I'm a Researcher in the Machine Teaching Group at Microsoft Research. My research lies at the intersection of human-computer interaction and machine learning. In particular, I design and develop tools to support both end-user and practitioner interaction with interactive machine learning systems. Throughout my work, I identify challenges and opportunities for improving the interactive machine learning process and design solutions that balance the needs of both the user and the machine. I also 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. 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.
My homepage is at: http://research.microsoft.com/~samershi
- Saleema Amershi, Max Chickering, Steven M. Drucker, Bongshin Lee, Patrice Simard, and Jina Suh, ModelTracker: Redesigning Performance Analysis Tools for Machine Learning, in Proceedings of the Conference on Human Factors in Computing Systems (CHI 2015), ACM – Association for Computing Machinery, April 2015.
- Saleema Amershi, Maya Cakmak, W. Bradley Knox, and Todd Kulesza, Power to the People: The Role of Humans in Interactive Machine Learning, in AI Magazine, AAAI - Association for the Advancement of Artificial Intelligence, December 2014.
- Patrice Simard, David Chickering, Aparna Lakshmiratan, Denis Charles, Léon Bottou, Carlos Garcia Jurado Suarez, David Grangier, Saleema Amershi, Johan Verwey, and Jina Suh, ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems, no. MSR-TR-2014-128, 16 September 2014.
- Todd Kulesza, Saleema Amershi, Rich Caruana, Danyel Fisher, and Denis Charles, Structured Labeling for Facilitating Concept Evolution in Machine Learning , in Proceedings of the Conference on Human Factors in Computing Systems (CHI 2014), ACM, May 2014. Best Paper Award