Frontiers in inference about risk. The project aims to develop new methods for robust risk evaluation and minimisation under various constraints and scenarios. Risk evaluation, estimation and prediction using past data is a central activity in diverse areas such as finance, insurance, superannuation and environmental regulation. The project aims to propose and solve innovatively robust risk optimisation problems under constraints, taking into account the time dynamics. Applications include risk ....Frontiers in inference about risk. The project aims to develop new methods for robust risk evaluation and minimisation under various constraints and scenarios. Risk evaluation, estimation and prediction using past data is a central activity in diverse areas such as finance, insurance, superannuation and environmental regulation. The project aims to propose and solve innovatively robust risk optimisation problems under constraints, taking into account the time dynamics. Applications include risk management around natural catastrophes and long-term asset investment of pension funds. The solutions and outcomes are expected to deliver optimal resource allocation proposals and better management of risk exposure in practice.Read moreRead less
Development of methods and algorithms to support multidisciplinary optimisation. This project will aim to develop a number of novel and computationally efficient schemes to deal with the key challenges facing multidisciplinary optimisation. These advancements will allow us to solve a number of challenging and intractable problems in science and engineering.
Decomposition and Duality: New Approaches to Integer and Stochastic Integer Programming. Because of their rich modelling capabilities, integer programs are widely used in industry for decision making and planning. However their solution algorithms do not have the maturity of their cousins in convex optimisation, where the theory of strong duality is ubiquitous. Efficient methods for convex optimisation under uncertainty do not apply to the integer case, which is highly non-convex. Furthermore, i ....Decomposition and Duality: New Approaches to Integer and Stochastic Integer Programming. Because of their rich modelling capabilities, integer programs are widely used in industry for decision making and planning. However their solution algorithms do not have the maturity of their cousins in convex optimisation, where the theory of strong duality is ubiquitous. Efficient methods for convex optimisation under uncertainty do not apply to the integer case, which is highly non-convex. Furthermore, integer models usually assume the data is known with certainty, which is often not the case in the real world. This project will develop new theory and algorithms to enhance the analysis of integer models, including those that incorporating uncertainty, while also enabling the use of parallel computing paradigms. Read moreRead less
Algorithms for hard graph problems based on auxiliary data. When solving computational problems, algorithms usually access only the data that is absolutely necessary to define the problem. However, much more data is often readily available. Especially for important or slowly evolving data, such as road networks, social graphs, company rankings, or molecules, more and more auxiliary data becomes available through computational processes, sensors, and simple user entries. This auxiliary data can g ....Algorithms for hard graph problems based on auxiliary data. When solving computational problems, algorithms usually access only the data that is absolutely necessary to define the problem. However, much more data is often readily available. Especially for important or slowly evolving data, such as road networks, social graphs, company rankings, or molecules, more and more auxiliary data becomes available through computational processes, sensors, and simple user entries. This auxiliary data can greatly speed up an algorithm and improve its accuracy. This project aims to design improved algorithms that harness auxiliary data to solve selected high-impact NP-hard graph problems, and will build a new empowering theory to discern when auxiliary data can be used to improve algorithms.Read moreRead less
Holistic Energy-Aware Scheduling for Distributed Computing Systems. Distributed computing systems are the platform of choice for many applications. In these systems, applications are submitted by a large number of users that compete for the shared heterogeneous resources (computers, storage communication links, etc). Concerns of power (or energy) consumption have become increasingly significant in the context of the design as well as the use of distributed computing systems. Therefore, there is ....Holistic Energy-Aware Scheduling for Distributed Computing Systems. Distributed computing systems are the platform of choice for many applications. In these systems, applications are submitted by a large number of users that compete for the shared heterogeneous resources (computers, storage communication links, etc). Concerns of power (or energy) consumption have become increasingly significant in the context of the design as well as the use of distributed computing systems. Therefore, there is a need to develop new generation of algorithms and software tools that enable the creation of environmentally friendly 'green' distributed systems. This project is a major step in this direction.Read moreRead less
Data and Job Scheduling in Large-Scale Distributed Systems. Distributed computing systems are the platform of choice for many applications. In these systems, applications are submitted by a large number of users that compete for the shared heterogeneous resources (computers, storage communication links, etc.). Thus, a distributed system can be viewed as a collection of computing and communication resources shared by active users. Towards this end, a new generation of algorithms and software tool ....Data and Job Scheduling in Large-Scale Distributed Systems. Distributed computing systems are the platform of choice for many applications. In these systems, applications are submitted by a large number of users that compete for the shared heterogeneous resources (computers, storage communication links, etc.). Thus, a distributed system can be viewed as a collection of computing and communication resources shared by active users. Towards this end, a new generation of algorithms and software tools need to be developed for the efficient utilisation of these systems through an appropriate allocation of the available resources to competing applications and users. This project is a major step in this direction.Read moreRead less
Replica Placement in Data-Intensive Distributed Computing Systems. Distributed computing systems are the platform of choice for many applications. In these systems, applications are submitted by a large number of users that compete for the shared heterogeneous resources (computers, storage communication links, etc). Thus, a distributed system can be viewed as a collection of computing, storage and communication resources shared by active users. Towards this end, a new generation of algorithms an ....Replica Placement in Data-Intensive Distributed Computing Systems. Distributed computing systems are the platform of choice for many applications. In these systems, applications are submitted by a large number of users that compete for the shared heterogeneous resources (computers, storage communication links, etc). Thus, a distributed system can be viewed as a collection of computing, storage and communication resources shared by active users. Towards this end, a new generation of algorithms and software tools need to be developed for the efficient utilisation of these systems through an appropriate allocation of data to competing applications and users. This project is a major step in this direction.Read moreRead less