EPSRC Reference: |
EP/N014162/1 |
Title: |
Deep Probabilistic Models for Making Sense of Unstructured Data |
Principal Investigator: |
Alvarez Lopez, Dr M A |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Neurosciences |
Organisation: |
University of Sheffield |
Scheme: |
Standard Research |
Starts: |
01 April 2016 |
Ends: |
30 September 2019 |
Value (£): |
974,162
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EPSRC Research Topic Classifications: |
Artificial Intelligence |
Information & Knowledge Mgmt |
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EPSRC Industrial Sector Classifications: |
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Related Grants: |
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Panel History: |
Panel Date | Panel Name | Outcome |
03 Sep 2015
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Making Sense From Data Panel - Full Proposals
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Announced
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Summary on Grant Application Form |
The future information infrastructure will be characterized by massive streaming sets of distributed data-sources. These data will challenge classical statistical and machine learning methodologies both from a computational and a theoretical perspective. This proposal investigates a flexible class of models for learning and inference in the context of these challenges. We will develop learning infrastructures that are powerful, flexible and 'privacy
aware' with a user-centric focus. These learning infrastructures will be developed in the context of particular application challenges, including mental health, the developing world and personal information management. These applications are inspired by collaborations with citizenme, the NewMind Network for Mental Health Technology Research and Makerere University in Kampala, Uganda.
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Key Findings |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Project URL: |
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Further Information: |
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Organisation Website: |
http://www.shef.ac.uk |