• Machine Learning Engineer at Adobe Search with a Master’s in Machine Learning at Carnegie Mellon University.
• Focused on applied machine learning, particularly in search and recommendation systems, with experience at Adobe Search and Microsoft Bing Advertising.
• Conducted NLP research at MBZUAI and ASUS-AICS. My work Linear Classifier: An Often-Forgotten Baseline for Text Classification has won ACL Outstanding Paper Award.
• Contributor to LibMultiLabel, a library for multi-class and multi-label classification.

Education

• Carnegie Mellon University, MS in Machine Learning, Aug. 2023 - Dec. 2024
• National Taiwan University, BS in Computer Science, Sep. 2017 - Jan. 2023

Professional Experience

• Machine Learning Engineer @ Adobe Search, San Jose, Feb. 2025 - Present
• Machine Learning Engineer Intern @ Adobe Search, San Jose, May 2024 - Aug. 2024
  Multimodal prompt generation for prompt suggestions and asset recommendation.
• Research Assistant @ MBZUAI, Abu Dhabi, May 2023 - July 2023
  Text classification and long document classification.
• Research Intern @ ASUS-AICS, Taipei, Mar. 2022 - Dec. 2022
  Development of NLP software LibMultiLabel for text classification.
• Software Engineer Intern @ Microsoft Bing Advertising, Taipei, July 2021 - Feb. 2022
  Embedding nearest neighbor search and keyword recommendation for advertising.

Projects

• Efficiency Enhancement in LLM Training
  Utilized CuPy for GPU arrays and enabled mixed precision training with MiniTorch for LLM development.
• A Baseline for Text Classification
  Concluded simple linear baselines must be conducted to ensure the proper use of the advanced LLMs.

Publications

• Linear Classifier: An Often-Forgotten Baseline for Text Classification
  Yu-Chen Lin, Si-An Chen, Jie-Jyun Liu, Chih-Jen Lin
  ACL 2023 (Outstanding Paper Award)