Computer science student and accessibility researcher working on human-centered AI.

I am an undergraduate computer science student at West Virginia University interested in accessibility, human-computer interaction, multimodal AI, and applied machine learning.

My work focuses on understanding what AI systems can reliably infer from sound, how uncertainty should be represented, and how these systems could support more accessible digital experiences.

I am continuing my research from the WVU Summer Undergraduate Research Experience with Dr. Anthony Sicilia, studying AI uncertainty, sound understanding, and the evaluation of audio-language models.

• Accessible computing and technology for Deaf and hard-of-hearing users
• Human-computer interaction and human-centered AI
• Audio-language models and multimodal reasoning
• AI uncertainty, calibration, and trustworthy evaluation
• Spatial audio and machine sound understanding
• Accessibility in games and real-time digital environments
• Privacy-preserving and on-device machine learning
• Reproducible machine-learning research

Languages: Python, Java, TypeScript, JavaScript, Dart, SQL
Machine Learning: PyTorch, TensorFlow, TensorFlow Lite, OpenCV, NumPy, pandas
Applications: Flutter, React, Vite, Firebase, Streamlit
Research Engineering: pytest, GitHub Actions, statistical analysis, deterministic evaluation, experiment provenance
Other: Git, Linux, Windows, REST APIs, FFmpeg

I plan to pursue graduate research in human-computer interaction, accessibility, and applied AI.

I am particularly interested in research involving accessible interaction, multimodal AI evaluation, audio intelligence, uncertainty-aware systems, and technology for disabled users.

• Portfolio: kaywijerathne.com
• Email: kaywijerathne@gmail.com
• LinkedIn: Kaushika Wijerathne

Building AI systems that communicate what they know, what they do not know, and how reliably they can help.