The Intelligent Editing Project seeks to apply neural networks and other modern machine learning techniques to furnish editorial assistance. We look beyond traditional grammatical error checking to focus on facilitating writers by providing them with fluent, meaningful text editing support that is appropriate to their objectives and their targeted readership. Our interests include sentence compression and summarization, paraphrasing and stylistic variation, and writing assistance for non-native writers. The MSR Abstractive Text Compression Dataset described in our…
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.
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…
Established: June 17, 2008
A large body of human-computer interaction research has focused on developing metaphors and tools that allow people to effectively issue commands and directly manipulate informational objects. However, with the advancement of computational techniques such as machine learning, we now have the unprecedented ability to embed 'smarts' that allow machines to assist and empower people in completing their tasks. We believe that there exists a computational design methodology which allows us to gracefully combine automated services with…
Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning EnvironmentsSaleema Amershi, CRISTINA CONATI , in Journal of Educational Data Mining, September 1, 2009,
Using Feature Selection and Unsupervised Clustering to Identify Affective Expressions in Educational GamesSaleema Amershi, Cristina Conati, Heather Maclaren, in In Proceedings of The Intelligent Tutoring Systems Workshop on Motivational and Affective Issues in ITS (ITS 2006), January 1, 2006,
August 15, 2017
March 24, 2017
Goldsmiths, University of London, UK
October 6, 2016
June 16, 2016
February 27, 2015
Ran Gilad-Bachrach, Ali Farhadi, Saleema Amershi, Tyler Johnson, and Christopher Lin
Microsoft Research, University of Washington, Microsoft