About

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.

Projects

Platform for Interactive Concept Learning (PICL)

Established: April 28, 2015

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The system leverages big data to find examples that maximize the training value of its interaction with the teacher. Building classifiers and entity extractors is currently an inefficient process involving machine learning experts, developers and labelers. PICL enables teachers with no expertise in…

PICL: Platform for Interactive Concept Learning

Established: August 25, 2014

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The system leverages big data to find examples that maximize the training value of its interaction with the teacher. Building classifiers and entity extractors is currently an inefficient process involving machine learning experts, developers and labelers.  ICE enables teachers with no expertise in…

Publications

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

Intelligence in Wikipedia
Daniel S. Weld, Fei Wu, Eytan Adar, Saleema Amershi, James Fogarty, Raphael Hoffmann, Kayur Patel, Michael Skinner, in In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 08) Senior Papers Track, January 1, 2008, View abstract, Download PDF

2007

2006

2005

Projects

Other