An algorithm is a sequence of instructions, typically to solve a class of problems or perform a computation. They are unambiguous specifications for performing calculation, data analysis, automated reasoning, or other tasks.
We’re developing provably correct algorithms for fundamental combinatorial and geometric problems, including scheduling, trajectory and graph analysis.
These problems have applications in many fields, such as mathematics, biology, robotics, operations research and social networks.
Our focus research is to narrow the gap between theory and practice and develop practical algorithms with rigorous performance guarantees.
To achieve this aim we use a multitude of frameworks, such as:
Our expert: Dr André van Renssen
Routing is the process of exchanging data between nodes and the reason why networks are constructed. Our research aims to design efficient and practical routing algorithms for highly dynamic graphs. Nowadays devices can join and leave at any given time and must operate in the presence of complex obstacles that disrupt communication, such as electromagnetic fields in wireless networks.
We intend to develop innovative methods to deliver the outcome of provably efficient routing in obstacle-rich environments. This will enable faster data gathering in sensor networks used in geographical and geological operations, such as earthquake detection and prediction.
Our partners: Defence, Science and Technology Group, Australian Government
Technological advances in tracking and sensing are producing an increasing number of opportunities to trace the movement of vehicles, humans and animals. Current tracking technology allows for low cost, almost continuous capture of individual trajectories with sub-second sampling rates.
As a result, a variety of disciplines including ecology, animal-behaviour research, sports, defence, geographic information systems and transport are dependent on movement analysis. The goal of our research is to develop fundamental algorithms and basic data structures with quality guarantees.
Scheduling jobs on machines is a fundamental area of optimisation. Most of the classical work on it focused on the setting in which the scheduler has complete information about the jobs. However, in modern applications of job scheduling, such as cloud computing and content delivery, the jobs are not known to the scheduler in advance but arrive over time. This research aims to develop algorithmic techniques for scheduling jobs that arrive over time.
Our expert: Lijun Chang
A graph is ubiquitous in modelling real-word data that captures the relationship of information. With the rapid development of information technology, we are nowadays facing large graph data with billions of nodes and edges where the traditional algorithms and techniques are inefficient in processing these large graph data.
In this project, our aim is to design efficient algorithms and techniques to speed up and scale large graph analytics, such as finding dense subgraphs, computing shortest paths and identifying communities.