I’m a computer science PhD researcher interested in a simple but consequential question: what happens when the assumptions built into artificial intelligence don’t match the people using it?
My work focuses on responsible and human-centered AI, particularly language and speech technologies. I study how automatic speech recognition and language models respond to dialects, multilingual speech, code-switching, accents, and other language variation that doesn’t line up with the standards these systems were built around.
That interest grew from a broader concern with how technology defines what counts as “correct,” “normal,” or even intelligible. I want evaluation methods that separate genuine system failure from natural linguistic difference, and AI systems that adapt to people instead of requiring people to adapt to them.
I’m pursuing my PhD in Advanced & Equitable Computing at Morgan State University, where I’m a member of the Human-AI eXperience (HAX) Lab led by Dr. Naja Mack.
My academic work is informed by years of leading AI and machine learning programs in industry. That keeps my research grounded in a practical question: how do we move responsible AI from a principle to something that can be measured, governed, and implemented?
Across all of it, my goal is AI that is more equitable, transparent, and representative of the people it’s meant to serve.