About

Megan R. Ebers is a machine learning data scientist on the Scientific Machine Learning team at Pacific Northwest National Laboratory (PNNL). Her research leverages machine learning and data-driven modeling to advance sensing technologies, integrating first principles and physics-based techniques whenever appropriate. By combining creativity with foundational scientific insights, her aim is to push the boundaries of what is possible in sensing applications, enabling solutions grounded in the physical realities of a system.

Dr. Ebers was a postdoctoral scholar in applied mathematics with the NSF AI Institute in Dynamic Systems at the University of Washington. Her postdoctoral research focused on data-driven and reduced-order methods for complex systems. In her PhD research, she investigated data-driven modeling and machine learning with sensor data from human motion. During her PhD, she interned with Genentech, applying physics-informed machine learning to understand which preclinical drugs might have the most potential to succeed in clinical trials.

Dr. Ebers was advised by Dr. Kat M Steele (who combines engineering, medicine, and accessible design to understand and enable human mobility) and Dr. J Nathan Kutz (who researches numerical methods for modeling complex dynamical systems). She was supported by the NSF Graduate Research Fellowship Program and the NSF AI Institute in Dynamic Systems.

When she’s not neck-deep in code, you’ll find her frolicking in the mountains, scouting out the best landscape photography compositions, or scheming another project in her workshop.