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From financial fraud detection and traffic-flow forecasting to AI explainability and fairness — I believe good models should not only be accurate, but also able to explain themselves.
AI/ML Algorithm Engineer @ Ernst & Young Taiwan · 2025/07 — Present
- 🤖 RAG Knowledge System — Leading architecture & development of an internal RAG-based knowledge management system (PoC), running locally on Llama 3.1 via Ollama.
- 🛒 Retail ML Recommender — Real-time product recommendation engine for a major retail chain (PoC), combining user behavior with product nutrition signals, refreshed in 5-minute batches.
- 🏦 Banking Credit Scoring — Owning credit-scoring model development for a banking client. Completed SAS Viya certification and supported the client's platform onboarding.
- 🧪 AI Model Governance — Owning fairness, explainability and AI capability evaluation for enterprise clients (SHAP analysis + multi-dimension fairness audit).
- 🎓 EY Corporate Mentor × NTU Accounting — Mentoring a 6–7 student team on a 16-week, 3-hour-per-week RAG project (Spring 2026).
| 🥇 #1 | Class rank, M2 S1 score 97/100 — Tamkang Statistics |
| 🎓 Phi Tau Phi | Honorary member, nominated by Tamkang Stats (2026) |
| 🛡️ Hackathon Finalist | DIGITIMES × AWS · Agent for Truth 2026 — BitoGuard |
| 🥈 2nd Place (1st vacant) | National Highway Intelligent Traffic Competition 2025 |
| 🎤 Invited Speaker | Chinese Institute of Transportation Annual Conference 2025 |
| 🏆 Best Popularity + Merit | Academia Sinica Data Science Stroll 2024 — dual award |
Hackathon finalist · DIGITIMES × AWS 2026
AI-driven crypto fraud detection across 63,770 users · 129,545 graph edges. HeteroSAGE + GAT heterogeneous GNN with stacking ensemble (XGBoost + LightGBM + CatBoost) on a 78-dim feature space. SHAP explainability and 4-dimension fairness audit baked in.
2nd Place (1st vacant), National Highway Comp. 2025 · Invited talk at Transportation Annual Conference
Dual-engine architecture combining MT-STNet (spatio-temporal GNN) with LWR shockwave theory for accurate congestion prediction and real-time alerts. RAG decision support adds explainability on top of prediction.
Best Popularity + Merit Award · Academia Sinica 2024
First application of Generalized Association Plots (GAP) to cyberbullying research. Combined with PCA, factor analysis and CCA, identified 5 user clusters and 4 behavioral factors from 672 respondents.
AI / ML · PyTorch · PyG · GNN (HeteroSAGE / GAT) · RAG · LLM · Ollama · XGBoost · LightGBM · CatBoost · SHAP
Languages · Python · TypeScript · Java · R · SAS · SQL
Frontend · Vue 3 · React 18 · Tailwind · D3.js · Three.js
Backend & Infra · FastAPI · Node.js · Spring Boot · Docker · AWS · Linux · MySQL
Statistics · Multivariate Analysis · GAP · PCA · CCA · Factor Analysis
Specialized · SAS Viya · MT-STNet · Fairness Audit
Certified · Azure AZ-900 · AI-900 · DP-900 · SAS Base · ESG Junior Manager
- 📰 When 97% of Your Data Lies — Extreme Imbalanced Classification in Crypto AML · Medium · 2025
- 📰 Building a Portfolio in One Day with AI — Claude Code Collab Reality Check · Medium · 2025
Open to AI/ML collaborations, talks, and tech discussions.
chwei9181@gmail.com




