Engineering secure, scalable data pipelines and AI systems for medical imaging research at Emory University - bridging computer vision, clinical validation, and production-grade software that clinicians trust.
Open-source automation tool that transforms GitHub Issues into working Pull Requests using GPT-4. Analyzes tasks, generates code changes, creates branches, and opens PRs - streamlining collaboration and reducing manual effort for maintainers.
Master's thesis implementing AWS-based real-time threat detection for microservices using LSTM and Autoencoder models. Achieved 90% detection accuracy with precision and recall scores above 80%. Published in CSUN ScholarWorks.
Machine Learning model using Random Survival Forests to predict heart failure risk in Type 2 Diabetes patients. Achieved 90% accuracy and 88% specificity, highlighting clinical effectiveness for early intervention and risk assessment.
Epidemiological analysis and supervised ML forecasting system for COVID-19 global case prediction. Combined statistical modeling with machine learning to analyze pandemic trends and estimate future case trajectories.
Dynamic e-commerce platform built with Django featuring user authentication, payment gateway integration, and order management. Responsive UI crafted with HTML5, CSS3, and Bootstrap for seamless shopping experience.
GitHub search application efficiently retrieving user profiles and top 5 repositories with essential metrics including Watchers, Stars, and Forks. Simplifies GitHub data exploration with clean UI and REST API integration.
Developing scalable data pipelines for radiology AI research, integrating imaging data with clinical metadata, and validating FDA-cleared AI models in clinical environments.
Led development of Proposal Pro, an AI-powered platform that automated proposal generation, reducing manual drafting time by 60% and improving win rates by 30%.
Led MERN stack code reviews and QA, improving application performance by 30% and user task completion by 25%.
Developed chatbot framework for MyApps System using Microsoft Bot Framework, reducing ticket volume by 30% and improving authentication security by 20%.
Led website overhaul using MERN stack, achieving 30% faster load time and enhanced mobile responsiveness with 95% client satisfaction rate.
I'm a Systems Software Engineer and AI Researcher at Emory University, focused on building reliable, scalable machine learning systems for medical imaging.
My work bridges computer vision, clinical validation, and production-grade software engineering - from developing secure data pipelines for radiology datasets to validating FDA-cleared AI models in real-world clinical environments.
Previously, I led the development of Proposal Pro at iQuasar, an AI-powered platform that reduced proposal drafting time by 60% and contributed to a 30% increase in RFP win rates. I've also contributed to open-source projects and conducted research in cybersecurity and healthcare ML.
I care deeply about building systems that are not only technically excellent but also interpretable, reproducible, and trustworthy. My research interests include uncertainty quantification in medical AI, attention mechanisms for interpretable diagnosis, and ethical deployment of ML models in clinical settings.
Open to collaboration on research projects, open-source contributions, healthcare AI initiatives, or consulting opportunities.