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Yun Sing Koh Profile page

• Professor School of Computer Science
• +6493737599 Ext.88299 (Work)
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• Advanced Machine Learning and Data Analytics Research (MARS) Lab
• Yun Sing's Website

Yun Sing Koh, a Professor at the School of Computer Science at the University of Auckland, New Zealand, is an expert in Artificial Intelligence (AI) and Machine Learning (ML). Her pioneering work delves into foundational AI research, focusing on continual learning and adaptation, domain generalization, transfer learning, anomaly detection, and data stream mining. Passionate about harnessing machine learning for societal benefit, Yun Sing's research has catalyzed interdisciplinary applications in environmental and health domains. She has published over 150 peer-reviewed publications in esteemed conferences and journals like IJCAI, IEEE ICDE, IEEE ICDM, Machine Learning Journal, and Journal of Artificial Intelligence. Yun Sing and her students' research excellence has been recognized with numerous awards, including Best Demo Paper for ACM MM 2023, Best Demo Paper for IEEE ICDE 2023, and Best Research Paper at AJCAI 2022 and DSAA 2022. She has various prestigious grants, including the New Zealand Royal Society Fast-Start Marsden funding, the United States Office of Naval Research Grant, MBIE Catalyst, and MBIE Endeavour Funds. Internationally, Yun Sing has held pivotal roles, including General Co-Chair at the IEEE International Conference on Data Mining in 2021 and the Australasian Data Mining Conference in 2023 and 2022. She has also served as Workshop Co-Chair at the ECML/PKDD conference in 2021, Program Co-Chair of the Australasian Data Mining Conference in 2018, and Program Co-Chair of the Australasian Joint AI Conference in 2024. Additionally, she has contributed as the Workshop Co-Chair for the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining. At the helm of the Advanced Machine Learning and Data Analytics Research (MARS) Lab and as the Director of the Centre of Machine Learning for Social Good, Yun Sing Koh continues to drive innovation, shaping the future of AI for the betterment of society. Explore and potential project at https://ml4sg.auckland.ac.nz/. Distinctions/Honours: Awards • Best Demo Paper award ACM MM (Core ranked: A*) 2023 (Main Author Aaron Keesing (PhD Student)) • Best Demo Paper award IEEE ICDE (Core ranked: A*) 2023 (Main Author Ben Halstead (Research Fellow)) • Best Research Paper award Australasian Joint AI Conference. 2022 (Main Author Bowen Chen (Masters Student)) • Best Research Paper award IEEE DSAA (Core ranked: A) 2022 (Main Author Ben Halstead (PhD Student)) • Finalist Women in AI Australia - NZ 2022 • Best Research Paper award Australasian Joint AI Conference. 2018 (Main Author Monica Bian (PhD Student)) Funding • MBIE 2020 Catalyst Strategic NZ-Singapore Data Science Programme “Advanced Graph Analytics for Human Brain Connectivity” 2020 -2023, Key Researcher • MBIE Data Science Programme “Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science” 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 Committee/Professional groups/Services. • General Chair IEEE ICDM 2021, AusDM 2022 • Workshop and Tutorial Chair ECML/PKDD 2021 • Steering Committee Member: AusDM • Senior PC Member: AAAI, ECML, IJCAI, PAKDD • PC Member: SIGKDD Qualifications: PhD (University of Otago); Masters of Software Engineering (University of Malaya); BSc (First class Hons) Computer Science (University of Malaya) Areas of Expertise: • Machine learning, specifically in: • unsupervised learning and weak supervision, • data stream mining and continual learning, • anomaly detection, and • transfer learning.

UNIVERSITY OF AUCKLAND APPOINTMENTS

• Professor Faculty of Science, School of Computer Science, New Zealand

FACULTY / INSTITUTE

• Faculty of Science

GRADUATE SUPERVISION

• PhD/Doctoral Accredited Supervisor
• Has Current Supervision Opportunity

MEDIA ENQUIRIES

• Available for media enquiries

FIELDS OF RESEARCH

• Machine learning
• Stream and sensor data
• Artificial intelligence
• Deep learning
• Data mining and knowledge discovery
• Data engineering and data science
• Machine learning not elsewhere classified

Artificial Intelligence

Why study Artificial Intelligence?

The growth of available data (“Big Data”) across companies, industry, government agencies and departments, iwi and community organisations has meant more and more of them are adopting AI into their mainstream activities. This has led to an increased demand for graduates with advanced skills in AI.

Studying AI at the University of Waikato will give students advanced skills and techniques to better leverage AI for the benefit of our communities.

The University of Waikato is well known worldwide for its computer science research and for development of  open source software  such as WEKA, MOA and ADAMS, with more than 10 million downloads. The University has recently  invested in New Zealand’s most powerful computer for AI , it is also the home of  Te Ipu o te Mahara, The Artificial Intelligence Institute .

Students have the opportunity to work alongside some of the world's leading AI researchers, with particular expertise in machine learning, deep learning, real-time analytics and open source software.

Career Opportunities

• Artificial intelligence architect
• Big data engineer
• Business analyst
• Data analyst/scientist
• Database programmer
• Financial advisor / analyst
• Machine learning engineer
• Mathematical Modeler
• Multimedia content creator
• Network Engineer
• Software Developer
• School of Computing and Mathematical Sciences

Study Artificial Intelligence in these qualifications

• Graduate Certificate
• Graduate Diploma
• Postgraduate Diploma
• Master of Science (Research)
• Master of Artificial Intelligence

Scholarships and prizes

Visit our Scholarship Finder for information about possible scholarships.

Subject regulations

Artificial Intelligence Subject regulations

Artificial Intelligence papers

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Joerg Simon Wicker

Director - Senior Lecturer

My main research area is machine learning and its application to bioinformatics, cheminformatics, and computational sustainability. Before joining the University of Auckland in 2017, I did a PostDoc at University of Mainz, Germany, and a PhD at Technical University of Munich, Germany. My current research focuses on diverse areas ranging from reliability of machine learning algorithms, particularly in respect to adversarial learning and bias to applications in epidemology, chemistry, environmental research, and cybersecurity.

Yun Sing Koh

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: data stream mining, lifelong and transfer learning, anomaly detection, and pattern mining. She has published more than 100 research papers in this field at top venues. She is passionate about pushing the boundaries of fundamental machine learning research and applying it to interdisciplinary areas such as environmental science and healthcare.

Michael Witbrock

Michael is building a research group, the Broad AI Lab, at the intersection of machine learning, reasoning and natural language understanding, with an additional focus on achieving the best social and civilizational impacts of increasingly powerful AI.

Senior Lecturer

Jiamou Liu is a Senior Lecturer at the School of Computer Science, The University of Auckland. Before joining the UoA, he was a Senior Lecturer at Auckland University of Technology between 2011 and 2015. Jiamou obtained a PhD in Computer Science from the University of Auckland, and worked as a research associate in University of Leipzig between 2009 and 2010 and Paris Diderot University (Paris 7) in 2013. He was a research intern at Microsoft Research Asia in 2008. During his PhD, he was a visiting student at Cornell University and National University of Singapore. Originally trained as a theoretical computer scientist, Jiamou’s current research is in Artificial Intelligence. In particular, his work revolves around the structural analysis of social networks, multiagent systems, as well as natural language processing. His recent research also connects to health-care, data privacy, computational social science, and spatial-temporal data analysis with application in traffic flow prediction.

Thomas Lacombe

Professional Teaching Fellow

My research interests are wide and various, centring on the application of machine learning and data mining to a wide range of fields such as data streams, image processing and computer vision. I have previously worked in industrial computer vision and machine learning applied to develop visual quality control for plastic injection production lines. More recently, I focused on automated hyper-parameter setting in a data stream environment.

I like to stay adaptable to broaden my expertise and I am especially motivated to work on interdisciplinary and applied projects.

Vithya Yogarajan

I am a Research Fellow at the University of Auckland. My primary research interest lies at the intersection of Artificial Intelligence, bias, and healthcare applications. I gained my PhD in domain-specific language models for multi-label classification of medical text, and am working towards improving healthcare outcomes of underrepresented and indigenous populations using AI. I worked for more than a decade in both academia and the health sector in New Zealand before returning to complete my PhD. My mother tongue is low-resourced, and I grew up in a society with unequal socialisation. Thus, I am particularly interested in mitigating social bias in data-driven models. I am motivated by the opportunities AI provides towards revitalising and preserving New Zealand’s endangered indigenous language, te reo Māori, and increasing the awareness and fostering of such linguistic minorities to improve health outcomes and equity in the health sector in the long term.

I have broad research interests in general topics of big data analytics, data mining and machine learning. Particularly, I am specialized in spatio-temporal data mining, text mining and recommender systems. In my recent research, I have worked on probabilistic generative models and deep generative models for mobility modeling and recommendations on spatio-temporal data such as GPS trajectories, location-based check-ins and geo-tagged tweets. I have also worked on efficient and effective topic mining algorithms for big spatio-temporal data.

Daniel Wilson

My academic training is in both philosophy and data science and I have research interests at the intersection of these domains, particularly with respect to professional ethics and socially responsible use of AI and ML. I also investigate privacy techniques employed in the public release of personal information, specifically with respect to confidentiality and usability. I am a member of Te Pokapū, the steering committee of Te Mana Raraunga – the Māori data sovereignty network

Katerina Taskova

My main research lies in the intersection of machine learning, meta-heuristic optimization, mathematical modeling, and data science with major applications in the filed of biology, ecology, engineering and social sciences.  My work is strongly motivated by real-life problems that can benefit from data-driven modeling and automated modeling approaches exploiting both domain-specific knowledge and different types of measured data as relevant for systems sciences.

Research Fellow

Alex was a commerce graduate who found his passion in artificial intelligence, especially in machine learning. His PhD thesis is on improving the generalisation of neural networks when labelled data is limited by utilising unlabelled data. Currently, his research focuses on NLP tasks and automated reasoning with neural networks. His other personal research interests include trying to understand the generalisation of neural networks, causal inference from data and symbolic regression.

Katharina Dost

Post Doctoral Fellow

I am a Post Doctoral Fellow in the School of Computer Science participating in projects on green and sustainable computing, freshwater modeling, and ethical computing. My main research interests revolve around the reliability of data and models, particularly with respect to biases, adversarial learning, and active learning. I enjoy solving real-life problems that matter, especially in chemistry or environmental applications.

Gill Dobbie

Gill has a wide range of research interests, including databases, the web, and software engineering. She is interested both in structured and semistructured data. More specifically, she is interested in how data can best be organized and managed, how the semantics of the data can be retained and expressed, and how querying can be carried out efficiently. Her main areas of interest pertain to databases and the web. She has worked in the foundations of database systems, defining logical models for various kinds of database systems, and reasoning about the correctness of algorithms in that setting. With colleagues at the National University of Singapore, she has defined a data model for semistructured data (called ORA-SS), providing a language independent description of the data. The group she was working with has used the ORA-SS data model to define a normal form for ORA-SS schema, defined valid views for semistructured databases, and described a storage structure for semistructured databases using object relational databases.

My main research interests are in the AI areas of machine learning and datamining. In particular, I am interested in various techniques for machine learning (such as ensemble approaches, techniques which overcome overfitting problems, and data-engineering as incorporating background knowledge) and their applications to real world problems. In addition I have been working in the area of search, planning, and representation increasingly in the last few years.

Joshua Bensemann

I am currently working in the field of artificial intelligence after completing a PhD in psychology. My research interests include incorporating concepts from psychology into deep learning as well as applying machine learning techniques to psychological data.

Diana Benavides Prado

Senior Research Fellow

I am currently a Senior Research Fellow at the Strong AI Lab, School of Computer Science, The University of Auckland. My research focuses in the areas of Transfer Learning, Lifelong/Continual Learning and Human-Algorithm Collaboration. My applied experience includes leading the design and deployment of Machine Learning tools for supporting decision making in a variety of application domains. I have mentoring and teaching experience in Data Science, Machine Learning, Algorithms and Programming. I hold a PhD in Computer Science from The University of Auckland, New Zealand.

Steffen Albrecht

As Research Fellow at the School of Computer Science I am involved in the SHIVERS project, investigating influenza and respiratory diseases in a post-COVID world. This project will be driven by algorithms from machine learning and data mining which is in line with my previous research activities. I received my PhD at the University of Mainz, Germany before I joined the Emergent AI Center in Mainz to work on neuroscience-related data and to explore novel machine learning strategies. My main research track is based on large-scale sequencing data from molecular biology and I am interested in working with interpretable models.

PhD Student

Song Yang, a researcher and practitioner, is keen to turn machine learning technologies into real-world value. His research interests lie in Deep Learning, Graph Neural Networks, Spatio-temporal Data Mining, Computer Vision, and AI in Healthcare. Song Yang started his PhD in December 2019. Before that, he worked as a full-time machine learning engineer with several years of actual work experience. The computer vision/deep learning models he used to work on have been installed in several hospitals and clinics in New Zealand to detect lesions/diseases from medical images automatically. Recently, he is interested in the emerging field of Graph Neural Networks and applying GNNs on graph structure data.

Supervisor: Jiamou Liu and Kaiqi Zhao

Xinglong (Luke) Chang

Xinglong (Luke) Chang is a PhD student at the School of Computer Science,  the University of Auckland, New Zealand. His supervisors are Dr Joerg Simon Wicker and Professor Gillian Dobbie. His research interests are adversarial learning and security issues related to machine learning.

Supervisors: Gill Dobbie and Joerg Wicker

Qiming (Bill) Bao

Qiming (Bill) Bao is a PhD student at the School of Computer Science, the University of Auckland, New Zealand. His supervisors are Professor Michael Witbrock and Dr Jiamou Liu. His research interests include soft reasoning related to deep learning.

Supervisors: Michael Witbrock and Jiamou Liu

Qianqian Qi

I received my M.Sc. degree specialised in Communications and Signal Processing from Imperial College London in 2014, and my B.Eng. degree majoring in Electronic and Information Engineering from Dalian University of Technology in 2013. After that, I worked as a software engineer in Singapore for more than four years. I am now researching the field of Natural Language Processing, mainly on natural language understanding and generation.

Supervisors: Michael Witbrock

Yonghua Zhu

Yonghua Zhu is a PhD student at the School of Computer Science, the University of Auckland, supervised by Michael Witbrock and Bakhadyr Khoussainov. Currently, his research focuses on Natural Language Processing (NLP) in particular with reading comprehension of scientific papers.

Supervisors: Michael Witbrock and Bakhadyr Khoussainov

Tim Hartill

After graduating from the University of Auckland with a B.A in Computer Science and Philosophy in the 1980s, Tim spent over 30 years in industry before returning in 2020 to complete a BSc Honours degree. He is currently engaged in research on the topic of training NLP models to generalise to answering questions they have not encountered before.

Supervisors: Michael Witbrock and Pat Riddle

I have been a PhD student at the School of Computer Science since 2022, working in the NLP direction of opinion mining and text summarisation. Prior to this, I completed my second Bachelor’s degree in Science (majoring in mathematics and computer science) and my Master’s degree in organisational psychology at the University of Auckland, and my first Bachelor’s at the University of Canterbury. My work experience in the education, management consulting, and marketing research industries centred around process / system redesign as well as quantitative and qualitative methods and analyses. My academic pursuit is driven by my passion in real-world problem-solving and innovations, especially in areas such as education, research, environment, and conservation. Outside my PhD topic, I have broad research interests in both theoretical and applied inter-disciplinary topics that involve applied mathematics, modelling, quantitative and qualitative analytics.

Supervisors: Katerina Taskova, Gill Dobbie, Joerg Wicker, and Paul Denny

Nooriyan Poonawala-Lohani

Influenza is a communicable respiratory illness that can cause serious public health hazards. Due to its huge threat to the community, accurate forecasting of Influenza-like-illness (ILI) can diminish the impact of an influenza season by enabling early public health interventions. Current forecasting models are limited in their performance, particularly when using a longer forecasting window. Commonly used methods to forecast ILI, including statistical methods such as ARIMA, limit prediction performance when using additional data sources that might have complex non-linear associations with ILI incidence. In my PhD, I use machine learning approaches to build models that can predict ILI cases more reliable further into the future.

Supervisors: Mehnaz Adnan, Pat Riddle, and Joerg Wicker

Jonathan Kim

I am currently pursuing a PhD from the Department of Computer Science, while working as a Senior Research Engineer at Callaghan Innovation. I have a Master of Engineering Management(Hons) and Bachelor of Computer Systems Engineering, both from University of Auckland. My research involves achieving robust semantic scene understanding through joint optimisation of SLAM and DCNNs.

Supervisors: Pat Riddle and Joerg Wicker

I’m a machine learning PhD student from the department of computer science, the University of Auckland after I completed my Master of professional studies in Data Science. My research is regard with applying machine learning techniques for Growing Up in New Zealand to help obtain insights from longitudinal data.

Supervisors: Yun Sing Koh, Susan Morton, and Joerg Wicker

Before jumping into the Machine Learning research area, Neset involved research and teaching in Mathematics for a long period of time. He has worked a variety of academic and non-academic jobs such as a research assistant, post-doc researcher, lecturer, software engineer, founder, and director of an engineering consultant company. His current interest is positioned at the intersection of natural language progressing and automation of data science. He has been researching various approaches such as deep learning and reinforcement learning techniques to obtain efficient ML systems for the mentioned areas. He has several publications in functional analysis, measure theory, optimization theory, and numerical analysis.

Ziyi Jiang completed his Honours degree at the University of Auckland with a direction in machine learning and data mining. Now, he is a PhD student at the School of Computer Science and focuses on the research topic of trajectory data mining with sub-trajectory patterns.

Supervisors: Kaiqi Zhao, Gill Dobbie

Olivier Graffeuille

I am working towards a PhD in Computer Science, after graduating from Engineering Science here at the University of Auckland. My current research is on using Machine Learning techniques to detect extreme climate events, namely using satellite data to detect cyanobacterial blooms in New Zealand lakes.

Supervisors: Moritz Lehmann, Yun Sing Koh, and Joerg Wicker

Zhenyun Deng

I am pursuing a PhD from the School of Computer Science at the University of Auckland. My research interests include Machine Learning and Natural Language Processing. My current research focuses on multi-step reasoning for complex question answering.

I’m a PhD student from the School of Computer Science at the University of Auckland. I have an M.E from the University of Western Australia and received both B.E and LL.B. from Southwest University, China. My current research focuses on applying Artificial Intelligence in Law.

Supervisors: Kaiqi Zhao and Robert Amor

Aaron Keesing

Aaron studied computer science and mathematics at undergraduate level before completing his BSc(Hons) dissertation on anomaly detection in wireless sensor networks. He then started his PhD research, currently supervised by Yun Sing Koh and Michael Witbrock. His research is focussed on improving the robustness of speech emotion recognition across diverse accents and languages. Aaron’s other interests more generally include affective computing, speech technology, artificial intelligence and mathematics.

Supervisors: Michael Witbrock and Yun Sing Koh

Stefan Fuchs

Stefan Fuchs is a PhD student at the University of Auckland, School of Computer Science. His research focuses on applying Natural Language Processing to automate the compliance checking process in the building industry. This application involves specialised domain terminology and knowledge, the structured and interlinked characteristics of legal texts, and the scarcity of labelled training data. His interest in generalisable machine learning models and learning from few examples can help to solve this domain application.

Supervisor: Michael Witbrock

Xiaobin Ren

I’m a machine learning Ph.D. student from the department of computer science, at the University of Auckland after I completed my Master of professional studies in Data Science. My research is to apply Graph Neural Networks and attention mechanisms to time series tasks.

Supervisors: Kaiqi Zhao, Patricia Riddle, Katerina Taskova

Ming-Bin (Bryan) Chen

MSc Student (2020-2021)

Coming from the multi-disciplines background of science and art, Ming-Bin’s primary research interests are in the intersection of computer science and linguistics, particularly AI in journalism. He is currently a research master student at the School of Computer Science of the University of Auckland, supervised by Professor Michael Witbrock. His current research project is developing an open domain interviewing agent, a dialogue bot capable of asking interviewing questions given a topic.

David Tse Jung Huang

Honorary Academic

Having started my academic training in Physics that eventually led to handling and analysing huge datasets, my main research interests naturally fall into how applications of advanced data science techniques, including ML and AI, can help transform workflows in scientific and technical disciplines for the future. Having previously applied modern data science tools and techniques in various aspects, from IoT and cloud technologies to custom analytics and visualizations, in disciplines such as Civil Engineering, Seismology and Environmental Science, currently I am mainly focused on improving predictions of chemical reactions in Environmental Chemistry.

Yang worked in the area of (inverse) reinforcement learning and computational game theory during his PhD study in the University of Auckland. He received his First Class Honours degree in computer science from the University of Auckland in 2018. He obtained his B.S. in computer science from Beijing Institute of Technology in 2017. During his PhD, he was a research intern at DAMO Academy (Alibaba) in 2020. He also got the Google Global PhD Fellowship Nomination in 2020. Currently his research focus is on applying reinforcement learning to automatic reasoning and proving.

Large-scale data science

I am keen to engage students in research topics that involve large-scale data science: i.e., data science that involves data processing and computation beyond the capabilities of existing commodity data science tools. A specific field of interest is distributed stream processing: where the data load is so high that filtering and analysis must be done on the data in motion, as it is impractical to store it for later querying.

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Postgraduate study in Artificial Intelligence

What can you study in artificial intelligence.

Artificial Intelligence (AI) is increasingly impacting society, and its effects are expected to grow as developments speed up.

Our new postgraduate Artificial Intelligence programmes have been designed to meet the needs of both developers and researchers who want to improve the capability of AI systems.

You’ll develop the essential knowledge, skills and experience to take your place on the exciting AI frontier, helping to shape and even revolutionise our world.

For the Master of Artificial Intelligence (MAI), the areas of study include:

• AI Fundamentals
• AI Architecture
• Deep Learning
• Natural Language Processing

Where can postgraduate study in Artificial Intelligence take you?

Many avenues open up to graduates with training in Artificial Intelligence. You will be able to fulfil the demands of the global job market with the essential knowledge, skills, and experience to undertake pioneering work and research in the dynamic area of Artificial Intelligence.

Jobs related to Artificial Intelligence

• Artificial intelligence (AI) engineer
• Big data engineer/architect
• Data engineer
• Machine learning engineer
• Natural language processing engineer
• Research scientist

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Postgraduate programmes

• Master of Artificial Intelligence (MAI)
• Postgraduate Diploma in Artificial Intelligence

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