Kuan-Ting Chen

I will join UCLA as a Computer Science PhD student in Fall 2026!

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andyngewcz2d@gmail.com

Abu Dhabi, UAE

/Taipei, Taiwan

I am a research engineer at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and National Taiwan University, working with Professor Chih-Jen Lin.

My research interests include:

  • Extreme multi-label classification, with active contributions to LibMultiLabel, a library for multi-class and multi-label classification.
  • Efficient machine learning, focusing on accelerating large language model (LLM) inference and enabling sparse computation.

In addition, I help maintain LIBSVM and LIBLINEAR, well-established libraries for support vector machines and large-scale linear models.

Before joining MBZUAI, I spent three years working as a machine learning engineer at Taiwan Semiconductor Manufacturing Company (TSMC) and Yahoo, where I gained hands-on experience developing machine learning models and conducting data analysis for industrial applications.

I received my M.S. in Statistics from National Chengchi University, Taiwan, in 2020, where I was fortunate to be advised by Professor Chiu-Hsing Weng, and my B.S. in Mathematics from National Central University, Taiwan, in 2018.

news

May 29, 2026 Our work on probability estimation for extreme multi-label learning has been accepted for presentation at the ECML PKDD 2026 Research Track!
Oct 14, 2025 Our ICDM 2025 paper was selected as a Best Paper Award candidate! 🎉
Aug 26, 2025 My work on Revisiting One-Versus-One and One-Versus-Rest has been accepted as a regular paper at IEEE ICDM 2025.

selected publications

  1. Multi-Class
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    Revisiting one-versus-one and one-versus-rest: insights into imbalanced multi-class classification
    Kuan-Ting Chen and Chih-Jen Lin
    In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2025
  2. Extreme Multi-Label
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    Probability Estimation in Tree-Based Linear Methods for Extreme Multi-Label Learning
    Sheng-Wei Chen, Kuan-Ting Chen, Guan-Ting Chen, and 2 more authors
    In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2026

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