I am a Researcher in the Adaptive Systems & Interaction Group at Microsoft Research AI. I received my PhD in 2012 from the Paul G. Allen School of Computer Science & Engineering at the University of Washington (UW). That year, my dissertation entitled “Designing for Effective End-User Interaction with Machine Learning” won UW’s Distinguished Dissertation Award. Prior to UW, I completed a MSc in Computer Science and a BSc in Computer Science & Mathematics at the University of British Columbia where I worked at The Laboratory for Computational Intelligence on Tools for Learning Artificial Intelligence. Through the years, I have also had the opportunity to work at Google Research and IBM Research.
My research lies at the intersection of human-computer interaction (HCI) and artificial intelligence (AI). Specifically, I create technologies to make people better at building and using machine learning. Examples include developing general purpose tools for data scientists and engineers building reusable predictive models (e.g., interactive platforms and visualization tools for machine teaching) and application specific techniques for end-users interacting with machine learning-driven systems in their everyday lives (e.g., intelligent assistants, recommender systems, and personalized search). Throughout my work, I distill guiding principles applicable in broader contexts to establish a foundation for human-AI interaction. Most recently, I lead an effort to develop general Guidelines for Human-AI Interaction, a unified and validated set of guidelines for designing intuitive interactions between people and AI-based systems.
I currently chair the Aether working group on Human-AI Interaction and Collaboration at Microsoft. Aether is a company-wide initiative focusing on responsible AI. I also serve on the Partnership on AI’s Collaborations Between People and AI Systems Working Group.