Prof Simon Maskell

Biografy

 I'm a Professor of Autonomous Systems at the University of Liverpool within the School of Electical Engineering, Electronics and Computer Science where I am affiliated to both the Centre for Autonomous Systems and the Institute for Risk and Uncertainty. I teach "Control Theory" (to second year undergraduates) and a Big Data Analytics module as part of a new MSc on Big Data and High Performance Computing (being delivered in partnership with the UK centre for supercomputing at STFC's Hartree Centre). I have also historically taught "Image Processing" (to a mix of third and fourth year undergraduates and MSc students).

I remain an Honorary Research Fellow in the Communications and Signal Processing Group in the Electrical and Electronic Engineering Department at Imperial College. Up until the end of 2012, I had been the "Technical Manager" for C2IS (Command and Control Information Systems) and a Senior QinetiQ fellow at QinetiQ and a Visiting Industrial Professor in the Engineering Department at Bristol University. At QinetiQ, I led projects conducting research and development (eg into different aspects of the multi-sensor multi-target tracking problem); the algorithms tackle problems such as detection, tracking, optimisation, pattern recognition, information management and intelligence processing.

In 2000, I was lucky enough to be awarded a Royal Commission for the Exhibition of 1851 Industrial Fellowship, which funded my PhD at the Signal Processing Group of Cambridge University Engineering Department. I was supervised by Professor Bill Fitzgerald at Cambridge and by Dr Neil Gordon (who is now at DSTO) and later Dr Alan Marrs at QinetiQ. My thesis was on "Sequentially Structured Bayesian Solutions". I researched how Bayesian tracking algorithms exploit the structure of problem that they tackle: time is ordered and tracking algorithms exploit the fact that knowledge of what's happening now can therefore be sufficient in terms of the past's ability to predict the future. I am now particularly interested in the ability to use the structure of problems in general in the design of algorithms for their solution. As such, I am pleased to be working on difficult problems being tackled by the Artificial Intelligence community for which I hope to develop particularly efficient and robust solutions. These include: inference in graphical models with loops (eg robustly processing very noisy images); learning strategies in partially observed games (ie getting a computer to learn from experience how to fool a human); tracking of articulated objects (eg tracking people in crowds using a network of webcams).


Research Grants

  • Multi source automated NSIF generation
  • WEB-RADR
  • Bayesian Analysis of Competing Cyber Hypotheses

Teaching Areas

  • Big Data Analysis
  • Image Processing
  • Instrumentation and Control
  • Risk and Uncertainty

External Engagement

Professional Membership

  • Institution of Engineering and Technology (Fellow Inst of Eng. and Technol.)

Academic Roles

  • IEEE Transactions Aerospace and Electronic Systems (Associate Editor 2014 - )
  • IEEE Signal Processing Letters (Associate Editor 2014 - )

 Publications

2015

Datasets reflecting students' and teachers' views on the use of learning technology in a UK university (Journal article)

Limniou, M., Downes, J. J., & Maskell, S. (2015). Datasets reflecting students' and teachers' views on the use of learning technology in a UK university. British Journal of Educational Technology, 46(5), 1081-1091.

DOI: 10.1111/bjet.12332


Social media and pharmacovigilance: A review of the opportunities and challenges (Journal article)

Sloane, R., Osanlou, O., Lewis, D., Bollegala, D., Maskell, S., & Pirmohamed, M. (2015). Social media and pharmacovigilance: A review of the opportunities and challenges. British Journal of Clinical Pharmacology, 80(4), 910-920.

DOI: 10.1111/bcp.12717


2013

Articulated human body parts detection based on cluster background subtraction and foreground matching (Journal article)

Bhaskar, H., Mihaylova, L., & Maskell, S. (2013). Articulated human body parts detection based on cluster background subtraction and foreground matching. Neurocomputing, 100, 58-73.

DOI: 10.1016/j.neucom.2011.12.039


2011

Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods (Journal article)

Gning, A., Mihaylova, L., Maskell, S., Pang, S. K., & Godsill, S. (2011). Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods. IEEE Transactions on Signal Processing, 59(4), 1383-1396.

DOI: 10.1109/TSP.2010.2103062


2010

A Bayesian approach to joint tracking and identification of geometric shapes in video sequences (Journal article)

Minvielle, P., Doucet, A., Marrs, A., & Maskell, S. (2010). A Bayesian approach to joint tracking and identification of geometric shapes in video sequences. Image and Vision Computing, 28(1), 111-123.

DOI: 10.1016/j.imavis.2009.05.002


Smoothing algorithms for state–space models (Journal article)
 
Briers, M., Doucet, A., & Maskell, S. (2010). Smoothing algorithms for state–space models. Annals of the Institute of Statistical Mathematics, 62(1), 61-89.

DOI: 10.1007/s10463-009-0236-2


2008

A Bayesian approach to fusing uncertain, imprecise and conflicting information (Journal article)

Maskell, S. (2008). A Bayesian approach to fusing uncertain, imprecise and conflicting information. Information Fusion, 9(2), 259-277.

DOI: 10.1016/j.inffus.2007.02.003