Edinburgh Research Explorer

Data Science CDT

Organisational unit: Sub-School

Contact information

10 Crichton Street
EH8 9AB
Edinburgh
United Kingdom

Organisation profile

Centre for Doctoral Training in Data Science

The EPSRC Centre for Doctoral Training (CDT) in Data Science, based at the School of Informatics, University of Edinburgh, is a world-class PhD programme training a new generation of data scientists with the technical skills and interdisciplinary awareness necessary to become R&D leaders in this dynamic area.  It comprises 50 PhDs over five intake years and the first cohort started the programme in September 2014.

Data science is the study of the computational principles, methods, and systems for extracting knowledge from data. Large data sets are now generated by almost every activity in science, society, and commerce — ranging from molecular biology to social media, from sustainable energy to health care. 

Data science asks: How can we efficiently find patterns in these vast streams of data? Many research areas have tackled parts of this problem: machine learning and artificial intelligence provide methods for finding patterns and making predictions and decisions from data; databases are needed for efficiently accessing data and ensuring its quality; statistics and optimization provide fundamental mathematical ideas and methods; ideas from algorithms are required to build systems that scale to big data streams; and natural language processingcomputer vision, and speech processing are each needed for analysis of different types of unstructured data. Recently, these distinct disciplines have begun to converge into a single field called data science.

Research outputs

  1. DNN Multimodal Fusion Techniques for Predicting Video Sentiment

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  2. Recognizing Emotions in Video Using Multimodal DNN Feature Fusion

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  3. Learning interpretable control dimensions for speech synthesis by using external data

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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