Norman Fenton

Queen Mary University of London (Professor)
QMUL Staff Profile

Norman’s current research focuses primarily on quantitative risk assessment. This typically involves analysing and predicting the probabilities of unknown events using Bayesian statistical methods including especially causal, probabilistic models (Bayesian Networks). This type of reasoning enables improved assessment by taking account of both statistical data and also expert judgment. In addition, Norman has a long track record of work in software engineering (including pioneering work on software metrics). The new third edition of his book “Software Metrics: A Rigorous and Practical Approach” was published in November 2014.


William Marsh

Queen Mary University of London (Professor)
QMUL Staff Profile

William’s research aims are to develop better ways to build useful risk and decision making techniques, using a combination of data and knowledge (or expertise). He mainly works with Bayesian networks and prefers to work with ‘end users’ who are making decisions. He is currently collaborating with several groups of clinicians to build decision support systems for medical decision problems.


Scott Mc Lachlan

Queen Mary University of London (PDRA, PhD Candidate)
Scott’s Profile

Scott is currently working on the PAMBAYESIAN project as a postdoctoral research assistant. He accumulated over 17 years experience in Infrastructure and Software Architecture, systems integration and Workflow design before returning to university to complete graduate and postgraduate qualifications and research in Health Informatics, Health and Cyber Law, Knowledge Engineering and Data Mining.


Maggie Wang

Queen Mary University of London (PDRA, PhD)
QMUL Staff Profile

Maggie is currently working on the PAMBAYESIAN project as a postdoctoral research assistant. She had worked for two years as a Postdoc in Haematopoietic Stem Cell Lab, Cambridge Institute for Medical Research. Before that, she got her PhD in applied statistics at Wageningen University, the Netherlands and her MSc in applied mathematics and computing at Cranfield University, United Kingdom. Her research has focused on developing algorithms for construction of probabilistic graphical models, especially Bayesian networks.


Anthony Constantinou

Queen Mary University of London (Lecturer)
Anthony’s QMUL Profile

Anthony is a Lecturer in Machine Learning and Data Mining. His research interests are in Bayesian Artificial Intelligence for causal discovery. This partly involves working with structure learning algorithms that discover Bayesian Network graphs, by taking into consideration different types of information such as available data, rule-based information, decision options, utilities to maximise and risks to minimise.


Anne Hsu

Queen Mary University of London (Lecturer)
Anne’s QMUL Profile

Anne’s work combines computer science and psychology. Her research includes machine learning, artificial agents, natural language processing and learning, human decision making, interaction design, and well being technology. Anne’s interests include developing interactive systems that use machine learning and understanding of human psychology to improve human behaviour. Her doctoral and postdoctoral research was in neuroscience and her PhD was awarded from the Department of Physics at UC Berkeley.


Martin Neil

Martin Neil

Queen Mary University of London (Professor)
QMUL Staff Profile

Martin is a Professor in Computer Science and Statistics in Queen Mary, University of London. His research interests cover Bayesian modeling and risk quantification in diverse areas. Experience in applying Bayesian methods to real problems has convinced him that intelligent risk assessment and decision analysis requires knowledge and data. Not just “Big Data”. He is also a joint founder and of Agena Ltd, who develop and distribute AgenaRisk, a software product for modeling risk and uncertainty. At Queen Mary he teaches decision and risk analysis and software engineering.

 


Lina Kyrimi

Evangelia (Lina) Kyrimi

Queen Mary University of London (PDRA, PhD)
QMUL Staff Profile

Evangelia is a statistician who works as a research assistant on the Pambayesian project. Having recently attained her PhD in computer science at Queen Mary University of London. Her thesis is titled “Bayesian Networks for Clinical Decision Making: Support, Assurance, Trust”. Her research interests lie in Bayesian modeling and decision support under uncertainty in medical applications.


Mariana Raniere

Queen Mary University of London (PhD Student)
QMUL Staff Profile

Mariana is a PhD student in Computer Science at Queen Mary University. She has bachelor and master degrees in Statistics from the Federal University of Rio de Janeiro, where she also worked as a lecturer. She has also worked as a researcher at the Institute for Applied Economic Research (Brazil). Her research interests lie in Bayesian Inference, Dynamic Models and applications of Bayesian Statistics.


Ali Fahmi

Queen Mary University of London (PhD Student)
Personal Website
QMUL Staff Profile

Ali is a PhD student in Computer Science at Queen Mary University of London. He holds a Bachelor’s degree in Industrial Engineering from University of Tabriz, Iran, and a Master’s degree in Management Engineering from Istanbul Technical University, Turkey. His research interests focus on decision support, Bayesian networks, and their application in medical and social contexts.


Haoyuan Zhang

Queen Mary University of London (PhD Student)
Personal Website
QMUL Staff Profile

Haoyuan is a Computer Science PhD student at QMUL. His thesis is entitled “A Bayesian-based Framework for Making Maintenance Decisions from Data and Expert Knowledge”. He is currently working on hierarchical Bayesian modelling for parameter learning and Bayesian Networks for supporting decisions making. He holds an MSc in Industrial Engineering and Logistic Management from the University of Hong Kong, where he worked on using optimisation techniques to plan and schedule manufacturing processes.



Jiali Wang

Queen Mary University of London (PhD Student)
QMUL Student Profile

Jiali is a Ph.D. student in Computer Science at Queen Mary University. She got her MSc in Quantitative Finance at Lancaster University, United Kingdom and B.S in Mathematics and Applied Mathematics at the University of Science and Technology Beijing, China. Her research has focused on Bayesian modeling, intelligent risk assessment, decision support, and their application in the cyber security field.