Bethany Edmunds
Teaching Professor, Associate Dean of Computing Programs, West Coast
Research interests
- Generalized reinforcement learning for mobile robotics
Education
- PhD in Computer Science, Rutgers University
- BS in Computer Science, Rowan University
Biography
Bethany Edmunds is a teaching professor and the associate dean of computing programs, West Coast at the Khoury College of Computer Sciences at Northeastern University. She is passionate about breaking down barriers to create greater diversity, access and inclusivity within the technology community. She brings together expertise in software development, machine learning, and educational innovation to create STEM opportunities for people of all backgrounds and abilities.
Prior to joining Northeastern, she was the first female Associate Dean of Computing at British Columbia Institute of Technology where she led the pedagogical innovation of the Computer Information Technology Program. Her industry experience includes developing flight simulation software while working for the Federal Aviation Administration (FAA), the government body that oversees all aspects of civil aviation in the United States.
Edmunds has been named to BC Business’s Most Influential Women in STEM, Business in Vancouver’s Forty under 40, and YWCA’s Women of Distinction.
Research publications
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A primer of artificial intelligence in medicine
Citation: “A primer of artificial intelligence in medicine”, Alexandra T. Greenhill, Bethany R. Edmunds, Techniques and Innovations in Gastrointestinal Endoscopy, Volume 22, Issue 2, 2020, Pages 85-89 -
Provably Efficient Learning with Typed Parametric Models
Citation: “Provably Efficient Learning with Typed Parametric Models”, Emily Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, and Nicholas Roy. Journal of Machine Learning Research, 2009. -
The Adaptive K-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning
Citation: “The Adaptive K-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning”, Carlos Diuk, Lihong Li, and Bethany R. Leffler. Proceedings of the International Conference on Machine Learning, 2009.
