Draft:Irwin King

King completed a Bachelor of Science in Engineering and Applied Science at the California Institute of Technology (Caltech) in 1984. He pursued graduate studies at the University of Southern California (USC), earning a Master of Science (1987) and a Doctor of Philosophy (1993) in computer science. His doctoral research focused on simulating biological neural networks for motion detection, working with Michael A. Arbib, Christoph von der Malsburg, and Irving Biederman, at the University of Southern California (USC).

King joined CUHK in 1993, rising from assistant professor to chair of the Department of Computer Science and Engineering. From 2010 to 2012, he worked at AT&T Labs Research on big data applications and later taught at the University of California, Berkeley as a visiting professor. He was also a Member of the Technical Staff at the AT&T Labs Research from 2010 to 2012 working on Big Data and data science-related projects{{Cite web |title=Irwin King Inventions, Patents and Patent Applications - Justia Patents Search |url=https://patents.justia.com/inventor/irwin-king |access-date=2025-04-16 |website=patents.justia.com}}.

He served as the Associate Dean (Education) of the Engineering Faculty at CUHK from August 2013 to July 2019. He also served as the Chair of the Department of Computer Science from August 2000 to July 2023. He founded CUHK’s Machine Intelligence and Social Computing Lab in 2006 and became the inaugural director of the ELearning Innovation and Technology (ELITE) Centre in 2017, promoting digital education tools.

Notable projects under his leadership include:

• [https://veriguide1.cse.cuhk.edu.hk/portal/page/index.jsp VeriGuide] (2005–present): A plagiarism-detection system to uphold academic integrity.
• [https://keep.edu.hk/ KEEP] (2014–present): A platform supporting MOOCs across Greater China.

1. ACM Fellow (2024){{Cite web |title=2024 ACM Fellows Celebrated for transformative contributions to computing science and technology. |url=https://www.acm.org/media-center/2025/january/fellows-2024 |access-date=2025-04-15 |website=www.acm.org |language=en}}
2. IEEE Fellow (2019){{Cite web |title=IEEE Fellows Directory - Member Profile |url=https://services27.ieee.org/fellowsdirectory/getdetailprofile.html?custNum=M4TAgAKy8q9lQfv2rb8W5A |access-date=2025-04-15 |website=services27.ieee.org}}
3. INNS Fellow (2021){{Cite web |title=Fellows and Senior Members |url=https://www.inns.org/fellows-senior-members |access-date=2025-04-15 |website=www.inns.org}}
4. AAIA Fellow (2022){{Cite web |title=Asia-Pacific Artificial Intelligence Association |url=https://www.aaia-ai.org/fellows?words=irwin%20king |access-date=2025-04-15 |website=www.aaia-ai.org}}
5. HKIE Fellow{{Cite web |title=Standing Committees {{!}} HKIE |url=https://www.hkie.org.hk/en/discover/committee/ |access-date=2025-04-15 |website=www.hkie.org.hk}}
6. Lee Woo Sing College Fellow
7. Apple Distinguished Educator
8. World’s Top 2% Scientists by Stanford University (September 2022){{Cite journal |last=Kumar |first=Rajendra |date=2022 |title=World's top 2% Scientist 2022 |url=https://rgdoi.net/10.13140/RG.2.2.30055.11687 |language=en |doi=10.13140/RG.2.2.30055.11687}}
9. ACM WSDM Test of Time Award (2022){{Cite web |date=2022-01-27 |title=WSDM Test of Time Award |url=https://www.wsdm-conference.org/2022/timetable/event/wsdm-awards-program-test-of-time-presentation/ |access-date=2025-04-15 |website=WSDM'22 |language=en-US}}
10. ACM SIGIR Test of Time Award (2020){{Cite web |title=SIGIR 2020 |url=https://sigir.org/sigir2020/awards/ |access-date=2025-04-15 |website=sigir.org |language=en}}
11. ACM CIKM Test of Time Award (2019){{Cite web |title=CIKM Test of Time Award |url=http://www.cikmconference.org/cikmToTA.html |access-date=2025-04-15 |website=www.cikmconference.org}}
12. 2021 INNS Dennis Gabor Award for work in Neural Engineering for Social Computing{{Cite web |title=Prof. Irwin King Awarded the 2021 INNS Dennis Gabor Award – CUHK CSE |url=https://www.cse.cuhk.edu.hk/news/achievements/irwin-king-2021-inns-dennis-gabor-award/ |archive-url=http://web.archive.org/web/20240617063215/https://www.cse.cuhk.edu.hk/news/achievements/irwin-king-2021-inns-dennis-gabor-award/ |archive-date=2024-06-17 |access-date=2025-04-15 |website=www.cse.cuhk.edu.hk |language=en-US}}
13. 2020 APNNS Outstanding Achievement Award {{Cite web |title=Awards |url=https://www.apnns.org/awards/ |access-date=2025-04-15 |website=APNNS |language=en-US}}

1. Vice-President, ACM SIGWEB (2023–present){{Cite web |title=SIGWEB - Executive Committee |url=https://www.sigweb.org/about-sigweb/executive-committee |access-date=2025-04-15 |website=www.sigweb.org}}
2. Vice-President of Conference, WebConf Steering Committee (2022–present)
3. Former President of INNS (2019–2020)
4. Executive Committee member, Hong Kong High Performance Computing Association (HKHPC) (2024–present){{Cite web |title=Executive Committee – hkhpc.org |url=https://www.hk-hpc.org/?page_id=1030 |access-date=2025-04-15 |language=en-US}}
5. General Co-Chair, WWW2020{{Cite web |title=The Web Conference 2020 |url=https://archives.iw3c2.org/www2020/committees/ |access-date=2025-04-15 |website=The Web Conference |language=en-US}}
6. General Co-Chair, RecSys 2013{{Cite web |title=RecSys – ACM Recommender Systems |url=https://recsys.acm.org/recsys13/committees/ |access-date=2025-04-15 |website=RecSys |language=en}}
7. General Co-Chair, ASONAM 2012{{Cite web |title=ASONAM 2012 Home page |url=https://asonam.cpsc.ucalgary.ca/2012/Stokman.html |access-date=2025-04-15 |website=asonam.cpsc.ucalgary.ca}}

1. Yifei Zhang, Hao Zhu, Menglin Yang, Jiahong Liu, Rex Ying, Irwin King, Piotr Koniusz: Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning. KDD (1) 2025: 1984-1995{{Cite book |last1=Zhang |first1=Yifei |last2=Zhu |first2=Hao |last3=Yang |first3=Menglin |last4=Liu |first4=Jiahong |last5=Ying |first5=Rex |last6=King |first6=Irwin |last7=Koniusz |first7=Piotr |chapter=Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning |date=2025-07-20 |title=Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 |chapter-url=https://dl.acm.org/doi/10.1145/3690624.3709249 |series=KDD '25 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=1984–1995 |doi=10.1145/3690624.3709249 |arxiv=2310.18209 |isbn=979-8-4007-1245-6}}
2. Aiwei Liu, Leyi Pan, Xuming Hu, Shuang Li, Lijie Wen, Irwin King, Philip S. Yu: An Unforgeable Publicly Verifiable Watermark for Large Language Models. ICLR 2024{{Cite journal |last1=Liu |first1=Aiwei |last2=Pan |first2=Leyi |last3=Hu |first3=Xuming |last4=Li |first4=Shuang |last5=Wen |first5=Lijie |last6=King |first6=Irwin |last7=Yu |first7=Philip S. |date=2023-10-13 |title=An Unforgeable Publicly Verifiable Watermark for Large Language Models |arxiv=2307.16230 |url=https://openreview.net/forum?id=gMLQwKDY3N |language=en}}
3. Conghao Xiong, Hao Chen, Hao Zheng, Dong Wei, Yefeng Zheng, Joseph J. Y. Sung, Irwin King: MoME: Mixture of Multimodal Experts for Cancer Survival Prediction. MICCAI (4) 2024: 318-328{{Cite book |last1=Xiong |first1=Conghao |last2=Chen |first2=Hao |last3=Zheng |first3=Hao |last4=Wei |first4=Dong |last5=Zheng |first5=Yefeng |last6=Sung |first6=Joseph J. Y. |last7=King |first7=Irwin |chapter=MoME: Mixture of Multimodal Experts for Cancer Survival Prediction |series=Lecture Notes in Computer Science |date=2024 |volume=15004 |editor-last=Linguraru |editor-first=Marius George |editor2-last=Dou |editor2-first=Qi |editor3-last=Feragen |editor3-first=Aasa |editor4-last=Giannarou |editor4-first=Stamatia |editor5-last=Glocker |editor5-first=Ben |editor6-last=Lekadir |editor6-first=Karim |editor7-last=Schnabel |editor7-first=Julia A. |title=Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 |chapter-url=https://link.springer.com/chapter/10.1007/978-3-031-72083-3_30 |language=en |location=Cham |publisher=Springer Nature Switzerland |pages=318–328 |doi=10.1007/978-3-031-72083-3_30 |isbn=978-3-031-72083-3}}
4. Yueen Ma, Dafeng Chi, Jingjing Li, Kai Song, Yuzheng Zhuang, Irwin King: VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder. NAACL-HLT (Findings) 2024: 364-378{{Cite journal |last1=Ma |first1=Yueen |last2=Chi |first2=DaFeng |last3=Li |first3=Jingjing |last4=Song |first4=Kai |last5=Zhuang |first5=Yuzheng |last6=King |first6=Irwin |date=June 2024 |editor-last=Duh |editor-first=Kevin |editor2-last=Gomez |editor2-first=Helena |editor3-last=Bethard |editor3-first=Steven |title=VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder |url=https://aclanthology.org/2024.findings-naacl.26/ |journal=Findings of the Association for Computational Linguistics: NAACL 2024 |location=Mexico City, Mexico |publisher=Association for Computational Linguistics |pages=364–378 |doi=10.18653/v1/2024.findings-naacl.26|arxiv=2307.00852 }}
5. Zixing Song, Yifei Zhang, Irwin King: No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning. NeurIPS 2023{{Cite journal |last1=Song |first1=Zixing |last2=Zhang |first2=Yifei |last3=King |first3=Irwin |date=2023-12-15 |title=No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning |url=https://papers.nips.cc/paper_files/paper/2023/hash/944ecf65a46feb578a43abfd5cddd960-Abstract-Conference.html |journal=Advances in Neural Information Processing Systems |language=en |volume=36 |pages=47511–47526}}
6. Yifei Zhang, Dun Zeng, Jinglong Luo, Zenglin Xu, Irwin King: A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy. WWW (Companion Volume) 2023: 1167-1176{{Cite book |last1=Zhang |first1=Yifei |last2=Zeng |first2=Dun |last3=Luo |first3=Jinglong |last4=Xu |first4=Zenglin |last5=King |first5=Irwin |chapter=A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy |date=2023-04-30 |title=Companion Proceedings of the ACM Web Conference 2023 |chapter-url=https://dl.acm.org/doi/10.1145/3543873.3587681 |series=WWW '23 Companion |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=1167–1176 |doi=10.1145/3543873.3587681 |arxiv=2302.10637 |isbn=978-1-4503-9419-2}}
7. Yankai Chen, Yifei Zhang, Menglin Yang, Zixing Song, Chen Ma, Irwin King: WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering. SIGIR 2023: 2521-2525{{Cite book |last1=Chen |first1=Yankai |last2=Zhang |first2=Yifei |last3=Yang |first3=Menglin |last4=Song |first4=Zixing |last5=Ma |first5=Chen |last6=King |first6=Irwin |chapter=WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering |date=2023-07-18 |title=Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval |chapter-url=https://dl.acm.org/doi/10.1145/3539618.3592089 |series=SIGIR '23 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=2521–2525 |doi=10.1145/3539618.3592089 |arxiv=2305.04410 |isbn=978-1-4503-9408-6}}
8. Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu: Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 5026-5041 (2022){{Cite journal |last1=Liu |first1=Pengpeng |last2=Lyu |first2=Michael R. |last3=King |first3=Irwin |last4=Xu |first4=Jia |date=September 2022 |title=Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation |url=https://ieeexplore.ieee.org/document/9444870 |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=44 |issue=9 |pages=5026–5041 |doi=10.1109/TPAMI.2021.3085525 |pmid=34061735 |arxiv=2106.04195 |issn=1939-3539}}

1. [ICONIP2023 Best Student Paper Award Finalist] Xiangli Yang, Xinglin Pan, Irwin King, Zenglin Xu: Generalized Category Discovery with Clustering Assignment Consistency. ICONIP (5) 2023: 535-547{{Cite book |last1=Yang |first1=Xiangli |last2=Pan |first2=Xinglin |last3=King |first3=Irwin |last4=Xu |first4=Zenglin |chapter=Generalized Category Discovery with Clustering Assignment Consistency |series=Lecture Notes in Computer Science |date=2024 |volume=14451 |editor-last=Luo |editor-first=Biao |editor2-last=Cheng |editor2-first=Long |editor3-last=Wu |editor3-first=Zheng-Guang |editor4-last=Li |editor4-first=Hongyi |editor5-last=Li |editor5-first=Chaojie |title=Neural Information Processing |chapter-url=https://link.springer.com/chapter/10.1007/978-981-99-8073-4_41 |language=en |location=Singapore |publisher=Springer Nature |pages=535–547 |doi=10.1007/978-981-99-8073-4_41 |isbn=978-981-99-8073-4}}
2. [ICONIP2020 Best Student Paper Award] Yaoman Li, Irwin King: AutoGraph: Automated Graph Neural Network. ICONIP (2) 2020: 189-201{{Cite book |last1=Li |first1=Yaoman |last2=King |first2=Irwin |chapter=AutoGraph: Automated Graph Neural Network |series=Lecture Notes in Computer Science |date=2020 |volume=12533 |editor-last=Yang |editor-first=Haiqin |editor2-last=Pasupa |editor2-first=Kitsuchart |editor3-last=Leung |editor3-first=Andrew Chi-Sing |editor4-last=Kwok |editor4-first=James T. |editor5-last=Chan |editor5-first=Jonathan H. |editor6-last=King |editor6-first=Irwin |title=Neural Information Processing |chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-63833-7_16 |language=en |location=Cham |publisher=Springer International Publishing |pages=189–201 |doi=10.1007/978-3-030-63833-7_16 |isbn=978-3-030-63833-7}}
3. [CVPR2019 Best Paper Award Finalist] Pengpeng Liu, Michael R. Lyu, Irwin King, Jia Xu: SelFlow: Self-Supervised Learning of Optical Flow. CVPR 2019: 4571-4580{{Citation |last1=Liu |first1=Pengpeng |title=Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation |date=2021-06-08 |url=https://arxiv.org/abs/2106.04195 |access-date=2025-04-15 |arxiv=2106.04195 |last2=Lyu |first2=Michael R. |last3=King |first3=Irwin |last4=Xu |first4=Jia}}
4. [ICONIP2017, Best Student Paper Award Runner-up] Shenglin Zhao, Irwin King, Michael R. Lyu: Geo-Pairwise Ranking Matrix Factorization Model for Point-of-Interest Recommendation. ICONIP (5) 2017: 368-377{{Cite book |last1=Zhao |first1=Shenglin |last2=King |first2=Irwin |last3=Lyu |first3=Michael R. |chapter=Geo-Pairwise Ranking Matrix Factorization Model for Point-of-Interest Recommendation |series=Lecture Notes in Computer Science |date=2017 |volume=10638 |editor-last=Liu |editor-first=Derong |editor2-last=Xie |editor2-first=Shengli |editor3-last=Li |editor3-first=Yuanqing |editor4-last=Zhao |editor4-first=Dongbin |editor5-last=El-Alfy |editor5-first=El-Sayed M. |title=Neural Information Processing |chapter-url=https://link.springer.com/chapter/10.1007/978-3-319-70139-4_37 |language=en |location=Cham |publisher=Springer International Publishing |pages=368–377 |doi=10.1007/978-3-319-70139-4_37 |isbn=978-3-319-70139-4}}
5. [CIKM2016 Best Paper Award Runner-up] Tong Zhao, Irwin King: Constructing Reliable Gradient Exploration for Online Learning to Rank. CIKM 2016: 1643-1652{{Cite book |last1=Zhao |first1=Tong |last2=King |first2=Irwin |chapter=Constructing Reliable Gradient Exploration for Online Learning to Rank |date=2016-10-24 |title=Proceedings of the 25th ACM International on Conference on Information and Knowledge Management |chapter-url=https://dl.acm.org/doi/10.1145/2983323.2983774 |series=CIKM '16 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=1643–1652 |doi=10.1145/2983323.2983774 |isbn=978-1-4503-4073-1}}
6. [WSDM 2022 Test of Time Award] Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, Irwin King: Recommender systems with social regularization. WSDM 2011: 287-296{{Cite book |last1=Ma |first1=Hao |last2=Zhou |first2=Dengyong |last3=Liu |first3=Chao |last4=Lyu |first4=Michael R. |last5=King |first5=Irwin |chapter=Recommender systems with social regularization |date=2011-02-09 |title=Proceedings of the fourth ACM international conference on Web search and data mining |chapter-url=https://dl.acm.org/doi/10.1145/1935826.1935877 |series=WSDM '11 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=287–296 |doi=10.1145/1935826.1935877 |isbn=978-1-4503-0493-1}}
7. [SIGIR 2020 Test of Time Award] Hao Ma, Irwin King, Michael R. Lyu: Learning to recommend with social trust ensemble. SIGIR 2009: 203-210{{Cite book |last1=Ma |first1=Hao |last2=Lyu |first2=Michael R. |last3=King |first3=Irwin |chapter=Learning to recommend with trust and distrust relationships |date=2009-10-23 |title=Proceedings of the third ACM conference on Recommender systems |chapter-url=https://dl.acm.org/doi/10.1145/1639714.1639746 |series=RecSys '09 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=189–196 |doi=10.1145/1639714.1639746 |isbn=978-1-60558-435-5}}
8. [CIKM 2019 Test of Time Award] Hao Ma, Haixuan Yang, Michael R. Lyu, Irwin King: SoRec: social recommendation using probabilistic matrix factorization. CIKM 2008: 931-940{{Cite book |last1=Ma |first1=Hao |last2=Yang |first2=Haixuan |last3=Lyu |first3=Michael R. |last4=King |first4=Irwin |chapter=SoRec: Social recommendation using probabilistic matrix factorization |date=2008-10-26 |title=Proceedings of the 17th ACM conference on Information and knowledge management |chapter-url=https://dl.acm.org/doi/10.1145/1458082.1458205 |series=CIKM '08 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=931–940 |doi=10.1145/1458082.1458205 |isbn=978-1-59593-991-3}}

== References ==

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