MS in ECE @ Duke University exploring deep learning, ML systems, and applied AI.
I’m someone who gets deeply absorbed in problems. Once something catches my attention, it rarely stays confined to my laptop, it follows me everywhere. I like researching how others approached a problem, understanding the reasoning behind different systems, breaking ideas apart on a whiteboard, and slowly building them back into something practical and real.
A lot of my projects start with curiosity: “What would it actually take to make this work outside a paper?”
That curiosity has pulled me into projects involving healthcare forecasting, wildfire detection, drone simulation, computer vision, and ML pipelines. I enjoy working at the intersection of research and engineering taking ideas apart, experimenting, and building end-to-end systems that solve real-world problems.
Lately, I’ve been especially interested in:
- Deep Learning & Computer Vision
- ML Systems Engineering
- Healthcare AI
- Time-Series Forecasting
- LLM + RAG Systems
- Data Pipelines & Orchestration
Built a Transformer + LSTM forecasting pipeline using longitudinal clinical records and retrieval-augmented explanations to predict disease progression over time.
Worked on wildfire detection research involving computer vision, edge optimization, and drone simulation environments for autonomous monitoring systems.
Built a production-style ETL workflow using Apache Airflow and PostgreSQL for ingesting and processing semi-structured event data.
Trained deep CNNs in PyTorch to classify focus, tracking, wind, and exposure errors in astronomical imagery.
- Time-Series Transformers
- Retrieval-Augmented Generation (RAG)
- ML Systems Engineering
- Healthcare AI