A collection of AI, machine learning, and software engineering projects showcasing innovation and technical expertise.
Production-ready ETL pipeline with RAG capabilities for clinical trials data. Built with PySpark + Delta Lake for data processing, Great Expectations for validation, OpenAI embeddings with FAISS vector search, and multilingual GPT-4 query interface. Features comprehensive audit trails and enterprise-grade architecture.
Developed FDA-compliant ML models for early cancer detection at Memorial Sloan Kettering. Achieved 92% sensitivity and 88% specificity on EHR data from 15K+ patients. Implemented SHAP explainability framework for clinical interpretability and conducted external validation across 3 hospital systems.
Developed real-time ML system for EMG-based wearable-robotic communication at AIR LAB. Improved system reliability by 25% and reduced latency by 40%. Implemented custom signal processing pipeline handling 1000+ Hz biosensor data with sub-10ms response time.
Developed physics-informed ML models for yield strength prediction in metallic materials at POSTECH. Achieved 15% improvement in prediction accuracy over traditional methods. Published in Acta Materialia (Impact Factor: 9.4) with 50+ citations.
Developed full-body pose estimation system using miniature wrist camera for privacy-preserving body tracking. Published in ACM IMWUT (Impact Factor: 7.9). Custom CNN architecture achieved real-time performance with 95% accuracy on pose keypoints while maintaining user privacy.
Developed personalized job coaching system using GPT-4 and RLHF at Georgia Tech. Implemented LoL-RL (Learning-over-Learning Reinforcement Learning) technique with co-design studies. Improved job placement rates by 35% across 200+ participants in pilot study.
Developing NLP models for automated science fiction story structure analysis at Cornell Sci-Fi Lab. Built generative models for narrative pattern recognition and story arc prediction. Processing 10K+ sci-fi texts with transformer-based architecture achieving 78% accuracy on story structure classification.
Built iOS app with Arduino biosensor integration for real-time emotion and pain tracking. Achieved 85% accuracy on emotion classification using custom CNN architecture. Deployed for 500+ patients, reducing manual assessment time by 60% and enabling immediate clinical interventions.