About Me
I’m a Computer Science master’s student at Duke University with a focus on machine learning and deep learning applications. With a 3.9 GPA from UC Davis, I’ve led projects in robust neural networks, image classification, and predictive modeling for games. My work includes a published paper on deep convolutional networks for cat breed classification, and practical ML systems like a League of Legends win predictor using local robustness techniques and a trading simulation platform. I’m experienced with Python, PyTorch, YOLO, and full-stack tools like React and FastAPI.
Education
- GPA: 3.9/4.0
- GRE: 339
Experiences
- Integrated Vue.js and Spring Boot for enhanced UX/UI and performance
- Boosted YOLOv3 image recognition accuracy through curated datasets
- Established Prometheus-based system monitoring with Grafana
- Graded assignments and provided feedback to improve problem-solving skills
Projects
WOAA-trading
- full-stack trading simulation platform using React (TypeScript), FastAPI (Python), PostgreSQL, and Redis.
LOL-Win-Prediction
- Mid-game win predictor for League of Legends using a DNN with a focus on local robustness (PGD, Jacobian, Marabou).
Chinese Handwritten Digit Classifier
- Achieved 95% accuracy on a 15,000-sample dataset using Logistic Regression and Neural Networks.
Cat Breed Classification
- A ML Classifier using YOLOv5 and VGG16, achieving 87% accuracy across five breeds
ResCash
- A decentralized bookkeeping app with React and Node.js.
BlackJack Brawl
- A strategic card game blending blackjack mechanics and unique card effects.
Publications
2024 International Conference on Modern Logistics and Supply Chain Management (MLSCM 2024), Oct 2024