Joerg Simon Wicker
Yun Sing Koh
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
Professional Teaching Fellow
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.
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
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 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.