Projects
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The "AI Policy for Mobile Apps" initiative is designed to develop comprehensive policies that govern the use of Artificial Intelligence in mobile applications. This project aims to address the ethical, privacy, and security challenges associated with AI in mobile environments, ensuring that AI technologies enhance user experiences without compromising user rights.
"Artificial Intelligence (AI) Ethics for Accessibility" is an initiative focused on ensuring that AI technologies used to enhance accessibility are developed and implemented according to rigorous ethical standards. The project specifically addresses the challenges and responsibilities involved in deploying AI in a manner that respects and enhances the dignity and rights of individuals with disabilities.
"Machine Learning (ML) for Accessibility " is an ambitious initiative that leverages Artificial Intelligence to break down barriers and enhance accessibility for individuals with disabilities across digital and physical realms. By integrating cutting-edge AI technologies with accessibility needs, the project aims to create smarter, more adaptive environments that are truly inclusive.
The "Natural Language Processing (NLP) for Accessibility" initiative focuses on utilizing Natural Language Processing to analyze social media interactions and bug reports to enhance digital accessibility. This project aims to extract and synthesize insights from user-generated content to improve accessibility features in software applications, particularly for individuals with visual disabilities or eye conditions.
This research project delves into the critical challenges and emerging opportunities in the field of educational accessibility,
particularly for deaf and hard-of-hearing students in online learning environments. Inspired by a series of case studies and literature reviews, the
project aims to address the specific accessibility barriers that were highlighted during the transition to online education amidst the COVID-19 pandemic.
Unit test code, just like regular/production source code, is subject to
bad programming practices, also known as anti-patterns, defects, and smells. Smells, being
symptoms of bad design or implementation decisions, has been proven to decrease the quality of
software systems from various aspects, such as making it harder to understand, more complex to
maintain, and more prone to errors bugs.