University of Edinburgh - Equipment Account
Lead Research Organisation:
University of Edinburgh
Department Name: UNLISTED
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
Publications
Brock A
(2016)
Context-Aware Content Generation for Virtual Environments
Foukas X
(2016)
FlexRAN
Harries A
(2016)
Compositional Compilation for Sparse, Irregular Data Parallelism
Faldu P
(2016)
LLC Dead Block Prediction Considered Not Useful
Tammana P
(2016)
Simplifying Datacenter Network Debugging with PathDump
Edwards H
(2016)
Censoring Representations with an Adversary
Brock Andrew
(2016)
Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
in arXiv e-prints
Papamakarios G
(2016)
Fast e-free Inference of Simulation Models with Bayesian Conditional Density Estimation
Rathinakumar S
(2016)
CPRecycle
Fowler S
(2016)
An Erlang Implementation of Multiparty Session Actors
in Electronic Proceedings in Theoretical Computer Science
Nash C
(2016)
Generative models of part-structured 3D objects
Eduardo S
(2016)
Data Cleaning using Probabilistic Models of Integrity Constraints
Cummins C
(2016)
Towards Collaborative Performance Tuning of Algorithmic Skeletons
Papamakarios G
(2015)
Distilling Intractable Generative Models
Karampatsis, R
(2015)
CDTDS: Predicting Paraphrases in Twitter via Support Vector Regression
Tammana P
(2015)
CherryPick
Edwards Harrison
(2015)
Censoring Representations with an Adversary
in arXiv e-prints
Miller M
(2015)
Carpet unrolling for character control on uneven terrain
Manilov S
(2015)
Free Rider
Geras Krzysztof J.
(2014)
Scheduled denoising autoencoders
in arXiv e-prints
Description | Equipment account to support the EPSRC and MRC Centre for Doctoral Training in Optical Medical Imaging (OPTIMA): The equipment provided to support OPTIMA has been invaluable in fostering interdisciplinary collaborations and bringing physical sciences together with biomedical applications. The majority of the equipment bought with this grant has been sited at the Queen's Medical Research Institute and so has been invaluable in enabling precise fluorescence, Raman spectroscopy and other optical measurements to take place in the setting of biomedical research labs. This has facilitated interactions between the chemists, engineers and clinicians involved in OPTIMA research. The research domains of the CDTs in Pervasive Parallelism and Data Science are both inherently quite broad (eg hence the "Pervasive" in the title), and each published paper makes it contribution in a more constrained sub-area. It is noteworthy that many of these have been made in top-tier publication venues, including HPCA, NIPS, ASPLOS, CGO and EUROSYS, in some instances winning best paper awards. These results relied upon the existence of the equipment provided by this grant. |
Exploitation Route | The CDTs are very diverse - component research projects may be taken forward by future destinations of our graduates, and by further projects sparked by interactions at our academic and industrial events. |
Sectors | Digital/Communication/Information Technologies (including Software),Electronics,Energy |
URL | http://web.inf.ed.ac.uk/infweb/student-services/cdt/ds |
Description | As in previous years, research findings presented in the many papers listed have all been delivered at international venues. Additionally, they have formed the basis of student presentations at a string of CDT industrial engagement events. These have sparked interactions which have led to numerous internships in industry. Within Informatics, these internships are a primary means of dissemination of research results beyond academia, both during the internship and often subsequently in the permanent employment which flows from them. In the EPSRC and MRC CDT in Optical Medical Imaging, access to equipment has driven new collaborations between physical sciences and biomedical sciences (>40 new collaborations). |