You are here

PhD student project

A project suitable for obtaining a PhD, which takes three years.

A quantitative diagnostic method incorporating brain images and clinical measures

Student: 
Grade: 

This is a project in collaboration with the Brain Research Imaging Centre under the
Edinburgh Imaging Prize Studentships (Centre for In Vivo Imaging Science).
See http://www.edinburghimaging.com/studentships/advertising.html
and the form in http://www.edinburghimaging.com/documents/CIVIS%20PhDOct2013/application...

Description:

Project status: 
Still available
Degree level: 
PhD
Background: 
The project would suit a student with strong statistical skills and a background in Neuroinformatics, Neuroscience, or Psychology but also potentially a student from a pure Statistical, Mathematical, or Engineering background and an interest in brain ageing and pathology. It requires understanding of statistical analyses and summaries (e.g. hypotheses testing, means, and percentiles), clinical and brain image data, the sensitivities and management of these data; and the ability to work as part of an interdisciplinary group of researchers. There will be additional mentorship from Prof Joanna Wardlaw (expertise in neuroimaging).
Supervisors @ NeSC: 
Other supervisors: 
Dr Dominic Job Dr Susan Shenkin
Subject areas: 
References: 
1. Dickie, D.A., et al. (2012). Do brain image databanks support understanding of normal ageing brain structure? A systematic review. Eur Radiol. 22, 1385-1394. 2. Farrell, C., et al. (2009). Development and initial testing of normal reference MR images for the brain at ages 65–70 and 75–80 years. Eur Radiol. 19, 177–183. 3. Mazziotta, J.C., et al. (2009). The myth of the normal, average human brain - the ICBM experience: (1) Subject screening and eligibility. Neuroimage. 44, 914-922. 4. Freedman, D. (2010). Statistical Models and Causal Inference: A Dialogue with the Social Sciences. Cambridge University Press, Cambridge. 5. Breteler, M., et al. (1994). Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study. Neurology. 44,1246-1252.
Student project type: 

Distributed multi-modal image collection and analysis

Multimodal Image data banks, of normal [3] and pathological subjects, are of great utility for improving collaboration and performing research with greater statistical power. The acquisition of images is expensive and time consuming; therefore it is important to reuse them. We are currently developing human brain image data banks, one of which is likely to be the largest bank of normal aging brains in the world.

Project status: 
Still available
Degree level: 
PhD
Background: 
Computer Science, mathematical sciences or engineering with strong foundations in computing
Supervisors @ NeSC: 
Other supervisors: 
Dominic E. Job
Subject areas: 
Bioinformatics
References: 
1. Slomka P, Baum, R. Multimodality image registration with software: state-of-the-art. EJNMMI. 36 2. Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroimform. 5:13 3. Dickie, D.A., Job, D.E., Poole, I., Ahearn, T.S., Staff, R.T., Murray, A.D., Wardlaw, J.M., 2012. Do brain image databanks support understanding of normal ageing brain structure? A systematic review. Eur. Radiol. 22, 1385-1394.
Student project type: 

Early detection of infarcts by improving brain perfusion imaging analysis

Outcome after severe ischemic stroke may improve with thrombolysis. Some studies have shown that parametric perfusion maps and other information might be useful in selecting patients for this potentially hazardous treatment. Traditionally, perfusion source images are deconvolved in order to create these parametric maps; such as cerebral blood flow and volume [1].

Project status: 
Still available
Degree level: 
PhD
Background: 
Informatics, Neuroscience, statistics or Mathematics.
Supervisors @ NeSC: 
Other supervisors: 
Joanna.Wardlaw
Subject areas: 
Bioinformatics
References: 
1. L. Ostergaard, R. Weisskoff, D. Chesler, C. Gyldensted, and B. Rosen, “High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. part i: Mathematical approach and statistical analysis,” Magn Reson Med, vol. 36, no. 5, pp. 715–25, 1996. 2. F. Zhu, T. Carpenter, D. R. Gonzalez, M. Atkinson, and J. Wardlaw, “Computed tomography perfusion imaging denoising using Gaussian process regression,” Physics in Medicine and Biology, vol. 57, no. 12, pp. N183–198, 2012. 3. F. Zhu, D. R. Gonzalez, T. Carpenter, M. Atkinson, and J. Wardlaw, “Automatic Lesion Area Detection Using Source Image Correlation Coefficient for CT Perfusion Imaging”, IEEE Transactions on Information Technology in Biomedicine, Under Revision. 4. F. Zhu, D. Gonzalez, T. Carpenter, M. Atkinson, and J. Wardlaw, “Parallel per- fusion imaging processing using GPGPU,” Computer Methods and Programs in Biomedicine, 2012. 5. I. Kane, T. Carpenter, F. Chappell, C. Rivers, P. Armitage, P. Sandercock and J. Wardlaw, “Comparison of 10 different magnetic resonance perfusion imaging processing methods in acute ischemic stroke effect on lesion size, proportion of patients with diffusion/perfusion mismatch, clinical scores, and radiologic outcomes”, Am Heart Assoc, vol . 38, no. 12, pp 3158-64, 2007.
Student project type: 

Ad hoc Cloud Computing

Student: 
Gary McGilvary

Commercial cloud providers offer computational services via co-located machines within data centres, whereas private clouds typically offer services via a set of dedicated servers. While both cloud models appeal to the mass market, there exists a long tail of potential cloud users that are unable to take advantage of either public or private cloud computing.

Project status: 
In progress
Degree level: 
PhD
Supervisors @ NeSC: 
Other supervisors: 
Dr. Adam Barker Dr. Ashley Lloyd
Student project type: 

Optimising Distributed Data Integration and Data Mining Service through Transformation of Data Workflow into Parallel Stream

Student: 
Chee Sun Liew

Over the past decades, running large-scale experiments using computational tools has become popular in modern science. The data processing steps involved in such experiments are usually complex and compute intensive. A challenge arises when the demand comes from large collaboration projects that involve running computations across institutions and continents, where the data and machines are located on distributed sites. The common solution to make the experiments more manageable is executing the processing steps as a workflow, using domain-specific or generic workflow management systems.

Project status: 
Finished
Degree level: 
PhD
Supervisors @ NeSC: 
Student project type: 

Improving quality and reliability of results in gene expression studies by accounting for systematic artefacts

Student: 
Rob Kitchen

With the growing complexity and procurement costs of these high-throughput platforms, it is becoming increasingly common for the experiments to be deployed in central ‘core facilities’. This service-oriented paradigm is a recent development and one that is generally welcomed by lab-researchers and data-analysts as it encourages the standardisation of experimental protocols and reduces costs of hardware maintenance.

Project status: 
Finished
Degree level: 
PhD
Supervisors @ NeSC: 
Other supervisors: 
Prof Peter Clarke (School of Physics); Dr Varrie Ogilvie (Molecular Medicine Centre, University of Edinburgh)
Subject areas: 
e-Science
Bioinformatics
Student project type: 

Improving knowledge curation in structured wiki-like collaborative environments

Student: 
Luna De Ferrari

This work aims at defining, modelling and evaluating the integrated use of collaborative software and machine learning for building high quality knowledge resources. A possible scenario is Molecular Biology, where high-throughput data production is overwhelming the traditional centralised data annotation by paid experts. Many biological resources have moved to collaborative software platforms, predominantly wikis, in an effort to involve the wider community and replicate the success story of Wikipedia.

Project status: 
Finished
Degree level: 
PhD
Supervisors @ NeSC: 
Other supervisors: 
Igor Goryanin, School of Informatics; Stuart Aitken, AIAI
Student project type: 
References: 
[1] William A Baumgartner, K. Bretonnel Cohen, Lynne M Fox, George Acquaah-Mensah, and Lawrence Hunter. Manual curation is not suffcient for annotation of genomic databases. Bioinformatics, 23(13):i41–i48, Jul 2007.

Planning Emergency Movement for the Built Environment

Student: 
Thomas French

The goal of this project is to investigate methods for finding emergency movement plans in dynamic and uncertain environments, specifically buildings. Current techniques used to solve these problems, like (Opasanon, 2004), make unrealistic assumptions about human behaviour during emergency movement. For example, they assume that occupants travelling through a building do not directly interact, and, therefore, provide instructions that presume people who arrive at a decision point at the same time will split up if told to do so.

Project status: 
Finished
Degree level: 
PhD
Supervisors @ NeSC: 
Other supervisors: 
Austin Tate, AIAI; Stephen Potter, AIAI; Gerhard Wickler, AIAI; Jose Torero, School of Engineering
Subject areas: 
e-Science
Algorithm Design
Genetic Algorithms/Evolutionary Computing
Student project type: 
Projects: 
References: 
(Opasanon, 2004) S. Opasanon. On Finding Paths and Flows in Multicriteria, Stochastic and Time-Varying Networks. PhD thesis, University of Maryland, 2004. (SFPE, 2002) SFPE. SFPE Handbook for Fire Protection Engineering. National Fire Pro- tection Association, 3rd edition, January 2002.

Gaussian Process deconvolution for perfusion imaging: evaluation of the usage of distributed and parallel computing

Student: 
Fan Zhu

Final version of the thesis submitted.

Original project description:

Project status: 
Finished
Degree level: 
PhD
Background: 
MSc in Computer Science essential. Strong background in imaging and distributed computing important.
Other supervisors: 
Prof Joanna Wardlaw (SF Brain Imaging Research Centre, University of Edinburgh) Dr Trevor Carpenter (BRIC, University of Edinburgh)
Subject areas: 
Bioinformatics
Projects: 
References: 
1. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C and Rosen BR “High resolution measurement of cerebral blood flow using intravascular tracer bolus passages: I.Mathematical approach and statistical analysis” Magn. Reson. Med. 36 715–25 2. Andersen IK et al; “Perfusion Quantification Using Gaussian Process Deconvolution”. Magnetic Resonance in Medicine 48:351-361 (2002). 3. Williams CKI and Rasmussen CE; “Gaussian processes for regression”. Advances in neural information processing systems, (1996), 514-520. 4. Choudhury A, Nair PB and Keane A; “A Data Parallel Approach for Large-Scale Gaussian Process Modeling”. Proc. the Second SIAM International Conference on Data Mining (2002).
Student project type: 

A Framework for Metadata Driven e-Science Implementations

Student: 
Yin Chen

Many e-Science applications are data intensive. Metadata is at the heart to serve the semantic interpretation, discovery, and integration of large-scale heterogeneous scientific data. The project explores observations of how metadata are used in a variety of e-Science disciplines, such as annotation, workflow, information integration, provenance, and curation. It aims to analyse the requirements for metadata central to e-Science applications, and examines state-of-the-art approaches.

Project status: 
Finished
Degree level: 
PhD
Supervisors @ NeSC: 
Other supervisors: 
Dr Stuart Aitken (School of Informatics, University of Edinburgh)
Subject areas: 
Databases
Distributed Systems
Software Engineering
Student project type: 

Pages

Subscribe to RSS - PhD student project