This is a European Research Council Advanced Grant (value 1,572,562 euros) for a 4-year programme from March 2014-March 2018. The project aims to improve evidence-based decision-making in areas such as medicine, law, forensics, and transport. What makes it radical is that it plans to do this in situations (common for critical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. Our solution is to develop a method to systemize the way expert driven causal (Bayesian Network) models can be built and used effectively either in the absence of data or as a means of determining what future data is really required. The project will enable scientists, statisticians, medics and lawyers, to be better able to reason about probability and understand the role and limitations of data, making better decisions with less data.
Collaboration with the Forensic Psychiatry Research Unit in the Wolfson Institute, September 2012-2014 Principal Investigator (RIM): William Marsh Funded by NIHR as part of the FPRU’s ongoing NIHR-funded programme grant Improving Risk Management in Mental Health
Collaboration with the Centre for Sports and Exercise Medicine, April 2012 - March 2013 Principal Investigator (RIM): William Marsh Funded by AXA PPP and ImpactQM KTA.
EPSRC Case Studentship, Co-supervisors: Dr Caroline Brennan (SBCS) and Dr Fabrizio Smeraldi, 2012-2015
D Caroline Brennan (SBCS, Principal Investigator), Dr Fabrizio Smeraldi (RIM) and Dr Matthew Parker (SBCS), £36775 of which £18000 contributed by Pfizer International, ImpactQM, 2012-2013
Using Bayesian network causal models to predict Premiership football results. This was a funded PhD studentship for Anthony Constantinou (2009-2012) and a KTA Scheme 1 secondment of Anthony Constantinou to Agena Ltd, 01.06.2012 to 31.08.2012, £4,798
In this project we will address how to make person re-identification systems scale effectively to many camera views. Effective many camera re-identification systems are currently infeasible because for this situation every O(N^2) pair of cameras defines a distinct machine learning problem. We will develop new transfer learning techniques to allow knowledge to be adaptively shared between camera pairs, making the task practically scalable for the first time.
Experts (including statisticians and forensic scientists) have argued for many years that Bayesian reasoning has the potential to improve the efficiency, transparency and fairness of the justice system, and to avoid the kind of fallacies in probabilistic reasoning that have not only troubled the appellate courts but are also likely to have misled tribunals of fact in many trials. This project was initiated by Norman Fenton, Professor of Risk Information Management at Queen Mary, University of London (QMUL), and is being developed with Amber Marks, Lecturer in Criminal Law and Evidence, QMUL. So far more than 80 interested parties from around the world have agreed to participate in a multi-disciplinary network that brings together world-class mathematicians, scientists, psychologists, legal academics and practitioners, police officers, journalists and lay people to collaborate on the issues surrounding the use of probabilistic reasoning in criminal law. This project was orginally a short term KT Project ECSA1F8R, 01/09/2011-28/02/2012, £26,338.
Digital Economy Research Cluster EP/G001987/1. April 2008-March 2009. Funding to QM: £196,425. Partners: Department of Mathematics and the Center of Advanced Computing and Emerging Technologies (ACET), University of ReadingInstitute of Particle Science and Engineering, Leeds University, Statistics Research Group, in the School of Mathematical Science, Queen Mary, ·Trauma Care Unit, Royal London Hospital; ·Centre for Haematology, Institute of Cell and Molecular Science Barts and The London School of Medicine and Dentistry; Oral Growth and Development, Institute of Dentistry, Barts and The London School of Medicine and Dentistry; Forensic Psychiatry Research Unit, Barts and The London School of Medicine and Dentistry; Dept of Biosurgery and Surgical Technology, Imperial College;· Centre for Health Management, Tanaka Business School, Imperial College London; Centre for Reviews and Dissemination, University of York; Agena Ltd; ESiOR Ltd Finland;· Department of Computer Science, Surrey University (Prof Paul Krause)View More