AI Student at FPT University — Da Nang Campus
Deep Learning · Computer Vision · Time-Series Forecasting · Applied Machine Learning
- AI student focused on building reproducible, evidence-backed machine learning projects.
- Interested in computer vision, deep learning, time-series forecasting, and practical AI deployment.
- Experienced with experiment design, model comparison, evaluation, error analysis, and technical documentation.
- Currently building a portfolio for AI/ML internship opportunities.
| Project | What it demonstrates | Verified result |
|---|---|---|
| Tomato Leaf Disease Classification | Fixed-data comparison of lightweight CNNs, strong CNN backbones, and Swin Transformer across six real-world disease classes | Swin-Tiny: 87.2% highest single-run accuracy; 84.8% ± 0.8% across three seeds |
| YOLOv7 Head Detection & People Counting | YOLOv7 head detector for CCTV video with per-frame occupancy counting and a reproducible data pipeline | 0.981 mAP@0.5, 0.960 precision, 0.949 recall |
| Hung Yen Water-Level Forecasting | Multivariate RNN, LSTM, and Transformer forecasting over 24-, 72-, and 120-hour horizons | 24h LSTM: R² 0.9313; best models improve RMSE over persistence by 14.09–19.00% |
| E-commerce Purchase Intention in R | Statistical testing, leakage-safe modeling, threshold tuning, and bootstrap confidence intervals for an imbalanced classification task | Decision Tree: F1 0.647, ROC AUC 0.843 |
| CSRNet Crowd Counting in PyTorch | Clean CSRNet implementation with density-map generation, training, evaluation, inference, and tests | Architecture and pipeline implemented; no unsupported benchmark claim |
| Tomato Disease AI Web App | Streamlit deployment layer for six-class Swin-Tiny inference with validation, Docker, tests, and CI | Deployment code linked to the verified tomato benchmark |
- Computer Vision: image classification, object detection, crowd counting, transfer learning, CNNs, Swin Transformer, YOLOv7, CSRNet
- Time Series: multistep forecasting, RNN, LSTM, Transformer, temporal validation, persistence baselines
- Machine Learning: preprocessing, feature analysis, class imbalance, statistical testing, model selection, threshold tuning
- Engineering: reproducible notebooks, testing, CI, GitHub Actions, Docker, Streamlit, clear project documentation
I am extending my portfolio from experimental notebooks into reproducible AI repositories and deployable demos. My next goals include tracking-based people counting with ByteTrack, model explainability, and lightweight deployment.
Open to AI/ML internship opportunities and technical collaboration.