Welcome to

MARS Lab

WE ARE THE

Advanced Machine Learning and Data Analytics Research (MARS) Lab

MARS is a research group in the School of Computer Science at the University of Auckland. The lab is developing the next generation of Machine Learning theory, algorithms, and applications. The lab uses Machine learning in real-world applications to make them more sustainable, affordable and resilient. The lab was created to support and grow a community of researchers involved in developing state-of-the-art Machine Learning algorithmic, computational, with interdisciplinary components. The lab has produced various outstanding cutting-edge projects on real-world applicability, such as environment and health.

This group is co-directed by Prof Gillian Dobbie and Professor Yun Sing Koh. The research group consists of one research fellow, ten Ph.D. students, four Master’s students, multiple honours and Part IV software engineering students. With a focus on machine learning topics such as:

Generative Adversarial Learning
Continual learning
Data Stream Mining
Time Series Analysis
Transfer Learning		 Deep Learning
Extreme Learning and Anomaly Detection
Semi-supervised Learning
Self-supervised Learning
Domain Generalization

 

More than just ML Research

At MARs, we understand that machine learning is a powerful tool, but it’s just one piece of the puzzle. That’s why we prioritize the application of our research to tangible, real-world problems. We collaborate with experts across various domains such as enviromental and climate scientist from TAIAO and NIWA to identify challenges where machine learning can offer novel solutions. Our goal isn’t simply to advance algorithms, but to create technologies that positively impact industries, communities, and the lives of individuals.

All Projects