About

The Machine Learning Group at the University of Auckland is the the biggest research group of the School of Computer Science. Our research covers diverse machine learning topics, carried out in a high number of theoretical and applied machine learning projects. We have strong experience in helping researchers use the full potential of their data using innovative machine learning techniques. Our research is published in the top machine learning conferences and journals.
Joerg Simon Wicker

Joerg Simon Wicker

Senior Lecturer

My main research area is machine learning and its application to bioinformatics, cheminformatics, computational sustainability, and privacy. My approach to research is to use interesting and challenging questions in other research areas and develop new machine learning methods that address them to potentially advance not only the field of machine learning, but also the area it is applied to. In my career, I worked on diverse machine learning topics including autoencoders, Boolean matrix decomposition, inductive databases, multi-label classification, privacy-preserving data mining, adversarial learning, and time series analysis.
Yun Sing Koh

Yun Sing Koh

Senior Lecturer

Dr Yun Sing Koh is a Senior Lecturer 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

David Tse Jung Huang

David Tse Jung Huang

Professional Teaching Fellow

Dr David Tse Jung Huang is an AWS Academy certified instructor working at the Auckland ICT Graduate School, the University of Auckland. David manages ICT short courses at the university and teaches into data science, machine learning, and cloud computing. He is an expert in applied machine learning and researches into change mining and analysis for big data and data streams. David is passionate about interactive teaching and keeping his students up-to-date with modern technology and theory. His most recent focus has been to upskill working professionals in machine learning and cloud computing.
Kaiqi Zhao

Kaiqi Zhao

Lecturer

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

Daniel Wilson

Professional Teaching Fellow

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

Katerina Taskova

Lecturer

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.

Gill Dobbie

Gill Dobbie

Professor

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.

Pat Riddle

Pat Riddle

Senior Lecturer

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.