Trustworthy AI · ML systems · Agentic AI

Building AI you can actually trust to run on its own.

I'm Tsung-Huan, an MSCS student at Georgia Tech, originally from Taiwan. Previously, I spent two years at Academia Sinica working across the data, training, and evaluation stack for LLM/VLM systems: VLM data pipelines and pre-training, LLM safety post-training, safety evaluator data, and adversarial attacks.

At Georgia Tech, I'm studying the stack behind AI systems that are capable, measurable, and deployable. Machine learning, deep learning, and deep reinforcement learning sharpen the modeling layer; data visualization and analysis and database concept & design ground the data layer; and hardware-software co-design for ML keeps me thinking about performance when models leave the notebook. In summer 2026, I'm joining LinkedIn to work on AI agent platforms.

Off the record: workouts, One Piece, and a playlist that can't decide between Chinese pop and R&B.

Timeline

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2018–2022 education

B.S. Electrical Engineering, National Taiwan University

Built my foundation in ML, NLP, computer vision, speech, and data science.

2023–2025 research

Research assistant at Academia Sinica

Worked across VLM data and pre-training, LLM safety post-training, evaluator data, and adversarial attacks.

2024 publication

NeurIPS Safe Generative AI Workshop

Presented work on preserving safety in fine-tuned LLMs.

2025–2027 education

MSCS at Georgia Tech

Broadening into ML infrastructure, deep learning, RL, data systems, and HW-SW co-design.

2026 next

AI/ML Intern at LinkedIn

Joining LinkedIn in summer 2026 to work on AI agent platforms.

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Publications

Selected papers & workshops
IEEE ASRU 2025

Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech Transformers

Tzu-Quan Lin, Tsung-Huan Yang, Chun-Yao Chang, Kuang-Ming Chen, Tzu-hsun Feng, Hung-yi Lee, and Hao Tang

Preprint

Profile-LLM: Dynamic Profile Optimization for Realistic Personality Expression in LLMs

Shi-Wei Dai, Yan-Wei Shie, Tsung-Huan Yang, Lun-Wei Ku, and Yung-Hui Li

NeurIPS Workshop 2024

Preserving Safety in Fine-Tuned Large Language Models: A Systematic Evaluation and Mitigation Strategy

Tsung-Huan Yang, Ko-Wei Huang, Yung-Hui Li, and Lun-Wei Ku

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