Longbing Cao

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Longbing Cao (Chinese: 操龙兵; born in 1969[citation needed]) is an AI and data science researcher at the University of Technology Sydney, Australia. His broad research interest involves artificial intelligence,[1] data science,[2] behavior informatics,[3] and their enterprise applications.[4]

Biography[edit]

Cao received one PhD in Pattern Recognition and Intelligent Systems from Chinese Academy of Science and another PhD in Computing Science at the University of Technology Sydney (UTS).[5][6] He had a bachelor’s degree in electrical automation, and a master's degree in data communication. Cao took a Chief Technology Officer role managing business intelligence system design and implementation in China before he started his academic life in Australia in 2005.[6]

He established and directed the first Australian research centre dedicated to big data analytics: Advanced Analytics Institute at UTS in 2011, where he built the analytics degrees: Master of Analytics[7] and PhD Thesis: Analytics[8] in 2011 at UTS.

He is the inaugural Editor-in-Chief of International Journal of Data Science and Analytics (JDSA), publishing since 2016, and the Editor-in-Chief of IEEE Intelligent Systems, the oldest AI publication in IEEE. He founded the IEEE International Conference on Data Science and Advanced Analytics (DSAA) and established and chairs:

  • IEEE Task Force on Data Science and Advanced Analytics
  • IEEE Task Force on Behavioral, Economic and Socio-cultural Computing
  • ACM SIGKDD Australian and New Zealand Chapter (ANZKDD)

Cao published several books and over 300 papers since 2005.[9][10][11][12] Cao's research focuses include data science[1][2][3][4](data analytics, data mining, machine learning, information system), artificial intelligence and intelligent systems.[13] His specialized areas include behavior informatics and behavior computing,[14][15][16][17] domain-driven data mining and actionable knowledge discovery,[18][19][20] agent mining,[21][22][23] non-IID learning,[24][25][26][27] and AI in finance and FinTech.[28] He led a series of enterprise analytics/data science projects for major government and business in domains including social security, taxation, immigration, capital markets, insurance, banking, telecommunication, health, transport, services, and education.[1][29]

Cao won the Eureka Prize for Excellence in Data Science in 2019 awarded by the Australian Museum.[30]

References[edit]

  1. ^ a b c Cao, Longbing (2018). Data Science Thinking: The Next Scientific, Technological and Economic Revolution. Springer.
  2. ^ a b Cao, Longbing (29 June 2017). "Data Science: A Comprehensive Overview". ACM Computing Surveys. 50 (3): 43:1–43:42. arXiv:2007.03606. doi:10.1145/3076253. ISSN 0360-0300. S2CID 207595944.
  3. ^ a b Cao, Longbing (24 July 2017). "Data science: challenges and directions". Communications of the ACM. 60 (8): 59–68. arXiv:2006.16966. doi:10.1145/3015456. ISSN 0001-0782. S2CID 591875.
  4. ^ a b Cao, Longbing (September 2016). "Data Science: Nature and Pitfalls". IEEE Intelligent Systems. 31 (5): 66–75. arXiv:2006.16964. doi:10.1109/MIS.2016.86. ISSN 1941-1294. S2CID 38758949.
  5. ^ "Longbing Cao's homepage".
  6. ^ a b "Cao UTS staff profile".
  7. ^ "Master of Analytics at UTS".
  8. ^ "PhD Thesis: Analytics at UTS".
  9. ^ "Longbing Cao's ACM profile".
  10. ^ "Longbing Cao's IEEE profile".
  11. ^ "Longbing Cao's ORCID".
  12. ^ "Longbing Cao's DBLP".
  13. ^ Cao, Longbing (2015). Metasynthetic Computing and Engineering of Complex Systems. Advanced Information and Knowledge Processing. doi:10.1007/978-1-4471-6551-4. ISBN 978-1-4471-6550-7. ISSN 1610-3947. S2CID 9341675.
  14. ^ Cao, Longbing (1 September 2010). "In-depth behavior understanding and use: The behavior informatics approach". Information Sciences. Including Special Section on Virtual Agent and Organization Modeling: Theory and Applications. 180 (17): 3067–3085. arXiv:2007.15516. doi:10.1016/j.ins.2010.03.025. ISSN 0020-0255. S2CID 7400761.
  15. ^ Cao, Longbing; Yu, Philip S., eds. (2012). Behavior Computing. doi:10.1007/978-1-4471-2969-1. ISBN 978-1-4471-2968-4. S2CID 38353335.
  16. ^ Longbing Cao; Yu, Philip S.; Kumar, Vipin (1 November 2015). "Nonoccurring Behavior Analytics: A New Area". IEEE Intelligent Systems. 30 (6): 4–11. doi:10.1109/MIS.2015.105. ISSN 1541-1672. S2CID 552165.
  17. ^ "The Behavior Informatics website".
  18. ^ Cao, Longbing; Yu, Philip S.; Zhang, Chengqi; Zhao, Yanchang (2010). Domain Driven Data Mining. doi:10.1007/978-1-4419-5737-5. ISBN 978-1-4419-5736-8.
  19. ^ Cao, Longbing; Zhang, Chengqi (9 April 2006). "Domain-Driven Actionable Knowledge Discovery in the Real World". Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science. Vol. 3918. Berlin, Heidelberg: Springer-Verlag. pp. 821–830. doi:10.1007/11731139_96. ISBN 978-3-540-33206-0.
  20. ^ "The AKD/DDDM website".
  21. ^ Cao, Longbing, ed. (2009). Data Mining and Multi-agent Integration. Bibcode:2009dmma.book.....C. doi:10.1007/978-1-4419-0522-2. ISBN 978-1-4419-0521-5.
  22. ^ Cao, Longbing; Weiss, Gerhard; Yu, Philip S. (1 November 2012). "A brief introduction to agent mining". Autonomous Agents and Multi-Agent Systems. 25 (3): 419–424. doi:10.1007/s10458-011-9191-4. ISSN 1573-7454. S2CID 7825848.
  23. ^ "The Agent Mining website".
  24. ^ Cao, Longbing (1 September 2014). "Non-IIDness Learning in Behavioral and Social Data". The Computer Journal. 57 (9): 1358–1370. doi:10.1093/comjnl/bxt084. ISSN 0010-4620.
  25. ^ Cao, Longbing (1 June 2016). "Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting". Engineering. 2 (2): 212–224. arXiv:2007.07217. doi:10.1016/J.ENG.2016.02.013. ISSN 2095-8099. S2CID 56098214.
  26. ^ Cao, Longbing (1 March 2015). "Coupling learning of complex interactions". Information Processing & Management. 51 (2): 167–186. arXiv:2007.13534. doi:10.1016/j.ipm.2014.08.007. ISSN 0306-4573. S2CID 11192392.
  27. ^ "The Non-IID Learning webpage".
  28. ^ "AI in Finance and FinTech".
  29. ^ Cao, Longbing; Yu, Philip S.; Zhang, Chengqi; Zhang, Huaifeng, eds. (2009). Data Mining for Business Applications. doi:10.1007/978-0-387-79420-4. ISBN 978-0-387-79419-8. S2CID 67777168.
  30. ^ "2019 Australian Museum Eureka Prize winners". Australian Museum. Retrieved 29 August 2019.
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