Nguyen Cong Phat (James)

Role: Leader – Course: CS231.Q11 – Introduction to Computer Vision (UIT – VNU-HCM).
Led and coordinated the end-to-end development of a face mask classification system, defining the experimental methodology and driving a comparative evaluation between traditional ML pipelines (HOG/LBP + SVM, KNN, Random Forest) and CNN-based models.
Designed, implemented, and deployed the full computer vision pipeline, including data preprocessing, feature extraction, hyperparameter optimization (Optuna, Keras Tuner), and real-time inference via OpenCV (YuNet) and Flask, achieving up to 98.99% test accuracy and >25 FPS real-time performance.

Python Computer Vision OpenCV Machine Learning CNN HOG LBP Flask

Role: Leader – Course: IE403.Q11 – Social Media Mining (UIT – VNU-HCM).
Initiated and led the HARE research framework by formulating the core hypothesis on rationale-based explainable hate speech detection and designing the fine-tuning strategy for Qwen2.5-3B using QLoRA (4-bit) and Chain-of-Thought prompting.
Constructed and curated an enhanced ViTHSD dataset by enriching 2,333 samples with rationale and implied-statement annotations, and implemented a two-stage semantic alignment pipeline (HARE) combining label supervision with reasoning-based learning.
Evaluated and deployed the system through a full-stack application (FastAPI + React/Vite), achieving F1 = 0.6026 on the ViTHSD test set and delivering a +17% F1 improvement on political hate speech compared to vanilla Qwen models.

Python NLP Data Science LLM Explainable AI Qwen2.5 QLoRA FastAPI React

Role: Leader – Course: CS221.Q12 – Natural Language Processing (UIT – VNU-HCM).
Led and designed the end-to-end Vietnamese NER project by defining the experimental framework and implementing three sequence labeling models (HMM, CRF, BiLSTM-CRF) on the VLSP 2016 dataset (CoNLL format).
Evaluated and benchmarked model performance using token- and entity-level metrics, with the CRF model achieving Acc = 0.9904, Token F1 = 0.9901, Non-O F1 = 0.9076, and Span F1 = 0.9191 on the test set.
Developed and deployed a Flask-based interactive demo for model comparison, real-time entity highlighting, and result visualization, ensuring reproducibility through archived models and training artifacts.

Python NLP HMM CRF BiLSTM-CRF PyTorch Flask

Analyzed over 113K+ customer transactions and built interactive Power BI dashboards to visualize service usage patterns and revenue flows.
Segmented customers by geography, asset value, and service adoption to identify “Gold” customers contributing 78% of total assets.
Implemented customer profiling to classify Regular, Silver, and Gold segments, uncovering high cross-sell potential.
Generated actionable insights and proposed data-driven campaigns that supported a +20% quarterly revenue growth target.

SQL Power BI Data Analytics

Developed an end-to-end pipeline to extract feedback data from MongoDB and transform it for MySQL storage using Python.
Implemented a Vietnamese text classification model using phoBERT, achieving a 92% F1-score on validation data.
Created an interactive dashboard to visualize customer sentiment and inform service quality improvement initiatives.

Python MongoDB MySQL phoBERT

Role: Feature Engineering \& Model Development Lead.
Led EDA and engineered new features through correlation-based elimination and data cleaning to enhance signal quality.
Developed and optimized tree-based models (XGBoost, LightGBM, CatBoost) via 5-fold cross-validation, achieving an F1-score of 0.8939 and AUC of 0.9631.
Authored and presented the final report, translating technical findings into clear narratives for academic evaluation.

Python XGBoost LightGBM CatBoost

Developed a real-time Streamlit dashboard analyzing capital flow and technical indicators in the Vietnamese stock market using vnstock API.
Visualized inflow/outflow trends, bid-ask ratios, and capital movement heatmaps through matplotlib and seaborn.
Optimized user interactivity with dynamic ticker input, time range selection, and statistical summaries for quick insights.

Python Streamlit vnstock Data Visualization

HTML CSS JavaScript

Bootstrap 5 WOW.js Owl Carousel Web Design

Developed an interactive Rock–Scissors–Paper game using Python + OpenCV for real-time hand gesture recognition via webcam feed.
Implemented an automated opponent system and visualized player–computer outcomes using OpenCV overlays.
Designed modular code with a custom hand detection library for scalability and future GUI enhancements.