MSc student project

In recent years there has been an increasing trend towards releasing micro-data to the public. This can be very important for research, but in some cases (e.g. medical data) these releases are limited due to privacy protection issues. Anonymisation is a limited solution that does not fully protect the individuals. Even when all the personal identifiers have been removed it might be possible to identify an individual from an anonymous records using quasi-identifiers and data linking with some other external data source (see references).

This project will appeal to you if you are interested in Learning from Data and Nature-Inspired Computation.

This is a challenging project and will appeal to students keen to make a contribution in the areas of scientific databases and geoinformatics.

With the increasing resolution of MR and CT scans, it has become feasible to reconstruct detailed 3D images of faces.

Usually face de-identification in medical imaging is done after the reconstruction, i.e. in 3D (see references). Different techniques are used to this end including brain extraction, removal of facial features and deformation of the face surface.

he aim of the project is to perform some exploratory work on how to deal with the problem of I/O bound processing, by implementing technology-specific components in a provided system. The goal is to distribute data and processing so that a CPU processes data locally, minimising data transfer. The assumption is that I/O is the major bottleneck in processing, and computation could be done with less powerful (greener and cheaper) CPUs, rather than with a powerful CPU that wastes energy waiting for data. Different technologies for storing and processing the data can be explored.

We are building a data-intensive machine as a research platform to explore data-intensive computational strategies. We are interested in computations over large bodies of data, where the data-handling is a dominant issue. Computational challenges with these properties are getting ever more prevalent as the cost of digital sensors and computational/societal data sources become ever cheaper, ever more powerful and more ubiquitous. The use of algorithms over such data are of growing importance in medicine, planning, engineering, policy and science.

Present day instrumentation networks in rivers provide huge quantities of multi-dimensional data. Although there are numerous machine learning tools that can extract trends, find patterns and predict future states given some data, it is crucial to properly optimize these techniques according to the semantic content of the data. Hydrology is a data immense science, which requires efficient mining of trajectories of measurements taken at different time points and positions.

Principal goal: to substantially improve the performance of the data-intensive analysis for genome-wide association studies (GWAS) by using graphics processing units (GPUs).

Principal goal: to develop, test and make available to the cosmology community a parameter estimation method for models that explain our dark Universe.

Principal goal: to apply machines learning to identify small molecues that are likely candidates to have relevant bioactivity for follow-up wet-lab experiments.

Principle goal: To evaluate existing data streaming implementation, formulate model to predict streaming performance corresponding to buffering strategy and then optimise data streaming with dynamical buffering implementation.

Principle goal: to investigate existing data placement strategies and build a decision model to improve data placement strategies in enacting data-intensive workflow.

Primary objective: to perform data mining on a real-world data set from a biology lab in the School of Biological Sciences with the aim to extract patterns that lead to hypotheses about mode of action of compounds and function of genes.

Primary goal: to develop a classification algorithm to detect Web Spam.

Web Spam refers to a set of techniques that intend to increase the ranking of a page in a search engine. From search engine providers and Web users point of view, Web Spam decreases the quality of information search in the Web [1] [2] [3]. The Web Spam can be broadly classified into two types: content spam and link spam. It is a critical and challenging task to detect Web Spam. The success of Web Spam detection has a high commercial value for industries.

Principal goal: by a way of real case study in the Life Science, the goals of this project include: 1) Understanding data-parallel processing using MapReduce model for addressing Performance issues in data intensive applications; 2) Investigating how to adapt data mining algorihtms to the MapReduce model; 3) Prototyping and comparing performance with other frameworks that support data intensive applications.