Research Projects

Featured Research Projects

MiniZinc Development Project

Project Lead: Guido Tack

Mini-zinc

MiniZinc is a free and open-source constraint modeling language developed at Monash University in collaboration with Data61 Decision Sciences and the University of Melbourne.

MiniZinc can be used to model constraint satisfaction and optimization problems in a high-level, solver-independent way, taking advantage of a large library of pre-defined constraints. The model is then compiled into FlatZinc, a solver input language that is understood by a wide range of solvers.

This project continues to develop and extend the MiniZinc language to make it easier to use, more efficient, and applicable to new problem classes. Current research includes topics such as Stochastic Optimisation, Large Neighbourhood Search, as well as methods for debugging and profiling constraint models. MiniZinc is also at the core of many of our industry projects.

Learning from learning solvers

Project lead: Prof Maria Garcia de la Banda

Learning solvers tree

Combinatorial optimisation problems are solved by finding a combination of choices that satisfies a set of constraints and optimises an objective function. This kind of decision making problem occurs in every area of our lives and finding high quality solutions to them is crucial. Unfortunately, solving these problems is remarkably difficult due to the exponential number of possible choice combinations (the search space). In industry, this often results in poor solutions being found and used.

The goal of this project is to revolutionise the modelling of combinatorial optimisation problems thanks to a surprising new insight: that the powerful technology developed to solve (rather than model) these problems, can also be used to improve the model itself

Next-generation protein structural alignment using information theory.

Project lead: Dr Arun Konagurthu

Next-generation protein structural alignment

Progress in protein structural biology has radically transformed our understanding of cellular and biological processes, driving far-reaching research advances in life sciences and medicine. Importantly, these advances rely heavily on a small set of computational technologies, of which structural alignments plays an indispensable role. A structural alignment is the assignment of correspondences (i.e., equivalences) between the three-dimensional (3D) coordinates of the amino acids of 2 or more proteins. Among many diverse uses, structural alignments are crucial in unraveling the distant past of protein evolution, unreachable by any other means. Despite the importance of structural alignments, it has not been formulated { much less solved { in a consistent and reliable way.

There are many methods to produce structural alignments for a given set of proteins. However, several studies have shown that the alignments obtained by these methods differ substantially. The aim of this project is to address the deficiencies in the current structural alignment state of the art, by using information-theory to develop a framework to measure structural alignment quality; to use modern combinatorial optimisation technology to design new search algorithms capable of obtaining biologically-meaningful and optimal structural alignments; and to use visualisation technology to provide innovative ways to meaningfully explore, visually and quantitatively, the quality of competing alignments for a given set of structures.