Dr Hong Gunn Chew

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• Appointments

  Date	Position	Institution name
  2017	Associate Lecturer	University of Adelaide, Adelaide
  2013 - 2017	Teaching Laboratories Manager	The University of Adelaide
  2012 - 2013	Casual Associate Lecturer (Practical Coordinator)	University of Adelaide, Adelaide

• Awards and Achievements

  Date	Type	Title	Institution Name	Country	Amount
  2016	Teaching Award	ECMS Faculty Prize for Excellence in Professional Support of Learning & Teaching	University of Adelaide	Australia	$1000

• Language Competencies

  Language	Competency
  Chinese (Mandarin)	Can speak and understand spoken
  English	Can read, write, speak, understand spoken and peer review

• Education

  Date	Institution name	Country	Title
  2013	University of Adelaide, Adelaide	Australia	Ph.D
  1995	University of Tasmania, Hobart	Australia	B.E., B.Sc.

• Research Interests

  Autonomous Vehicles Computer Software Computer System Security Electrical and Electronic Engineering Knowledge Representation and Machine Learning

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• Journals

  Year	Citation
  2009	Chew, H., & Lim, C. (2009). On regularisation parameter transformation of support vector machines. Journal of Industrial and Management Optimization, 5(2), 403-415. DOI Scopus3 WoS3

• Book Chapters

  Year	Citation
  2018	Millar, K., Cheng, A., Chew, H., & Lim, C. (2018). Deep Learning for Classifying Malicious Network Traffic. In M. Ganji, L. Rashidi, B. Fung, & C. Wang (Eds.), Trends and Applications in Knowledge Discovery and Data Mining (Vol. 11154, Lecture Notes in Artificial Intelligence ed., pp. 156-161). Springer. DOI
  2005	Chew, H., Lim, C., & Bogner, R. (2005). An implementation of training dual-nu support vector machines. In L. Qi, K. Teo, & X. Yang (Eds.), Applied optimization - Optimization and control with applications (Vol. 96, pp. 157-182). New York, USA: Springer. DOI WoS7

• Conference Papers

  Year	Citation
  2018	Millar, K., Cheng, A., Chew, H., & Lim, C. (2018). Deep Learning for Classifying Malicious Network Traffic. In Trends and Applications in Knowledge Discovery and Data Mining Vol. 11154 (pp. 156-161). Melbourne, Australia. DOI
  2017	Millar, K., Smit, D., Page, C., Cheng, A., Chew, H., & Lim, C. (2017). Looking deeper: Using deep learning to identify internet communications traffic. In Macquarie Matrix: Special edition, ACUR 2017 Vol. 6.1 (pp. 1-21). Adelaide, Australia.
  2016	Young, B., Ertugrul, N., & Chew, H. (2016). Overview of optimal energy management for nanogrids (end-users with renewables and storage). In Proceedings of the 2016 Australasian Universities Power Engineering Conference, AUPEC 2016 (pp. 6 pages). Brisbane, AUSTRALIA: IEEE. DOI Scopus2 WoS1
  2004	Chew, H., Lim, C., & Bogner, R. (2004). Dual-nu support vector machines and applications in multi-class image recognition. In A. Rubinov, & M. Sniedovich (Eds.), Proceedings of the 6th International Conference on Optimization: Techniques and Applications 2004 (pp. CD-ROM 1-CD-ROM 11). CD-ROM: University of Ballarat.
  2001	Chew, H., Lim, C., & Bogner, R. (2001). On initialising nu-Support Vector Machine Training. In D. Li (Ed.), Proceedings of the 5th International Conference on Optimization: Techniques and Applications (pp. 1740-1747). HONG KONG: ICOTA.
  2001	Chew, H., Bogner, R., & Lim, C. (2001). Dual v-support vector machine with error rate and training size biasing. In V. John Matthews (Ed.), Proceedings of IEEE Signal Processing Society International Conference on Acoustics, Speech, and Signal Processing 2001 (pp. CDROM 1-CDROM 4). CD-ROM: IEEE SIGNAL PROCESSING SOCIETY. DOI
  2001	Chew, H., Bogner, R., & Lim, C. (2001). Dual ν-support vector machine with error rate and training size biasing. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings Vol. 2 (pp. 1269-1272). SALT LAKE CITY, UT: IEEE. Scopus62 WoS6
  2001	Chew, H., Bogner, R., & Lim, C. (2001). Dual nu-support vector machine with error rate and training size biasing. In 2001 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-VI, PROCEEDINGS (pp. 4041). SALT LAKE CITY, UT: IEEE.
  2000	Chew, H., Crisp, D., Bogner, R., & Lim, C. (2000). Target detection in radar imagery using support vector machines with training size biasing. In J. Wang (Ed.), Proceedings of ICARCV 2000 - Sixth International Conference on Control, Automation, Robotics and Vision (pp. CD). Singapore: School of Electrical & Electronic Engineering, NTU.

• Theses

  Year	Citation
  2013	Chew, H. G. (2013). Support Vector Machines with Dual Error Extensions for Target Detection and Object Recognition. (PhD Thesis, The University of Adelaide).

Practical coordinator of these courses:

Course coordinator of Final Year Projects for Undergraduates (Honours) and Postgraduates (Masters)

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• Current Higher Degree by Research Supervision (University of Adelaide)

  Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
  2018	Co-Supervisor	Classifying Network Connected Devices and Applications using Deep Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Kyle Alexander Millar

• Other Supervision Activities

  Date	Role	Research Topic	Location	Program	Supervision Type	Student Load	Student Name
  2018 - ongoing	Co-Supervisor	Classifying Network Connected Devices and Applications using Deep Learning	The University of Adelaide	—	Doctorate	Full Time	Kyle Millar

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• Committee Memberships

  Date	Role	Committee	Institution	Country
  2013 - ongoing	Member	School Advisory Committee	University of Adelaide	Australia

• Memberships

  Date	Role	Membership	Country
  1999 - ongoing	Member	Institute of Electrical and Electronic Engineers	United States

• Offices Held

  Date	Office Name	Institution	Country
  2002 - 2003	Treasurer	IEEE - Computer Society SA chapter	Australia

• Position: Associate Lecturer
• Phone: 83131641
• Email: honggunn.chew@adelaide.edu.au
• Campus: North Terrace
• Building: Ingkarni Wardli, floor 3
• Room: 3 52
• Org Unit: School of Electrical and Electronic Engineering