Saleema Amershi

I'm a PhD student in Computer Science & Engineering at the University of Washington advised by James Fogarty. I'm also a member and former student coordinator of the dub group, an interdisciplinary HCI group at the University of Washington that includes faculty, graduate students and industry researchers from Computer Science, the Information School, Human Centered Design and Engineering, DXARTS and other on campus departments as well as local industrial research labs. My research interests are in human-computer interaction, statistical machine learning, intelligent user interfaces and user modeling. I'm currently working on designing new approaches for enabling effective end-user interactive machine learning.

I have a M.Sc. in Computer Science from the University of British Columbia where I worked in The Laboratory for Computational Intelligence with Cristina Conati on a machine learning based framework for user modeling. I also have a B.Sc. in Computer Science and Mathematics from the University of British Columbia.

My research interests are in designing new approaches to enable effective end-user interactive machine learning. While developers skilled in statistical machine learning have been successful in building intelligent systems to enhance human productivity and capabilities with large unstructured data sets, a fundamental limitation of relying on developers to provide these capabilities is that developers cannot possibly foresee the countless variety of distinctions end-users might want to make within large datasets in pursuit of their every day goals. A promising solution, therefore, is to enable people to interactively train machine learning systems themselves. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, a better understanding is needed of how to design effective interaction with interactive machine learning systems. I am examining this question in a variety of interactive machine learning systems, aiming to broaden interaction with large unstructured data and to accelerate the integration of intelligent computing into our everyday lives.