Conference Video: Catherine Nelson, PhD Practical Privacy-preserving Machine Learning
This is Catherine Nelson PhD’s tech talk from the WiDS Puget Sound Conference 2020. Enjoy!
ABSTRACT:
What if we could build accurate machine learning models while preserving user privacy? There’s a growing number of tools to help, from federated learning to encrypted ML. In this talk, I’ll review what works, what doesn’t work, and where these tools fit in a machine learning pipeline.
Catherine Nelson is a Senior Data Scientist for Concur Labs at SAP Concur, where she explores innovative ways to use machine learning to improve the experience of a business traveler. Her key focus areas range from ML explainability and model analysis to privacy-preserving ML. She is also co-author of the forthcoming O'Reilly publication “Building Machine Learning Pipelines", and she is an organizer for Seattle PyLadies, supporting women who code in Python. She has been recognized as a Google Developer Expert in machine learning. In her previous career as a geophysicist she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University.