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Chih-Jen Lin
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Chih-Jen Lin | |
|---|---|
林智仁 | |
| Born | 1971 (age 54–55)[1] |
| Education | National Taiwan University (BS) University of Michigan (MS, PhD) |
| Known for | LIBSVM |
| Awards | ACM Fellow (2015) AAAI Fellow (2014) IEEE Fellow (2011) |
| Scientific career | |
| Fields | machine learning data mining optimization |
| Workplaces | National Taiwan University |
| Thesis | Studies in large-scale optimization |
| Website | www |
Chih-Jen Lin (Chinese: 林智仁; pinyin: Lín Zhìrén) is a Taiwanese computer scientist who is a distinguished professor of computer science at National Taiwan University, and a leading researcher in machine learning, optimization, and data mining. He is best known for the open-source library LIBSVM, an implementation of support vector machines.[2] With his students he also developed LIBLINEAR, a widely used library for large-scale linear classification.[3]
Biography
[edit]Chih-Jen Lin earned a B.S. (1993) in mathematics at National Taiwan University, and an M.S.E. (1996) and a Ph.D. (1998) in Industrial and Operations Engineering at the University of Michigan.[4][5]
Lin's machine-learning software has been adopted by industry, with his libraries used by Yahoo, Google, eBay, and Microsoft.[6] He has held visiting research positions, as a visiting scientist at Yahoo! Research (2006–2007) and visiting professor at Google Research (2008),[4] and as a visiting scholar at Alibaba in 2017.[7]
Awards and honors
[edit]- ACM Fellow (2015), for contributions to the theory and practice of machine learning and data mining[8]
- TECO Award (2015) in Electrical Engineering, Information and Communication Technology[1]
- AAAI Fellow (2014), for contributions to machine learning and the development of widely used SVM software; he was the first researcher from Taiwan to be elected an AAAI Fellow[9][4][10]
- Best Paper Award (2013), ACM Conference on Recommender Systems[11]
- IEEE Fellow (2011), for contributions to support vector machine algorithms and software[4]
- Best Paper Award (2010), ACM SIGKDD Conference[4]
Selected works
[edit]Software
[edit]- LIBSVM implements the sequential minimal optimization algorithm for kernelized support vector machines. LIBSVM Homepage
- LIBLINEAR, a library for large-scale linear classification. LIBLINEAR Homepage
Articles
[edit]- Chang, Chih-Chung; Lin, Chih-Jen (2011). "LIBSVM: A library for support vector machines". ACM Transactions on Intelligent Systems and Technology. 2 (3). doi:10.1145/1961189.1961199. S2CID 961425.
References
[edit]- ^ a b "第22屆東元獎科技類得獎人:林智仁" [22nd TECO Award, Science and Technology category: Chih-Jen Lin] (PDF) (in Chinese). TECO Technology Foundation. Archived from the original (PDF) on 2022-10-09. Retrieved 2026-06-29.
- ^ Chang, Chih-Chung; Lin, Chih-Jen (2011). "LIBSVM: A library for support vector machines". ACM Transactions on Intelligent Systems and Technology. 2 (3). doi:10.1145/1961189.1961199. S2CID 961425.
- ^ Fan, Rong-En; Chang, Kai-Wei; Hsieh, Cho-Jui; Wang, Xiang-Rui; Lin, Chih-Jen (2008). "LIBLINEAR: A Library for Large Linear Classification". Journal of Machine Learning Research. 9: 1871–1874.
- ^ a b c d e Bonissone, Piero P. (May 2011). "IEEE Fellows—Class of 2011". IEEE Computational Intelligence Magazine. Vol. 6, no. 2. IEEE. pp. 9–10. doi:10.1109/MCI.2011.940610. Retrieved 2026-06-29.
- ^ Lin, Chih-Jen (1998). Studies in large-scale optimization (PhD). University of Michigan. ProQuest 304433761.
- ^ "開發人人必用AI工具,還送「售後服務」 林智仁:影響力就是世界因你而不同" [Building AI tools everyone uses, with after-sales service too — Chih-Jen Lin: influence is making the world different because of you]. 天下雜誌 [CommonWealth Magazine] (in Chinese). Retrieved 2026-06-29.
- ^ 吳昇 (2017-12-04). "台大資工的 AI 優秀學者統整!台灣的 AI 發展有他們才有無限潛力" [A roundup of NTU CSIE's outstanding AI scholars: Taiwan's AI has unlimited potential because of them]. TechOrange (in Chinese). Retrieved 2026-06-29.
- ^ "Chih-Jen Lin – ACM Fellows 2015". Association for Computing Machinery. Retrieved 2026-06-29.
- ^ AAAI Fellows Elected in 2014, Chih-Jen Lin, National Taiwan University
- ^ "台大人工智慧幫,全球搶挖角". TechNews (in Chinese). Retrieved 2026-06-29.
- ^ "RecSys Best Paper Awards". ACM Conference on Recommender Systems. Retrieved 2026-06-29.