Centre of Data Science and Artificial Intelligence projects

605—Machine Learning for New Zealand Fish Data Analysis

In New Zealand, 60% of every fish caught is processed to low-value ($2 NZ kg-1) fish oil and fish meal, usually for inclusion in animal feeds—a terrible waste of one of Earth’s limited resources. A research programme led by the NZ Institute for Plant and Food Research, in collaboration with Callaghan Innovation, Victoria University of Wellington, Otago and Deakin, is now seeking to change that. The research team is designing a ‘factory of the future’ inspired by ‘Industry 4.0’, which will process every kilogram of fish into high-value products. To do this we first need to be able to measure the composition of every (and any) mixture of marine biomass in minutes instead of days. This is an exciting opportunity to be part of a highly collaborative research programme spanning NZ and Australia.

An excellent student who has strong Python and COMP307/COMP309 background is required. The student/scholar will be located in the Kelburn Campus of VUW. The successful scholar/student will be working with Professors Bing Xue and Mengjie Zhang at CDSAI, Victoria University of Wellington as well as Dr Daniel Killeen from Plant and Food Research. The expected project outcome includes analysis of results, implementation of machine learning methods and a project report.

Supervisor

606—AI in Climate Change: Interpretable Ice Sheet Modeling using Machine Learning

Climate change is causing sea levels to rise significantly, which considerably influences the human economy and species' living. One of the direct mechanisms that contribute to the observed sea level rise is the melting of land ice. It is vital to gain insight into how the ice sheet changes in the future. Numerical models have been used to predict how fast ice sheet is melting. These models are based on complex physical numerical models with numerous complex differential equations. It is computationally expensive and time-consuming. This project will investigate interpretable machine learning models that can predict ice sheet change effectively and efficiently.

An excellent student who have strong Python and COMP 307/COMP 309 background is required. The student/scholar will be located in the Kelburn Campus of VUW. The successful scholar/student will be working with Professors Bing Xue, Mengjie Zhang, and Bach Nguyen from Centre of Data Science and Artificial Intelligence and Professsor Nick Golledge from Antarctic Research Centre, Victoria University of Wellington.

Supervisor

608—Machine Learning and Computer Vision for Detecting Tree Branches

This project seeks an excellent summer scholar from current year-3 and year-4 students in Artificial Intelligence, Computer Science, and Software Engineering with AI specialisation to carry out research in AI and machine learning and computer vision for automatically detecting the unwanted branches of big trees for the drones to take and cut them so that the trees can grow quickly in an expected way. This will need to adopt and develop new computer vision techniques using neural networks and genetic programming for quickly and accurately detect the intersections of the tree branches and the trunk of the tree. Current research has successfully detected those main stems/branches for the pine trees under good weather and a smooth environment. This project will explore and develop new AIML techniques for improving performance and finding good solutions for other tree species in noisy environments.

A good background in Python programming and COMP307/COMP309 is required. A good background in statistics/optimisation would be preferred. The successful scholars/students will be working with Professors Bing Xue and Mengjie Zhang in the Centre for Data Science and AI. The student/scholar will be located at Victoria University of Wellington, and is expected to generate/submit a paper from their work.

Supervisor

609—Data Science and Artificial Intelligence for Aquaculture

This large project seeks 5 excellent summer scholars from current year-3 and year-4 students in Artificial Intelligence, Data Science, Computer Science and Software Engineering with AI specialisation, and (Applied) Statistics to carry out research in data science and AI for the New Zealand aquaculture industry. The successful scholars/students will use and develop different kinds of DSAI, machine learning and computer vision algorithms and techniques to support decision-making in the New Zealand aquaculture industry for responding to climate challenges, managing fish disease, improving fish production yields and fish farming sustainably at scale. These include intelligent/statistical modelling, deep learning and transfer learning, image processing and analysis, evolutionary and multi-objective decision-making, and interpretable AI and visualisation. We expect this to enable the aquaculture industry to efficiently produce high-quality, low-carbon protein for New Zealand without compromising the environment.

A good background in Python programming and COMP307/COMP309 is required. A good background in statistics/optimisation would be preferred. The successful scholars/students will be working with one of the experienced supervisors in the Centre for Data Science and AI: Mengjie Zhang, Ivy Liu, Bing Xue, Richard Arnold, Yi Mei, Bin Nguyen and Harith Al-Sahaf. The students/scholars will be located at Victoria University of Wellington, and are expected to generate/submit a paper from their work.

Supervisor