Information Director

Information Director

-  109 posts

Sushant is a Ph.D. candidate at the Golisano College of Computing and Information Sciences at the Rochester Institute of Technology, where he specializes in accessibility for people with disabilities, human-computer interaction and computational linguistics. He is interested in building machine learning (ML) systems that model human communication with a goal to enhance human-to-human or human-to-machine interaction.

2008 Best Paper

Computer Usage by Young Individuals With Down Syndrome: An Exploratory Study

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Jim Thatcher: Outstanding Contribution Recipient 2008

The development of one of the first screen readers for DOS, and the first screen reader for a graphical user interface on the PC.

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How to Integrate Wireless Technology with Web Services Technology to Support and Enhance Sign Languages Learning?

This project researches a new concept of Sign Language mobile learning.

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Taux: A System for Evaluating Sound Feedback in Navigational Tasks

This thesis presents the design and development of an evaluation system for generating audio displays that provide feedback to persons performing navigation tasks.

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Using a Common Accessibility Profile to Improve Accessibility

A reference model is presented to act as a theoretical foundation. This Universal Access Reference Model (UARM) focuses on the accessibility of the interaction between users and systems, and provides a mechanism to share knowledge and abilities between users and systems.

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ASSETS 2007

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Evaluation of a Haptic Tongue Device

This thesis improves a prototype device that has been built to fit onto the tongue and receive visual information in a tactile form

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2007 Best Student Paper

Slipping and Drifting: Using Older Users to Uncover Pen-Based Target Acquisition Difficulties

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2007 Best Paper

Evaluating American Sign Language Generation Through the Participation of Native ASL Signers

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Individual-Technology Fit: Matching Individual Characteristics and Features of Biometric Interface Technologies with Performance

biometric describes physiological measures that may be used for non-muscularly controlled computer applications, such as brain-computer interfaces

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