Associate Professor Yun Sing Koh

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Associate Professor


Yun Sing Koh is an Associate Professor at the School of Computer Science, The University of Auckland, New Zealand. Her research is in the area of machine learning. Within the broad research realm, she is currently focusing on three strands of research: data stream mining, lifelong and transfer learning, and pattern mining. She has published more than 100 research papers in this field at top venues.

She has been active in the research community including serving as the General Chair at the IEEE International Conference on Data Mining 2021, Workshop Chair at the ECML 2021, Program Co-Chair of the Australasian Data Mining Conference 2018 and as the Workshop Chair for the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining.

Research | Current

My current research interests include

  • Data Mining specifically Pattern Mining,
  • Data Stream Mining,
  • Machine Learning,
  • Information Retrieval.

Please get in touch for PhD, MSc, Honours projects.


Teaching | Current

COMPSCI361 Machine Learning

Postgraduate supervision

Current PhD Supervision / Co-Supervision

  • Callum Cory (2021)  Graph-Based Deep Learning Models for Brain Network Analysis (co-supervised with Dr Miao Qiao, AProf Kelly Ke, Dr Diana Benavides Prado)
  • Olivier Graffeuille (2020) Machine Learning for Extreme Event Detection (co-supervised with Dr Jörg Wicker, Dr Moritz Lehman)
  • Wernsen Wong (2020) Transfer Deep Learning for Data Stream (co-supervised with Prof Gill Dobbie)
  • Peter Devine (2020) What do users say? Using unsupervised machine learning to robustly analyse multi-platform user feedback (primary supervisor: Dr Kelly Blinco)
  • Aaron Keesing (2019) Emotion Recognition in Speech (co-supervised Prof Michael Witbrock, A/Prof Ian Watson)
  • Ocean Wu (2019) Continual Learning and Adaptation for Evolving Data Streams (co-supervised with Prof Gill Dobbie)
  • Ben Halstead (2019) Adapative Predictive System for Life-long Learning (co-supervised with Dr Pat Riddle)
  • Shuxiang Zhang (2018) Concept Drift Detection (co-supervised with Prof Gill Dobbie)



MBIE 2020 Catalyst Strategic NZ-Singapore Data Science Programme “Advanced Graph Analytics for Human Brain Connectivity” (Amount: Part of NZD 3 Million), 2020 -2023, Key Researcher

MBIE Data Science Programme “Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science” (Amount: Part of NZD 13 Million), 2020 -2027, Key Researcher

MBIE Endeavour Research Programme “Our Generation, our Voices, all our Futures”, 2020 - 2025, Key Individual

Office Naval Research Grant “Prediction in Evolving Data Streams using an Adaptive System” 2019 – 2020, PI

Marsden Fast-Start 2018 “An Adaptive Predictive System for Life-long Learning on Data Streams” 2019 – 2021, PI

Precision Driven Health *A deep learning platform for GP referral triage 2018 -2020, Named collaborator

Vice-Chancellor’s Strategic Development Fund “Building resilience at the weakest link: A user-aware interactive and intelligent security system” 2018-2019, PIs: G. Russello, P. Corballis, Y.S. Koh, D. Lottridge

AUT University Vice Chancellor Emerging Researcher Award (2009), Auckland University of Technology

Areas of expertise

Machine learning specifically in the area of unsupervised learning, data stream mining, anomaly detection, transfer learning.

Committees/Professional groups/Services

General Chair IEEE ICDM 2021

Workshop and Tutorial Chair  ECML/PKDD 2021

Steering Committee Member: AusDM

Senior PC Member: AAAI


Selected publications and creative works (Research Outputs)

As of 29 October 2020 there will be no automatic updating of 'selected publications and creative works' from Research Outputs. Please continue to keep your Research Outputs profile up to date.
  • Huggard, H., Koh, Y. S., Dobbie, G., & Zhang, E. (2020). Detecting Concept Drift in Medical Triage. SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 10.1145/3397271.3401228
    Other University of Auckland co-authors: Gill Dobbie
  • Zhang, S., Jung Huang, D. T., Dobbie, G., & Koh, Y. S. (2020). SLED: Semi-supervised locally-weighted ensemble detector. Proceedings - International Conference on Data Engineering. 10.1109/ICDE48307.2020.00183
    Other University of Auckland co-authors: Gill Dobbie
  • Zhuo, S., Sherlock, L., Dobbie, G., Koh, Y. S., Russello, G., & Lottridge, D. (2020). REAL-Time Smartphone Activity Classification Using Inertial Sensors-Recognition of Scrolling, Typing, and Watching Videos While Sitting or Walking. Sensors (Basel, Switzerland), 20 (3).10.3390/s20030655
    Other University of Auckland co-authors: Gill Dobbie, Giovanni Russello, Danielle Lottridge
  • Zhao, D., & Koh, Y. S. (2020). Feature drift detection in evolving data streams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10.1007/978-3-030-59051-2_23
  • Benavides-Prado, D., Koh, Y. S., & Riddle, P. (2020). Towards Knowledgeable Supervised Lifelong Learning Systems. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 68, 159-224.
    Other University of Auckland co-authors: Patricia Riddle
  • Wu, O., Koh, Y. S., Dobbie, G., & Lacombe, T. (2020). PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data Streams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10.1007/978-3-030-47436-2_2
    Other University of Auckland co-authors: Ocean Wu, Gill Dobbie, Thomas Lacombe
  • Anderson, R., Koh, Y. S., Dobbie, G., & Bifet, A. (2019). Recurring concept meta-learning for evolving data streams. EXPERT SYSTEMS WITH APPLICATIONS, 13810.1016/j.eswa.2019.112832
    Other University of Auckland co-authors: Gill Dobbie
  • Fournier-Viger, P., Zhang, Y., Lin, J. C.-W., Fujita, H., & Koh, Y. S. (2019). Mining local and peak high utility itemsets. INFORMATION SCIENCES, 481, 344-367. 10.1016/j.ins.2018.12.070


Contact details

Primary office location

Level 4, Room 479
New Zealand

Web links