Members

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.

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

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.

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.

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 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

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

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

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

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