The Mathematics Research Lab brings together a number of mathematicians and data scientists within the School of IT at Deakin University in order to consolidate and focus their research on the areas of interest to the School and the University. It aims to create a collaborative working environment where cross-area synergies, mentorship and peer support, mutual training and collegiality thrive. The primary focus of the Mathematics Lab is on Decision Sciences, Computational Mathematics and Optimisation as well as Applied Statistics and Mathematical Modelling, in both theoretical and practical aspects.
Vision and Research Capabilities
- The lab will be active in both fundamental and industry research. In fundamental research, we will focus on advancing the theoretical study and the development of highly efficient solution methodologies in Optimization, and in novel ways to integrate Optimization and AI. The lab will also conduct research in Decision making, Combinatorial Optimization, Nonsmooth Optimization, Graph theory, Statistics, Mathematical Modeling and other mathematical areas.
- In industry research, we will reinforce existing industry partnership and engage with new industry partners in developing solutions to highly complex industry optimization problems, e.g., scheduling, routing, resource allocation, planning, timetabling, statistical interference and prediction.
Key Research Areas
Our research focuses on aggregation operators in AI, fuzzy systems and image processing. Fuzzy systems employ the concept of partial truth to mimic human decision-making. We build computer systems that help people make decisions from fragmented evidence and explain the logic behind those decisions.
We also focus on processing images, videos and other data to help develop autonomous systems that rely on image sensors, such as robots, rovers and drones.
Data analytics is about discovering patterns and properties of big and small data and developing data-driven models. Our research focuses on signal processing, smart sensing, anomaly detection, and aspects of information security and privacy.
Algorithms and optimisation
Our research covers industrial models and solutions based on discrete and continuous numerical optimisation, global optimisation, constraint satisfaction, models on graphs and data analysis methods.
We also cover the development of powerful and efficient algorithms, including parallel programming and graphic processing units. We provide fundamental techniques at the core of many AI algorithms, including machine learning, clustering and regression.