Hello 👋, My name is Ching Chang, also known as Jason Chang, and I am a PhD candidate (ABD) in Computer Science at National Yang Ming Chiao Tung University (NYCU), Taiwan, advised by Prof. Wen-Chih Peng. Currently, I am a Visiting Graduate Researcher in Computer Science at UCLA, working with Prof. Wei Wang.

My research focuses on Time Series Analysis, Large Foundation Models, Causal Discovery, and Multimodal Reasoning. I have published multiple papers in top AI and data science conferences and journals, including NeurIPS, AAAI, ICDE, CIKM, and ACM TIST, with total . I have also served as a reviewer for 31 papers across top-tier conferences and journals, including ICDE, KDD, NeurIPS, ICLR, AAAI, TNNLS, and ACM TIST.

If you’re interested in collaboration, feel free to contact me at blacksnail789521@gmail.com. You can also check out my CV here 📄.

📖 Educations

• 2021.09 – 2026.03 (Expected), National Yang Ming Chiao Tung University (NYCU), Taiwan, PhD in Computer Science (ABD)
• 2025.02 – 2026.02 (Expected), University of California, Los Angeles (UCLA), USA, Visiting Graduate Researcher in Computer Science
• 2016.09 – 2018.09, National Chiao Tung University (NCTU), Taiwan, MSc in Computer Science and Engineering
• 2012.09 – 2016.06, National Chiao Tung University (NCTU), Taiwan, BSc in Electrical and Computer Engineering

💻 Work Experience

• 2022.09 – 2025.02, Research Scientist (Intern), TSMC, Hsinchu, Taiwan
  Root Cause Analysis · Causal Discovery · Time Series Analysis

• 2021.01 – 2025.01, Research Scientist (Intern), GoEdge.ai, Hsinchu, Taiwan
  Time Series Analysis · Large Foundation Models · Causal Discovery

• 2019.07 – 2020.12, Machine Learning Engineer, TSMC, Hsinchu, Taiwan
  Root Cause Analysis

• 2018.04 – 2018.09, Machine Learning Engineer (Intern), EPISTAR, Hsinchu, Taiwan
  Root Cause Analysis

• 2016.07 – 2016.08, Software Engineer (Intern), MediaTek, Hsinchu, Taiwan
  Multimedia Firmware

📝 Publications

ACM TIST 2025

LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Ching Chang, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen

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• LLM4TS is a framework that adapts pre-trained Large Language Models for multivariate time series forecasting through a two-stage fine-tuning process. It captures multi-scale temporal patterns and achieves state-of-the-art performance across full-shot and few-shot settings.

ICDE 2024

TimeDRL: Disentangled Representation Learning for Multivariate Time-Series

Ching Chang, Chiao-Tung Chan, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen

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• TimeDRL is a self-supervised learning framework for multivariate time series data that learns disentangled timestamp- and instance-level embeddings without relying on augmentations. It introduces dual-level objectives for predictive and contrastive learning, and achieves strong performance across forecasting and classification tasks, even in low-label scenarios.

• AAAI 2025 (Workshop: AI4TS) PromptTSS: A Unified Model for Time Series Segmentation with Multi-Granularity States, Ching Chang, Ming-Chih Lo, Wen-Chih Peng, Tien-Fu Chen.

• NeurIPS 2024 (Workshop: Time Series in the Age of Large Models) Align and Fine-Tune: Enhancing LLMs for Time-Series Forecasting, Ching Chang, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen, Sagar Samtani.

• NeurIPS 2024 (Workshop: Self-Supervised Learning - Theory and Practice) Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series, Ching Chang, Chan Chiao-Tung, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen.

• NeurIPS 2024 (Workshop: Time Series in the Age of Large Models) Text2Freq: Learning Series Patterns from Text via Frequency Domain, Ming-Chih Lo, Ching Chang, Wen-Chih Peng.

• CIKM 2024 COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data, Ting-Yun Ou, Ching Chang, Wen-Chih Peng.

• AAAI 2024 Root Cause Analysis in Microservice Using Neural Granger Causal Discovery, Zheng-Ming Lin, Ching Chang, Wei-Yao Wang, Kuang-Da Wang, Wen-Chih Peng.

• Preprint Detecting and Ranking Causal Anomalies in End-to-End Complex System, Ching Chang, Wen-Chih Peng.

🎖 Honors and Awards

• 2024.11 Overseas Postgraduate Research Fellowship Program, National Science and Technology Council, Taipei, Taiwan
• 2024.06 International Conference Scholarship, National Yang Ming Chiao Tung University, Taipei, Taiwan
• 2024.05 International Conference Scholarship, National Science and Technology Council, Taipei, Taiwan
• 2024.02 AAAI Student Scholarship, 38th AAAI Conference on Artificial Intelligence, Vancouver, Canada
• 2022.02 Xin Miao Key Technology Doctoral Scholarship, Xin Miao Education Foundation, Taipei, Taiwan
• 2021.09 Industry-Academia Cooperative PhD Project Scholarship, Ministry of Education Republic of China (Taiwan), Taipei, Taiwan

💬 Invited Talks

• 2023.08, LLM Industry-Academia Technical Exchange Conference, National Center for High-Performance Computing
  Delivered a talk on Time Series Analysis with LLMs, discussing the use of large language models for analyzing time series data and their potential applications in industry.
  Shared the stage with Hung-Yi Lee and Hsiang-Tsung Kung.

🎓 Academic Services

• Reviewer for Conferences: ICDE’24, KDD’24, NeurIPS’24, ICLR’25, AAAI’25, KDD’25
• Reviewer for Journals: TNNLS’25, TIST’25
• Student Volunteer: AAAI’24