About

• MS CS, National Taiwan University (2025–2027).
• BS CS, National Taiwan University (2021–2025, graduated)

I’m in the first year of my master’s at NTU, simultaneously working in two research labs that cover very different parts of the AI space:

• MIRLab (Multimedia Information Retrieval Lab) — where I’m doing quantitative trading research on TWSE
• MSLab (Machine Intelligence and Agentic System Lab) — where my thesis work focuses on knowledge conflicts and memory in large language models

Before grad school, I spent about 18 months as a backend engineer intern at Shopback and CMoney. I also did a research assistant at Academia Sinica’s Citi TACC lab working on synthetic data generation for cybersecurity.

Research Interests

• Knowledge conflict in LLMs: what happens when parametric knowledge (from pretraining) conflicts with contextual knowledge (from fine-tuning or prompting), and how to resolve it — including via activation steering without weight updates
• RAG safety: robustness of retrieval-augmented systems against adversarial or conflicting context; how models decide when to trust retrieved content vs. parametric memory
• DL on quantitative trading: applying deep learning to systematic strategies on TWSE — sequence models for signal generation, learned feature combination, and cross-asset transfer

Industry Interests

• AI Engineering for LLM: building production LLM systems — inference optimization, prompt pipelines, evaluation frameworks, and deployment at scale
• Infrastructure build-up: designing and scaling backend infrastructure for AI products — from serving layers to observability and reliability engineering
• Backend systems: distributed systems, high-concurrency APIs, microservice architecture — the engineering foundation that makes AI products actually work

Education

• MS, Computer Science, National Taiwan University, 2025–2027 · Expected Graduation 2027
• BS, Computer Science, National Taiwan University, 2021–2025 · Graduated

Labs: MIRLab (Multimedia Information Retrieval Lab) · MSLab (Machine Intelligence and Agentic System Lab)

Experience Timeline

Technical Profile

• Most fluent in: TypeScript, Python, C#
• Backend stack I know well: ASP.NET Core, Node.js, Kubernetes, Docker, MongoDB, PostgreSQL
• ML tools I use regularly: PyTorch, HuggingFace Transformers, PEFT/LoRA, Activation Steering
• Currently learning: Go, more CUDA-level attention mechanisms, TWSE market microstructure

Contact

ted20030214@gmail.com · GitHub · LinkedIn · (+886) 902-323-591