees in electrical and computer engineering and physics: bachelor's in 2017, master's in 2020, and PhDs in 2024. She received the PhD in physics from Indiana University Indianapolis and all other degrees from Purdue University in Indianapolis. Through all of her graduate studies, her physics research was limited by a lack of suitable computational methods, and her engineering research was limited by a lack of fundamental theory. She aims to rectify this situation in her future work by intentionally designing and using machine learning (ML) models that are trustworthy according to established laws of physics. As a result of her training with Indiana University's Educational Best Practices Institute, she rewrote the lab curriculum for introductory physics labs. Then, as a fellow with the American Physical Society Data Science Community of Practice, she developed open-source curriculum that is available in a GitHub repository. Currently, she is a postdoctoral fellow at the University of Toronto, where she is creating the first trustworthy AI framework for scientific AI/ML by developing quantitative metrics from a foundational AI perspective. She plans to release her framework as an open-source Python package. Simultaneously, she is building software for a self-driving laboratory platform that evaluates battery degradation using AI. As a professor, Dale will specifically predict to students where their difficulties in courses will arise, allowing them to recognize challenges quickly and her to address learning issues early.

Research Interests

Trustworthy AI, Computer Vision, Materials Science