Erin T. Solovey, Ph.D.
Assistant Professor of Computer Science
WPI Research opportunities: I am currently recruiting motivated Ph.D., Masters and undergraduate students interested in Human-Computer Interaction research. Contact me if interested. Read more...
I am currently recruiting motivated students interested in doing research in human-computer interaction (HCI), specifically in the area of accessibility for the Deaf community. I also have ongoing projects on emerging interaction modes and techniques such as brain-computer interfaces, physiological/wearable computing, affective computing, tangible interaction, and reality-based interaction. Many of my projects also involve applying machine learning techniques to human-computer interaction, and/or human interaction with complex and autonomous systems and vehicles. Before contacting me, please take a look at my research page and my publications. To learn more about faculty research interests at WPI, please see our Research page.
I am also looking for students specifically for two recently funded NSF grants.
In the United States, American Sign Language (ASL) is the primary language of many deaf adults, and many deaf students receive classroom instruction in ASL while learning English as a second language. However, most interactive computing tools are presented and navigated exclusively in English, even those designed for deaf audiences. Making access to technology contingent upon a sufficient command of a second language creates significant barriers and access delays for deaf individuals. This project takes a human-centered computing approach to build a foundation that advances understanding of how deaf individuals could work and learn in environments that are designed with their needs and preferences at the forefront. It investigates the feasibility and effectiveness of new SL1 technology, which will provide delivery of signed language (SL) content by allowing deaf signers to navigate, search, and interact with technology completely in their first language (L1). The optimization of SL1-based user interfaces has never before been attempted and could lead to a breakthrough in historic communication and learning barriers; determining preferences, needs and optimized presentation of information for Deaf users will benefit this population and future populations of ASL signers. Technology that is truly accessible to deaf SL-signers has the power to facilitate lifelong learning, enhance access to educational content such as STEM topics, improve career opportunities, and allow SL-based organization of SL corpora, assessments, dictionaries, learning and employment resources. This work will directly impact deaf individuals, parents, interpreters, teachers, and students studying SL. Direct collaboration with deaf graduate and undergraduate students, deaf faculty, and deaf researchers, along with several partner schools for the deaf will ensure that the Deaf community has an instrumental leadership role in the design of future tools that meet their needs.
Abstract: From elementary school math games to workplace training, computer-based learning applications are becoming more widespread. With these programs, it becomes increasingly possible to use the data generated, such as correct and incorrect problem-solving responses, to develop ways to test for student knowledge and to personalize instruction to student needs. The logs of student responses can capture answers, but they fail to capture critical information about what is happening during pauses between student interactions with the software. This project will explore the use of measurements of brain activity from lightweight brain sensors alongside student log data to understand important mental activities during learning. Using brain imaging, the project team will examine whether students are thinking deeply about the problem or mind-wandering during pauses in the learning tasks and use the combined log and brain data to make predictions about learning outcomes. This work will build a foundation for new methods of combining neuroimaging, machine learning, and personalized learning environments. With a better understanding of when and how learning occurs during pauses in tutoring system use, learning technology researchers and developers will be able to create adaptive interventions within tutoring systems that are better personalized to the needs of the individual.News: A new kind of thinking cap.
NSF highlighted our recent grant in this episode of NSF360.
Prospective CS PhD Students: Apply here! (Funded as Research/Teaching Assistant)
In addition to computer science, WPI also has graduate programs in data science, learning sciences, interactive media and game design, neuroscience, robotics engineering, bioinformatics and computational biology.
Current Openings for Masters students (possibility of funding): I am also looking for Masters degree students. We have a limited number of slots for funded Masters degrees, where you would be hired as a teaching assistant. The thesis option is a great opportunity for developing research experience that is valuable for pursing a PhD in the future, or for going into industry. To learn more about faculty research interests at WPI, please see our Research page.
Prospective CS Masters Students: Apply here! (Teaching Assistantships Available)
Key learning objectives include gaining an understanding of interdisciplinary human-computer interaction research methods, experience developing robust software following current best practices in software engineering, exposure to machine learning methods and training in experimental design and analysis.
WPI's Computer Science department in Worcester, MA (an hour outside of Boston) is an exciting place to study human-computer interaction, with outstanding faculty and a wonderful community. If you are applying to one of the graduate programs, and are interested in working in my group, make sure to indicate this as well as your research interests in your essay.