Bernease Herman is a data scientist and researcher at the University of Washington eScience Institute. Her research focuses on interpretable machine learning with work in fairness, accountability, and transparency. In her work, she collaborates with academic researchers, startups, and non-profits with applications of machine learning across domains. She's worked on problems from data collection and analysis strategies in autonomous marine vehicles to predicting inequity in Seattle urban data.

Before joining UW eScience, Bernease was a Software Development Engineer at Amazon, where she collaborated with operations research scientists and statisticians to optimize statistical inventory models to Amazon’s Inventory Planning and Control system. Previous to Amazon, Bernease worked on derivatives pricing and predictive modeling at the research arm of Morgan Stanley. Bernease earned her BS in Mathematics and Statistics from the University of Michigan.

She spends her time Olympic weightlifting, rowing, as well as hunting down simplified explanations and analogies for new concepts.