I'm a Researcher in the Computer Human Interactive Learning (CHIL) group at Microsoft Research. My research interests are at the intersection of human-computer interaction and machine learning. In particular, I'm interested in designing effective end-user driven machine learning for a variety of real-world applications. Concrete examples include image classification, access control in online social networks, and alarm triage in large-scale computer networks. Throughout my work, I identify challenges and opportunities for improving the interactive machine learning process and design new and balanced solutions. I also distill guiding principles applicable in a broader context, providing a foundation for future end-user interactive 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.