Lab

Jörg Simon Wicker
Senior Lecturer

I am senior lecturer at the School of Computer Science of the University of Auckland, CTO of enviPath, and lead the Machine Learning Group at UoA. My main research area is machine learning and its application to bioinformatics, cheminformatics, and computational sustainability. Before joining the University of Auckland in 2017, I did a PostDoc at University of Mainz, Germany, and a PhD at Technical University of Munich, Germany. I am always interested in interesting new research areas both for applied and non-applied machine learning, currently, I am particularly interest in reliability of machine learning algorithms, adversarial machine learning, and bias, with applications in chemistry, epidemiology, and environmental research.

Katerina Taskova
Senior 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.

Postdocs

Katharina Dost
Postdoc

I am a Post Doctoral Fellow in the School of Computer Science participating in projects on green and sustainable computing, freshwater modeling, and ethical computing. My main research interests revolve around the reliability of data and models, particularly with respect to biases, adversarial learning, and active learning. I enjoy solving real-life problems that matter, especially in chemistry or environmental applications.

Steffen Albrecht
Research Fellow

As Research Fellow at the School of Computer Science I am involved in the SHIVERS project, investigating influenza and respiratory diseases in a post-COVID world. This project will be driven by algorithms from machine learning and data mining which is in line with my previous research activities. I received my PhD at the University of Mainz, Germany before I joined the Emergent AI Center in Mainz to work on neuroscience-related data and to explore novel machine learning strategies. My main research track is based on large-scale sequencing data from molecular biology and I am interested in working with interpretable models.

PhD Students

Cathy Hua
PhD student

I started my PhD journey at the School of Computer Science in 2022, working in the direction of opinion mining and text summarisation. My prior academic training included a second Bachelor’s degree in Science (mathematics and computer science) and a Master’s degree in organisational psychology from the University of Auckland. As a returned student, I have worked in the education, management consulting, and marketing research industries centred around process / system redesign as well as quantitative and qualitative methods and analyses. I am passionate about contributing to real-world problem-solving, especially in areas such as education, biodiversity, sustainability, and conservation. Outside my PhD topic, I have broad interests in both theoretical and applied inter-disciplinary topics that involve applied mathematics, modelling, quantitative and qualitative analytics.

Supervisors: Katerina Taskova, Gill Dobbie, Joerg Wicker, and Paul Denny

Mark Chen
PhD Student

I am a PhD student in Computer Science at the University of Auckland. My research focuses on adversarial learning on time series. The field of time series involves numerous vital applications such as stock market prediction, climate change investigation, energy consumption estimation, etc. Model robustness is always one of the biggest concerns in those crucial applications. In addition, adversarial learning could also provide insights into those models, which might potentially provide instructions on interventions required at the present in order to change the future. Supervisors: Gill Dobbie and Joerg Wicker

Nooriyan Poonawala-Lohani
PhD Student

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

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

Liam Brydon
PhD Student

My research is primarily in cheminformatics, investigating using machine learning to predict the outcomes of chemical reactions. This can help reduce the R&D time for novel chemicals that improve people’s lives, including drugs for disease treatment and fertilisers for reducing agricultural environmental impacts. In 2022 I completed my Bachelor of Advanced Science (Honours) at the University of Auckland, and in 2023 I am continuing my research as a PhD student.
I am also involved in a project as part of Predator Free NZ 2050 using machine learning to aid in reducing the predator population in New Zealand bush. I joined this project in November 2022 as a research assistant for Katerina Taskova, and I will be continuing this role part-time alongside my PhD.

Supervisors: Katerina Taskova, Gill Dobbie, Joerg Wicker

Ioannis Ziogas
PhD Student

I am currently pursuing my second PhD at the School of Computer Science, University of Auckland. My primary research interests revolve around survival ML models and their application on environmental and social science data, broadly construed. My educational background consists of a PhD in Political Science from the University of Florida and a PostDoc from Mississippi State University. Since 2018, I have also been a Visiting Assistant Professor at the University of Mississippi.

Supervised by Gill Dobbie and Joerg Wicker

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

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

Sandra Gómez-Gálvez
PhD Student

I am a PhD student at The University of Auckland. I have a Master´s Degree in Artificial Intelligence from the Polytechnic University of Madrid, Spain, and a Combined Honours Degree in Software Engineering and Mathematics from the Rey Juan Carlos University in Madrid, Spain.

My PhD research is part of a SfTI project with the mission to eradicate New Zealand predators and pests by 2050. My main goal is to use Artificial Intelligence to make predator (rats, possums, mustelids, etc.) identification using Artificial Vision, Neural Networks and other Artificial Intelligence systems. As I am in my first year, I am currently looking for where to contribute to the Computer Science state of the art and inside the project.

My supervisors are Katerina Taskova and Gill Dobbie.

Annie Lyu
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

Johnny Zhu
PhD Student

I am a PhD candidate in Computer Science at the University of Auckland, where my research focuses on time series analysis. Specifically, I am interested in studying trend turning, feature extraction, and motifs within time series data. In addition, I am working on incorporating adversarial learning techniques into time series forecasting models to extract insights for real-world applications, particularly in pandemic and weather forecasting.

My supervisors are Gill Dobbie and Joerg Wicker.

Visitors

Rui Zhang
Visiting PhD

I am a PhD candidate in Computer Science at the University of Electronic Science and Technology of China, where my research interest is adversarial machine learning, mainly focusing on adversarial defense. Specifically, I am interested in studying the adversarial robustness of models, the safety of models, and the purification of adversarial samples. In addition, I am working on integrating the synchronization characteristics of nature into the model, hoping that the neurons of the neural network can have more characteristics of nerve cells, so as to improve the robustness of the model.

Nicolas Samelson
Visitor / Research Assistant

I am final-year computer engineering student from ECAM Brussels Engineering School in Belgium. I’m passionate about applying machine learning techniques to tackle real-world environmental challenges. Through a research internship at the University of Auckland, I’m developing a model to detect and classify predators using thermal camera data, contributing to New Zealand’s Predator Free 2050 goal. My masters thesis is about developing a novel framework for freshwater management using machine learning to integrate process-based models, expert judgment, and uncertainty quantification.

Postgraduate Students

Charlie Chen
Master of Data Science Project – Artificial Intelligence and Freshwater Modelling
Nicholas Patel
Summer Project 2023/2024 – Adversarial Attacks on Time Series

Undergraduate Students

Sean Park
Summer Project 2023/2024 – Do Neural Networks Pay Off?
Joshua Tan
Summer Project 2023/2024 – Auditing Machine Learning Models using Adversarial Learning

I am currently a third-year Software Engineering student at the University of Auckland. My summer research project is focused on exploring adversarial attacks on machine learning models. The core aim of this project is to establish a framework for assessing the reliability of these models. By identifying and analyzing the adversarial regions within data spaces, we aim to pinpoint the vulnerabilities that could lead to model failures. Outside of my current research, my interests extend into the broader implications of AI, especially considering it as an existential risk. I am particularly interested in the capabilities and potential dangers posed by frontier AI models, especially as they get increasingly powerful.

Alumni

Chloe Haigh
Summer Project 2019/2020 – Privacy Defence & 380 Project – Biodegradation Half-Life Prediction
Bruno Naveen Joswa
Master of Data Science Project 2020 – Identififying and Analysing Bat Calls
Angela Hollings
Part 4 Project 2021 – Ear, nose and throat app development
Ryan La
Summer Project 2021/2022 – Auditing Machine Learning Models: Quantifying Reliability using Adversarial Regions
Aryan Lobie
380 Project 2019 – Weather Prediction using Deep Neural Networks
Marrick Lip
MSc 2022 – A Machine Learning Framework for the Analysis of Bat Calls
Ziqing Yan
Master of Data Science Project 2018 – A New Field of Data Mining: Classification of Movies based on VOCs
Xiao Li
Master of Data Science Project 2021 – Prediction of Earthquakes in the Ring of Fire
Tom Fevriér
380 Project 2019 – Identifing Markers for Human Emotion in Breath Using Convolutional Autoencoders on Movie Data
Hannah Zhang
Part 4 Engineering Project 2023 – A Dating Platform for Interrpersonal Relationship Research
Zac Pullar-Strecker
Honours 2022 – enviPath & Summer Project 2020/2021 – Adversarial Active Learning
Loukas Lyden
Master of Data Science Project 2019 – Modelling User Behaviour in Online Shopping
Elizabeth Yap
Part 4 Project 2021 – Ear, nose and throat app development
Johnathan Leung
380 Project 2021 – odel Response to Electroconvulsive Therapy Changes based on EEG Traces
Charles Tremlett
Master of Data Science Project 2019 – Generating Chemical Structures and Improving Models using Reinforcement Learning
Kitty Li
Honours 2019 – Mining the RDF Graph to Improve the Performance of Classifiers
Mary Grace De la Pena
Master of Data Science 2020 – Machine Learning-based Prediction of Biodegradation Persistence
Tsz Fung Ip
Master of Data Science Project 2022 – New Zealand Long-tailed Bat Audio Analysis with Machine Learning
Andrew Chester
MSc 2020 – Detecting Bias in Machine Learning Algorithms: End to End De-identification Framework for Clinical Text
Hasnain Cheena
Summer Project 2018/2019 – The Smell of Fear & 380 Project 2020 – Machine Learning Approaches for Mass Spectrometry Data Analysis
Viaan Saunderson
Honours 2022 – Adversarial Attacks on Graphs
Sarah Kim
Summer Project 2021/2022 – Identifying and analysing bat calls
Milan Law
Honours 2020 – Data Analysis of COVID-19 Data Sets
Yuye Zhang
Summer Prooject 2021/2022 – Auditing Machine Learning Models: Quantifying Reliability using Adversarial Regions
Matthew Mulvey
Summer Project 2019/2020 – Machine Learning in the Analysis of Mass Spectrometry Data
Catherine Liu
Master of Data Science Project 2019 – Dynamic Pricing
Rayner Rebello
Summer Project 2018/2019 – Advanced Methods for Boolean Matrix Decomposition
Lang Cheng
Part 4 Engineering Project 2023 – A Dating Platform for Interrpersonal Relationship Research
Hamish Duncanson
Honours 2021 – IMITATE: Identification and Mitigation of Selection Bias & Summer Project 2020/2021 – Image Compression with Multivariate Decision Trees
Sichun (Victor) Yin
380 Project 2018 – Advanced Boolean Matrix Decomposition
Yuanchi Ma
Master of Data Science Project 2021 – Inference of Cluster Information
Samantha Cen
Master of Data Science Project 2019 – Identifying Contrails in the CARIBIC Data Set
Josh Bensemann
Master of Data Science Project 2020 – Change Mining in the Smell of Fear Data Set
Maxwell Zhu
Honours 2022 – Machine Learning Matching Algorithms in Dating Platforms
Masoumeh Shariat
Master of Data Science Project 2019 – Analysis of Petrol related VOCs in the CARIBIC Data Set
Owen Meyer
Master of Data Science Project 2020 – Analysis of the CARIBIC Data Set
Chong Chuah
Honours 2022 – Bias in Machine Learning
Xianzhong Li
Master of Data Science Project 2021 – Inference of Cluster Information
Zhe Wu
Master of Data Science Project 2021 – Prediction of Earthquakes in the Ring of Fire
Sam Chen
Honours 2022 – Adversarial Attacks on Clustering Algorithms

Lab pictures