OUR People
Academic Staff
Yun Sing Koh
Bio: Yun Sing Koh is a Professor at the School of Computer Science, The University of Auckland, New Zealand. Her main research area is Artificial Intelligence (AI) and Machine Learning (ML). Specifically, focusing on several research strands: continual learning and adaptation, transfer learning anomaly detection, and data stream mining. Yun Sing is passionate about using machine learning for social good, and her research has been applied to interdisciplinary applications in environment and health domains. Yun Sing has published 100+ peer-reviewed publications in top conferences and journals, including IJCAI, IEEE ICDE, IEEE ICDM, Machine Learning Journal and Journal of Artificial Intelligence. She won the New Zealand Royal Society Fast-Start Marsden funding (2018) and the United States Office of Naval Research Grant (2019). Yun Sing has been active in the research community, including serving as the General Co-Chair at the IEEE International Conference on Data Mining 2021, Workshop Co-Chair at ECML/PKDD conference 2021, Program Co-Chair of the Australasian Data Mining Conference 2018 and as the Workshop Co-Chair for the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining.
Gillian Dobbie
Bio: At school, I excelled in Sciences and discovered my love of Computer Science. I went on to study Applied Engineering at undergraduate and postgraduate level, with a focus on computing and process control. After my undergraduate degree I spent a couple of years in industry working for PEC, programming in assembly language. My PhD research addressed theoretical aspects of database systems. I was one of the first women to complete a PhD in Computer Science at the University of Melbourne. My current research focuses on machine learning, in particular data stream mining and adversarial attacks. My research group creates algorithms that can be used in various application areas, such as predicting peaks and troughs in COVID-19 cases, predicting dementia using routinely collected data, monitoring critical and/or remote sensors, and detecting and defending against various adversarial attacks.
PhD Students
THOMAS BAILIE
Bio: –
Bowen Chen
Bio: Bowen is a Ph.D. candidate at the University of Auckland. His work focused on measuring the complexity and classification difficulty of instances within a dataset. He recently worked with the National Institute of Weather and Atmosphere (NIWA) to apply transfer learning methods to infer air pollution between different towns in NZ. Currently, his research focuses on applying self-supervised learning to climate modeling.
Supervisors: Yun Sing Koh, Gill Dobbie
Callum Cory
Bio: Callum is a Ph.D. student working on applying Graph-based Deep Learning models toward Neuroimaging data to help with the identification and understanding of degenerative brain diseases such as Parkinson’s and Alzheimer’s. Challenges in this area include how to effectively downsize a graph into gradually smaller representations, as well as how to combine information from multiple MRI variants, which cover both functional and structural information, into a single model.
Supervisors: Yun Sing Koh, Miao Qiao, Diana-Bernavidas-Prado, Kelly Ke
Cristian González
Bio: Cristian is a statistician with a Master in Statistics and a background in health data analysis. He is working on using Machine Learning Models and routinely collected data to predict dementia in Newzealander people. He is interested in classification and prediction analysis with different types of data (text data, longitudinal data, imagining data, etc.)
Supervisors: Gill Dobbie, Sarah Cullum, Claudia Rivera-Rodriguez
Di Zhao
Bio: Di worked in the area of transfer learning, domain generalization and computer vision during his Ph.D study at the University of Auckland. He received his First Class Honors degree and First Class Master degree in computer science from the University of Auckland in 2019 and 2020. Before that, he finished a BSc in computer science at the University of Waikato and worked as a software engineer for three years. Currently, his research focuses on applying curriculum learning to domain generalization tasks.
Supervisors: Yun Sing Koh, Gill Dobbie
YIWEN WANG
Bio: –
Jack Julian
Bio: My work is on continual learning with replay using confidence to inform the most effective time to replay during training.
Supervisors: Yun Sing Koh
YINUOXUE
Bio:
Olivier Graffeuille
Bio: My work focuses on developing novel Machine Learning algorithms to solve environmental problems with limited data. Currently, I’m focusing on estimating algal blooms from satellite imagery, focusing on the areas of Semi-Supervised Learning and Multi-Task Learning.
Supervisors: Yun Sing Koh, Joerg Wicker, Moritz Lehman
Ricky Qiao
Bio: I am a PhD student. I am currently working on longitudinal trajectory modeling using novel machine learning methods on the “Growing up in New Zealand” longitudinal study. I am also interested in and have worked on AI music and computer vision.
Supervisors: Yun Sing Koh, Caroline Walker
Wernsen Wong
Bio: The widespread growth of model capabilities and increased data collection has introduced new challenges for models to learn and update themselves sequentially and continuously during inference. Retraining entire models from scratch are inefficient, time-consuming, or impossible when new tasks are seen as previous data is no longer available. Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of non-i.i.d data tasks without catastrophically forgetting knowledge learned from the previous tasks. We investigate how to reduce catastrophic forgetting through a novel framework that dynamically balances an adaptive buffer and an adaptive network over time.
Supervisors: Yun Sing Koh, Gill Dobbie