I'm a Researcher in the Machine Teaching Group at Microsoft Research. "Machine teaching" is machine learning with a focus on increasing user, or "teacher", productivity and effectiveness.

My research lies at the intersection of human-computer interaction and machine learning. In particular, I create tools to support both practitioner and end-user interaction with machine learning systems. Examples include general purpose tools to support data scientists and machine learning experts building reusable predictive models for production use and application specific tools to support the average person interacting with machine learning in their everyday lives (e.g., automation technologies and recommender systems). Throughout my work, I distill guiding principles applicable in a broader context to help provide a foundation for future human-driven machine learning systems.

In 2012 I received my PhD in Computer Science from the University of Washington's Computer Science & Engineering department, where I was advised by James Fogarty. My dissertation entitled "Designing for Effective End-User Interaction with Machine Learning" won the University of Washington's 2013 Distinguished Dissertation Award. During my time in grad school, I also had the opportunity to work with some amazing people at Google Research, Microsoft Research (VIBE, ASI and TEM) and IBM Research.

Prior to UW, I completed a MSc in Computer Science at the University of British Columbia where I worked at The Laboratory for Computational Intelligence. I also have a BSc in Computer Science and Mathematics from the University of British Columbia.