I am pursuing my Ph.D. in Computer Science (CS) from George Mason University(GMU), Virginia. I graduated with my Bachelor's and Master's in Computer Science from the Volgenau School of Engineering at George Mason University. I joined GMU's Ph.D. program in Fall 2021 and am currently working in the
SAGE Lab
advised by Dr. Kevin Moran. My research is focused on using machine learning techniques to create developer tools that allow developers to create more accessible software.
Certifications: Stanford Machine Learning
Interests: Photography, Golf, Basketball, Football (NFL), and Formula 1
My current research project is a project, MIRACLE, that aims to examine both end-user facing, and developer-facing tools for facilitating access and use of traditional software systems (e.g., web/mobile/desktop) to various populations of users with disabilities, while providing a tool for developers to create more accessible software.
Python 98%
Java 96%
Keras/Tensofrlow 90%
PyTorch 85%
Hadoop 80%
AWS Sagemaker/EC2 92%
Apache Spark 85%
Photoshop 95%
Docker/Kubernetes 75%
C & C++ 85%
- Identified research gaps in Android app accessibility for Motor-Impaired Users via a literature survey of the field
- Used Java and Python to develop MIRACLE, the world's first automated tool to detect motor-impairment accessibility issues in applications
- Built with PyTorch Computer Vision, Pattern-Matching, and Static Analysis to detect various violations in an application through screenshots and XML data
- MIRACLE achieved 87% accuracy when detecting accessibility guidelines at runtime, making it a reliable tool for developers to test their applications
- Targeting ICST '23
- Worked on a Multi-Disciplinary team with researchers and surgeons that worked to prototype a surgical voice assistant
- Designed and implemented a wake-word for surgical voice assistants using Tensorflow, Sagemaker, S3, and current research in voice assistants after consulting with surgeons and hospitals about requirements.
- Used Python, Librosa, PyAudio, and PyTorch to parse and classify windowed audio to detect the wake-word.
- Achieved 80\% accuracy on wake-word detection prototype in an input stream which exceeded expectations and is now in operation room devices across the US
- Developed a project to increase ease of communication between doctors and patients at hospitals by tracking calls, requirements, and patient to doctor communication
- Built a series of REST APIs using Node Js back-end, React front-end, and MongoDB database
- Lead weekly SCRUM meetings with offshore teams in development and integration into production
If you've made it this far, let's talk and get things rolling!
