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.
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
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
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
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
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 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
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 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
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 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.
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
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
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.
Visitors
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.
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
Currently doing my Master’s degree in Data Science. My dissertation focuses on the prediction of chemical interactions using various ML methods. As part of my research, I am investigating whether neural networks demonstrate superior performance to other ML algorithms. Research interests include natural language processing and time series forecasting.
Supervisors: Joerg Wicker & Katharina Dost
I’m studying for a Master of Data Science at the University of Auckland. My research aims to use adversarial learning to explore existing models’ applicability domains and create a methodology/criterion to estimate their future performance after encountering distributional shifts.
My current interests include adversarial attacks, biologically inspired spiking neural networks, and the conditions under which adding stochastic elements to deep networks can help improve robustness against adversarial attacks, and I am also interested in reinforcement learning, knowledge representations, machine understanding, and reasoning.
I am currently a fourth-year Bachelor of Advanced Science (Honor) student at the University of Auckland. My research interest is Fairness Machine Learning. More specifically, we mitigate bias in machine learning models in order to ensure machine learning models’ predictions will not be discriminatory in any way. My summer research project involves surveying the current state of bias evaluation frameworks and identifying future improvements. My current honor research project is concerned with utilising meta-learning to systematically detect bias in datasets, especially bias that we are not aware of.
Supervisors: Katharina Dost, Joerg Wicker & Jonathan Kim
Alumni
Katharina was participating in projects on green and sustainable computing, freshwater modeling, and ethical computing. Her main research interests revolve around the reliability of data and models, particularly with respect to biases, adversarial learning, and active learning. She enjoys solving real-life problems that matter, especially in chemistry or environmental applications.