Members

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

Group Leader - 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

Michael Witbrock

Michael Witbrock

Professor

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.

Thomas Lacombe

Thomas Lacombe

Honorary Academic

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.

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.

Jason Tam

Jason Tam

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.

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

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.

David Tse Jung Huang

David Tse Jung Huang

Honorary Academic

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.
Joshua Bensemann

Joshua Bensemann

Research Fellow

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.

Song Yang

Song Yang

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: Kaiqi Zhao

Xinglong (Luke) Chang

Xinglong (Luke) Chang

PhD Student

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

PhD Student

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

Qianqian Qi

PhD Student

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

Nooriyan Poonawala-Lohani

Nooriyan Poonawala-Lohani

PhD Student

Supervisors: Mehnaz Adnan, Pat Riddle, and Joerg Wicker

Jonathan Kim

Jonathan Kim

PhD Student

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

Annie Lu

Annie Lu

PhD Student

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

Ming-Bin (Bryan) Chen

Ming-Bin (Bryan) Chen

MSc Student

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.

Supervisors: Michael Witbrock

Katharina Dost

Katharina Dost

PhD Student

Since July 2019 I am a PhD student in the School of Computer Science. Before, I was employed as a Data Scientist in marketing where my work centered around predictive models and clustering techniques. I did my undergraduate studies in Mathematics and my Master’s degree in Scientific Computer Science at the University of Mainz, Germany. My main research interests are Selection Bias identification and mitigation especially as a pre-processing step for a broad range of supervised or unsupervised models.

Supervisors: Pat Riddle and Joerg Wicker

Olivier Graffeuille

Olivier Graffeuille

PhD Student

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

Zhenyun Deng

PhD Student

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.

Supervisors: Michael Witbrock and Pat Riddle