Thái Hoài An

Built a near real-time Business Intelligence system for KKBox churn monitoring and retention decision support. The project combines Kafka log replay, Spark Structured Streaming, ClickHouse OLAP storage, FastAPI APIs, and a React dashboard to deliver descriptive, predictive-proxy, and prescriptive analysis in one workflow.

TomatoHub is a full-stack platform for charity operations that helps organizations launch campaigns faster, lets supporters donate or volunteer with clearer trust signals, and keeps campaign activity transparent. The product combines role-based workflows, QR-based check-in/check-out, public transparency logs, and AI-assisted campaign drafting plus supporter recommendations.

UEH NLP course final project building an end-to-end Information Extraction system for Vietnamese medical text. Implemented a pipeline architecture (NER → Entity Pairing → Relation Extraction) inspired by PURE, recognized 5 entity types and 4 relation types, and used semi-supervised hybrid learning with silver data to overcome limited labeled data.

Data Mining course final project building comprehensive analytics system for 14 Vietnamese banking stocks in VN30 index. Integrated multi-source data (market OHLCV, financial reports, macro indicators, news sentiment) and developed 4 XGBoost models: Return Regression, Direction Classification, Risk Forecasting, and Regime Detection. LLMs layer provides reasoning and investment recommendations in natural language.

Data Visualization course final project building weather prediction model for 34 Vietnamese provinces using Softmax Regression. Collected 265K+ records from Open-Meteo API (2005-2025), engineered lag features, cyclic encoding for seasonality, and accumulation features. Classified weather into 3 groups (Clear/Cloudy, Drizzle, Rain) with rigorous train/test split by time.

Faculty-level research project providing comprehensive comparative evaluation of machine learning approaches for Vietnamese fake news detection. Systematically analyzed three major model families: traditional deep learning (BiLSTM with Word2Vec/FastText), transfer learning (PhoBERT frozen/fine-tuned), and large language models (Qwen2.5-7B, Llama-2-7B, DeepSeek) across zero-shot and few-shot paradigms. Evaluated on ReINTEL dataset (9,713 Vietnamese social media posts with 83.2% real vs 16.8% fake class imbalance).

Faculty-level research comparing Sequential and Multi-task Learning architectures for breast cancer diagnosis from ultrasound images. Built on U-Net with EfficientNet-B4 backbone, systematically evaluated Deformable Convolution and Capsule Network modules through ablation study. Evaluated on BUSI dataset (780 images: Normal/Benign/Malignant) with rigorous statistical testing (Shapiro-Wilk, Mann-Whitney U, Kruskal-Wallis, Tukey HSD).

Artificial Intelligence course final project applying Genetic Algorithm to solve Maximum Network Flow Problem. Implemented custom GA operators: path-based crossover to maintain flow conservation, adaptive mutation for escaping local optima, and balance flow mechanism. Built interactive Python GUI for real-time graph editing, parameter tuning, and algorithm comparison with Ford-Fulkerson.

Collaborated with a cross-university team to build a logistics solution combining data management, ML allocation, and IoT warehouse tracking to reduce food waste.

Minute standardizes the meeting lifecycle for BFSI/LPBank enterprises: pre-meeting context gathering, real-time in-meeting assistance, and post-meeting minutes + action items generation—all with citations, audit trails, and access control. Built with SAAR (Self-aware Adaptive Agentic RAG) architecture featuring stage-aware routing (Pre/In/Post), real-time WS pipeline (audio → SmartVoice STT → session bus → live transcript/recap/ADR), permission-aware RAG with pgvector, and tool-calling with human-in-the-loop confirmation.