Simon Maskell's Homepage

That's me in my office and this is my website. You can contact me on: s maskell liverpool ac uk >.

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 director of the EPSRC Centre for Doctoral Training in Distributed Algorithms. I also lead the Liverpool Big Data Network and am affiliated to both the Centre for Autonomous Systems and the Institute for Risk and Uncertainty. I currently teach "Control Theory" (to second year undergraduates). I have also historically taught "Image Processing" (to a mix of third and fourth year undergraduates and MSc students) and a Big Data Analytics module as part of the MSc on Big Data and High Performance Computing (being delivered in partnership with the UK centre for supercomputing at STFC's Hartree Centre). My research team currently comprises:

• Data Scientists
• Marcel Hernandez (senior Data Scientist);
• Alex Phillips (Data Scientist, and a former PhD student, (thesis));
• Alberto Acuto (Data Scientist);
• Tony Thompson (Data Scientist);
• Shashi Lakra (Data Scientist).
• Post Docs
• Paul Horridge (Post-Doctoral Research Assistant, funded by Dstl);
• Yifan Zhou (Post-Doctoral Research Assistant, funded by EPSRC's Big Hypotheses project, and a former PhD student, (thesis));
• Lyudmil Vladimirov (PDRA, funded by AFOSR and a former PhD student, (thesis));
• Matt Bright (Post-Doctoral Research Assistant, funded by EPSRC's Big Hypotheses project);
• Soodeh Habibi (Post-Doctoral Research Assistant, funded by EPSRC's Big Hypotheses project);
• Alexey Narykov (Post-Doctoral Research Assistant, funded by ESA).
• PhD Students
• Gemma Cook (PhD student, co-funded by an EPSRC/ESRC CDT and Dstl, co-supervised with Marek Ziebart and Corina Constantinescu);
• Marco Fontana (PhD student, funded by Sintela and aligned with EPSRC Centre for Doctoral Training in Distributed Algorithms, co-supervised with Angel Garcia-Fernandez as primary supervisor);
• Vincent Beraud (PhD student, funded by EPSRC Centre for Doctoral Training in Distributed Algorithms with UK Government, co-supervised with Vassil Alexandrov (from STFC's Hartree Centre);
• Matthew Carter (PhD student, funded by EPSRC Centre for Doctoral Training in Distributed Algorithms with IBM, co-supervised with Paul Spirakis;
• Efthyvoulos Drousiotis (PhD student, funded by EPSRC Centre for Doctoral Training in Distributed Algorithms with the National Crime Agency, co-supervised with Paul Spirakis;
• Panagiotis Pentaliotis (PhD student, funded by EPSRC Centre for Doctoral Training in Distributed Algorithms with the National Crime Agency, co-supervised with Paul Spirakis;
• Andy Millard (PhD student, funded by EPSRC Centre for Doctoral Training in Distributed Algorithms with UK Government, co-supervised by Simon Goodchild (from STFC's Hartree Centre);
• Josh Murphy (PhD student, funded by EPSRC Centre for Doctoral Training in Distributed Algorithms with UK Government, co-supervised by Simon Goodchild (from STFC's Hartree Centre);
• Adam Neal (PhD student, funded by EPSRC Centre for Doctoral Training in Distributed Algorithms with Aleph Insights, Frazer-Nash Consulting and Dstl, co-superviseed with Peter Green);
• Daniel Sumler (PhD student, funded by EPSRC Centre for Doctoral Training in Distributed Algorithms with QinetiQ, co-supervised with Lee Devlin as primary supervisor).

• Flávio de Melo (PhD student)(thesis);
• Matteo Fasiolo (Post-Doctoral Research Assistant, who was funded by EPSRC to work on Bayesian Analysis of Competing Cyber Hypotheses);
• Roberta Piroddi (Post-Doctoral Research Assistant, who was funded by EPSRC to work on Bayesian Analysis of Competing Cyber Hypotheses);
• Richard Sloane (Post-Doctoral Research Assistant, who was working on WEB-RADR);
• Joanna Hajne (Post-Doctoral Research Assistant, working on WEB-RADR);
• Elias Griffith (Post-Doctoral Research Assistant, who was funded (via Roke Manor) by Dstl, and worked on WEB-RADR).
• Lykourgos Kekempanos (PhD student, who was funded by EPSRC), (thesis);
• Chloe Barrett-Pink (PhD student, who was funded by Dstl, co-supervised with Laurence Alison as primary supervisor)(thesis);
• James Wright (PhD student, who was funded by EPSRC iCASE award with Airbus), (thesis);
• Mark Mawdsley (Data Scientist);
• Christian Pollitt (Data Scientist);
• Lee Devlin (Post-Doctoral Research Assistant, who was funded by Dstl, National Crime Agency and EPSRC's Big Hypotheses project).
• Darren Cook (PhD student, who was funded by an EPSRC/ESRC CDT and co-supervised with Laurence Alison), (thesis);
• Phil Clemson (Post-Doctoral Research Assistant, who was funded by Dstl and EPSRC's Big Hypotheses project);
• Alessandro Varsi (Post-Doctoral Research Assistant, who was funded by EPSRC's Big Hypotheses project, and was a former PhD student, (thesis));
• Conor Rosato (Post-Doctoral Research Assistant, who was funded by an EPSRC/ESRC CDT and supported by Dstl, co-supervised with John Harris and Sarah O'Brien), (thesis).
• Elpida Kontsioti (PhD student, who was co-funded by an EPSRC/ESRC CDT and Astrazeneca, co-supervised with Munir Pirmohamed), (thesis);
• Robert Moore (PhD student, who was funded by an EPSRC iCASE award).
• Michael Ransom (PhD student, who was funded by an EPSRC iCASE award with Leonardo, co-supervised with Jason Ralph);
• Katerina Chatzopoulou (PhD student, who was funded by an EPSRC iCASE with IBM, co-supervised with Angel Garcia-Fernandez), (thesis);

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, a Visiting Industrial Professor in the Engineering Department at Bristol University and an Honorary Research Fellow in the Communications and Signal Processing Group in the Electrical and Electronic Engineering Department at Imperial College. 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).

I live very happily with my wife, Michelle, and my two sons in Allerton in Liverpool, UK; Allerton is a leafy suburb of Liverpool, which is about two hours by (fast) train from London. I used to thoroughly enjoying playing Rugby Fives (here's a video that, if you look carefully, you will see includes me playing an even more obscure sport, Winchester Fives) and occasionally go for a run or play squash or football, but I've recently started playing tennis more. I don't sail though - that's another Simon Maskell. Things I like include: Lobster, Mange Tout, Chocolate, Pink Floyd, Goldie Lookin Chain, The Egg, Fight Club, Fifth Element, City of Lost Children, Cezanne, Matisse and Picasso. Things I don't like so much include: pickled beetroot, Justin Timberlake, Citizen Kane and Turner.

I went to South America once and took a load of pictures of the Iguazu falls which I merged together. I also went to Marloes Sands in West Wales and Kennedy Space Centre in Florida and did the same. These results look like this:

The following is planned to be an up-to-date list of my publications - time will tell. The publications document my thoughts at various points. Co-authors (who have a mention because they have websites) include Yaakov Bar-Shalom, Mark Briers (who also received one of the aforementioned Royal Commission for the Exhibition of 1851 Industrial Fellowships, to conduct his PhD at Cambridge University with Arnaud Doucet and at QinetiQ with me), Richard Everitt, Kiruba and Ben Alun-Jones. Where possible, I've provided links to versions of the documents. Some of these necessitate appropriate subscriptions to online sources (ieeexplore etc); if the links don't work, it may be because you shouldn't have access!

Journal Papers / Book Chapters

• A Howard, D Hughes, P Green, A Velluva, A Gerada, S Maskell, I Buchan, and W Hope. Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use. Accepted for publication in Nature Communications. 2024.
• C Rosato, P Green, J Harris, S Maskell, W Hope, A Gerada and A Howard. Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review. Accepted for publication in IEEE Access. 2024.(pdf)
• A Abuzour, S Wilson, A Woodall, F Mair, A Clegg, E Shantsila, M Gabbay, M Abaho, A Aslam, D Bollegala, H Cant, A Griffiths, L Hama, G Leeming, E Lo, S Maskell, M O'Connell, O Popoola, S Relton, R Ruddle, P Schofield, M Sperrin, T Van Staa, I Buchan and L Walker. A qualitative exploration of barriers to efficient and effective Structured Medication Reviews in Primary Care: Findings from the DynAIRx study. Accepted for publication in PLOS ONE. 2024. (medRxiv)
• L Devlin, M Carter, P Horridge, P L Green and S Maskell. The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject. IEEE Signal Processing Letters. 2024. (pdf)
• E Kontsioti, S Maskell, I Anderson and M Pirmohamed. Identifying Drug-Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects. Accepted for publication in Clinical Pharmacology and Therapeutics, 2024.(preprint), (pdf)
• A Varsi, L Devlin, P Horridge and S Maskell. A General-Purpose Fixed-Lag No-U-Turn Sampler for Nonlinear Non-Gaussian State Space Models. Accepted for publication in IEEE Transactions on Aerospace and Electronic Systems, 2024. (pdf)
• L Anastassiou, J F Ralph, S Maskell and P Kok. Bayesian Estimation for Bell State Rotations. AVS Quantum Science. Vol 4, Issue 2. 2024.(arXiv preprint),(pdf)
• M Hernandez, A Garcia-Fernandez and S Maskell. Non-Myopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation. Accepted for publication in IEEE Transactions on Aerospace and Electronic Systems, 2023.(arXiv preprint), (pdf)
• Y Msosa, A Grauslys, Y Zhou, T Wang, I Buchan, P Langan, S Foster, M Walker, M Pearson, A Folarin, A Roberts, S Maskell, R Dobson, C Kullu and D Kehoe. Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People with Depression. IEEE Journal of Biomedical and Health Informatics. 2023. (pdf)
• G Burnside, C P Cheyne, G Leeming, M Humann, A Darby, M A Green, A Crozier, S Maskell, K O'Halloran, E Musi, E Carmi, N Khan, D Fisher, R Corcoran, J Dunning, W J Edmunds, K Tharmaratnam, D M Hughes, L Malki-Epshtein, M Cook, B M Roberts, E Gallagher, K Howell, M Chand, R Kemp, M Boulter, T Fowler, M G Semple, E Coffey, M Ashton, The COVID-19 Genomics UK Consortium, M Garcia-Finana and I E Buchan. COVID-19 risk-mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region. Journal of Royal Society of Medicine. 2023. (pdf)
• S Anderson, L Stone and S Maskell. Repeated Filtering for Smoothing Particle Filters. JAIF. 18(1). 2023. (pdf)
• E Kontsioti, S Maskell and M Pirmohamed. Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions. Pharmacoepidemiology and Drug Safety. 2023. (pdf), (response to comment)
• C Rosato, R Moore, M Carter, J Heap, J Harris, J Storopoli and S Maskell. Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models. Information (Special Issue on The Role of Social Media during the Ongoing Outbreaks of COVID-19 and Monkeypox: Applications, Use-Cases, Analytics, and Beyond)). 2023. 14(3). (pdf)
• M Fontana, A Garcia-Fernandez and S Maskell. Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter. IEEE Transactions on Aerospace and Electronic Systems, 2023.(pdf)
• M Uney, P Horridge, B Mulgrew and S Maskell. Coherent long-time integration and Bayesian detection with Bernoulli track-before-detect. IEEE Signal Processing Letters, 2023. (pdf)
• L Walker, A Abuzour, D Bollegala, A Clegg, M Gabbay, A Griffiths, C Kullu, G Leeming, F Mair, S Maskell, S Relton, R Ruddle, E Shantsila, M Sperrin, T Van Staa, A Woodall and I Buchan. The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity. Journal of Multimorbidity and Comorbidity, 2022.(pdf)
• S Maskell, Y Zhou and A Mira. Control Variates for Constrained Parameters. IEEE Signal Processing Letters. Vol 29, pp. 2333-2337. 2022.(pdf)
• C Taylor, S Maskell and J F Ralph. Using hybrid multiobjective machine learning to optimize sonobuoy placement patterns. IET RSN. 2022.(pdf)
• M J Wright, L Anastassiou, C Mishra, J Davies, A Phillips, S Maskell and J F Ralph. Cold Atom Inertial Sensors for Navigation Applications. Frontiers in Physics. 2022. (pdf)
• R Moore, C Rosato and S Maskell. Refining epidemiological forecasts with simple scoring rules. Phil. Trans. R. Soc. A. 2022. (arXiv preprint), (pdf)
• A Phillips, M J Wright, I Riou, S Maddox, S Maskell and J F Ralph. Position fixing with cold atom gravity gradiometers. AVS Quantum Science. 2022. (pdf), (highlight)
• C Rosato, L Devlin, V Beraud, P Horridge, T Schön, S Maskell. Efficient Learning of the Parameters of Non-linear Models Using Differentiable Resampling in Particle Filters. IEEE Transactions on Signal Processing. 2022. (pdf),(arXiv preprint)
• E Kontsioti, S Maskell, A Bensalem, B Dutta and M Pirmohamed. Similarity and Consistency Assessment of Three Major Online Drug-Drug Interaction Resources. British Journal of Clinical Pharmacology. 2022. (pdf)
• J Wu, L Wen, P Green, J Li and S Maskell. Ensemble Kalman Filter based Sequential Monte Carlo Sampler for Sequential Bayesian Inference. Statistics and Computing. 32:20. 2022.(arXiv preprint). (pdf)
• E Kontsioti, S Maskell, B Dutta, and M Pirmohamed. A Reference Set of Clinically Relevant Adverse Drug-Drug Interactions. Nature Scientific Data 9. 2022.(pdf)
• A Varsi, S Maskell and P Spirakis. An O(log₂N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures. Algorithms. 2021. 14(12) 342. (pdf)
• A Garcia-Fernandez, S Maskell, P Horridge and J F Ralph. Gaussian Tracking With Kent-Distributed Direction-of-Arrival Measurements. IEEE Transactions on Vehicular Technology, 2021. (pdf)
• P L Green, L Devlin, R Moore, R Jackson, J Li and S Maskell. Increasing the Efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels. Mechanical Systems and Signal Processing. Vol 162. 2022.(arXiv preprint). (pdf).
• A F Garcia-Fernandez, J F Ralph, P Horridge and S Maskell. A Gaussian filtering method for multi-target tracking with nonlinear/non-Gaussian measurements. IEEE Transactions on Aerospace and Electronic Systems. 2021. (pdf)
• A Varsi, J Taylor, L Kekempanos, E Pyzer-Knapp and S Maskell. A Fast Parallel Particle Filter for Shared Memory Systems. IEEE Signal Processing Letters. 2020. (pdf)
• A Garcia-Fernandez and S Maskell. Continuous-Discrete Multiple Target Filtering: PMBM, PHD and CPHD Filter Implementations. IEEE Transactions on Signal Processing. 68: 1300-1314. 2020. (pdf)
• J van Stekelenborg, J Ellenius, S Maskell, T Bergvall, O Caster, N Dasgupta, J Dietrich, S Gama, D Lewis, V Newbould, S Brosch, C Pierce, G Powell, A Ptaszynska-Neophytou, A Wisniewski, P Tregunno, G N Noren and M Pirmohamed. Recommendations for the use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR. Drug Safety. 2019. (pdf).
• C Barrett-Pink, L Alison, S Maskell and N Shortland. On the Bridges: Insight Into the Current and Future Use of Automated Systems as Seen by Royal Navy Personnel. Journal of Cognitive Engineering and Decision Making. 2019. (pdf)
• C Pierce, S de Vries, S Bodin-Parssinen, L Harmark, P Tregunno, D Lewis, S Maskell, R Van Eemeren, A Ptaszynska-Neophytou, V Newbould, N Dasgupta, A Wisniewski, S Gama and P Mol. Recommendations on the Use of Mobile Applications for the Collection and Communication of Pharmaceutical Product Safety Information: Lessons from IMI WEB-RADR. Drug Safety. 2019. (pdf)
• C Mishra, S Maskell, S Au, and J F Ralph. Efficient estimation of probability of conflict between air traffic using Subset Simulation. IEEE Transactions on Aerospace and Electronic Systems. 2019.(pdf)
• F De Melo and S Maskell. A CPHD approximation based on a discrete-Gamma cardinality model. IEEE Transactions on Signal Processing, 67(2):336-350, January 2019.(pdf)
• O Caster, J Dietrich, M-L Kurzinger, M Lerch, S Maskell, G N Noren, S Tcherny-Lessenot, B Vroman, A Wisniewski and J van Stekelenborg. An assessment of the utility of social media for broad-ranging statistical signal detection in pharmacovigilance: Results from the WEB-RADR project. Drug Safety. 2018.(pdf)
• J F Ralph, M Toros, S Maskell, K Jacobs, M Rashid, A J Setter and H Ulbricht. Dynamical model selection near the quantum-classical boundary. Accepted for publication in Physical Review A. 2018.(arXiv preprint). (pdf)
• D Bollegala, S Maskell, R Sloane, J Hajne and M Pirmohammed.Learning Causality Patterns for Detecting Adverse Drug Reactions from Social Media. JMIR Public Health and Surveillance. Vol 4, No 2. May 2018.(pdf)
• E J Griffith, C Mishra, J F Ralph and S Maskell. A System for the Generation of Synthetic Wide Area Aerial Surveillance Imagery. Simulation Modelling Practice and Theory. Volume 84, May 2018, Pages 286-308.(pdf),(arXiv preprint),(example flyover video),(example simulated ARGUS video)
• J F Ralph, S Maskell, K Jacobs. Multi-parameter estimation along quantum trajectories with Sequential Monte Carlo methods. Physical Review A. Vol. 96, No. 5. November 2017.(pdf),(arXiv preprint)
• J Thiyagalingam, L Kekempanos and S Maskell. MapReduce Particle Filtering with Exact Resampling and Deterministic Runtime. EURASIP Journal on Advances in Signal Processing (Special issue on Advanced Computational Methods for Bayesian Signal Processing). 2017:71. (pdf).(arXiv preprint)
• M Fassiolo, F de Melo and S Maskell. Langevin Incremental Mixture Importance Sampling. Statistics and Computing. 2017.(pdf)
• P Green and S Maskell. Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Mechanical Systems and Signal Processing. 2017. (pdf), (preprints.org)
• J Raphael, S Maskell and E Sklar. An Intersection-centric Auction-based Traffic Signal Control Framework. In Agent-Based Modeling of Sustainable Behaviors (book). Springer, 2017.(chapter)
• R Sloane, O Osanlou, D Lewis, D Bollegala, S Maskell and M Pirmohamed. Social Media and Pharmacovigilance: A Review of the Opportunities and Challenges. British Journal of Clinical Pharmacology. 2015. (pdf)
• M Limniou, J Downes, S Maskell, M Bowden and J Marshall. Datasets capturing students' and teachers' views on the role of learning technology. British Journal of Educational Technology, Special Issue: Open Data in Learning Technology. Volume 46, Issue 5, pages 1081–1091, September 2015.(pdf)
• H Bhaskar, L Mihaylova and S Maskell. Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching. Neurocomputing, Special Issue on Behaviours in Video. Volume 100. Pages 58-73. 2013.(pdf)
• A Gning, L Mihaylova, S Maskell, S Pang and S Godsill. Group Object Structure and State Estimation with Evolving Networks and Monte Carlo Methods. Volume 59, Issue 4, April 2011. pp 1383-1396. IEEE Transactions on Signal Processing.(pdf)
• P Minvielle, A Doucet, A Marrs and S Maskell.A Bayesian Approach to Joint Tracking and Identification of Geometric Shapes in Video Sequences. Journal of Image and Vision Computing. Volume 28, Issue 1, January 2010, Pages 111-123.(pdf)
• M Briers, A Doucet, and S Maskell. Smoothing algorithms for state-space models. Volume 62, Number 1, 61-89. Annals of the Institute of Statistical Mathematics. December 2010.(pdf)
• S Maskell. Statistical Methods for Target Tracking. In Wiley Encyclopedia of Computer Science and Engineering (book). Edited by B W Wah. Published by Hoboken, NJ, January 2009. Vol 5. pp 2820-2829.(pdf)
• S Maskell. A Bayesian Approach to Fusing Uncertain, Imprecise and Conflicting Information. Information Fusion Journal. 9(2):259-277. April 2008.(pdf)
• S Maskell, R Everitt, R Wright and M Briers. Multi-Target Out-of-Sequence Data Association: Tracking Using Graphical Models. Information Fusion Journal, 7(4):434-447. December 2006.(pdf)
• K Hermiston and S Maskell. Fusion Challenges in the Detection and Identification of Difficult Objects and Events. Journal of Defence Science. 10(3), September 2005.
• M Rutten, N Gordon and S Maskell. Recursive Track-Before-Detect with Target Amplitude Fluctuations. IEE Proceedings on Radar Sonar Navigation, 152(5), October 2005, pp345-352 (pdf).
• S Maskell, M Briers, R Wright and P Horridge. Tracking using a Radar and a Problem Specific Proposal Distribution in a Particle Filter. IEE Proceedings on Radar Sonar Navigation, 152(5), October 2005, pp315-322 (pdf).
• S Maskell. Joint Tracking Manoevring Targets and Classification of Their Maneovrability. EURASIP JASP 2004:15 (2004) 2339-2350 (Special Issue of EURASIP Journal on Applied Signal Processing on Particle Filtering in Signal Processing) (pdf).
• S Maskell. Basics of the Particle Filter. In N Shephard and A Harvey, editors, State Space and Unobserved Component Models (book). Cambridge University Press, 2004.
• S Maskell, N Gordon, M Rollason, and D Salmond. Efficient Multitarget Tracking using Particle Filters. Journal Image and Vision Computing, 21(10):931-939, September 2003. (pdf)
• M S Arulampalam, S Maskell, N Gordon, and T Clapp. A Tutorial on Particle Filters for On-line Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 50(2):174-188, February 2002. (pdf). Winner of IEEE Donald G Fink Prize in 2019.

Conference Papers

• 2024
• E Griffith, A Phillips, L Mai, S Maskell, J F Ralph. Target tracking in a complex simulated world. Proc SPIE Security and Defence Confernece. 2024.(pdf)
• J Murphy, C Rosato, A Millard and S Maskell. Parameterizing Hierarchical Particle Filters with Concept Drift for Time-varying Parameter Estimation. Accepted for publication in Proc APSIPA ASC. 2024.
• C Rosato, J Murphy, A Varsi, P Horridge and S Maskell. Enhanced SMC²: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals. Accepted for Publication in Proc MFI. 2024. (ArXiV preprint), (pdf)
• E Drousiotis, A Varsi, P Spirakis and S Maskell. An SMC Sampler for Decision Trees with Enhanced Initial Proposal for Stochastic Metaheuristic Optimization. Accepted for Publication in Proc LION. 2024.
• S Habibi, E Drousiotis, A Varsi, S Maskell, R Moore and P Spirakis. Conditional Importance Resampling for an Enhanced Sequential Monte Carlo Sample. Accepted for Publication in Proc LION. 2024.
• 2023
• A Acuto, S Maskell and J Duncan. Defending the Unknown: Exploring Reinforcement Learning Agents' Deployment in Realistic, Unseen Networks. Proc CAMLIS 2023. (pdf)
• C Rosato, A Varsi, J Murphy and S Maskell. An O(log₂N) SMC² Algorithm on Distributed Memory with an Approximately Optimal L-Kernel. Accepted for publication in Proc SDF/MFI, 2023. (ArXiV pre-print). 2nd Best Paper at SDF/MDI 2023.(pdf)
• E Drousiotis, A Varsi, P Spirakis and S Maskell. A Shared Memory SMC Sampler for Decision Trees. Proc SBAC-PAD 2023.(pdf)
• C Taylor, J F Ralph, S Maskell, A Narykov. Joint optimization of sonar waveform selection and sonobuoy placement. Proc SSPD 2023. (pdf)
• D Cook, M Zilka, H Desandre, S Giles and S Maskell. Protecting Children from Online Exploitation: Can a trained model detect harmful communication strategies? Proc AAAI/ACM Conference on AI, Ethics, and Society 2023.(pdf)
• E Drousiotis, D Joyce, R Dempsey, A Haines, P Spirakis, L Shi and S Maskell. Probabilistic Decision Trees for Predicting 12-Month University Students Likely to Experience Suicidal Ideation. Proc AIAI 2023.(pdf)
• 2022
• Y Zhou, L Devlin, G Cook, S Maskell and J Barr. An Automated System to Discover and Track Unknown Geosynchronous Objects using a Ground-based Optical Telescope. Proc AMOS, 2022. (pdf).
• J Barr, O Harrald, S Hiscocks, N Perree, H Pritchett, S Vidal, J Wright, P Carniglia, E Hunter, D Kirkland, D Raval, S Zheng, A Young, B Balaji, S Maskell, M Hernandez and L Vladimirov. Stone Soup open source framework for tracking and state estimation: enhancements and applications. In Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, vol. 12122, pp. 43-59. SPIE, 2022.(pdf)
• M Ransom, J F Ralph and S Maskell. Information fusion and tracking using Bernoulli filters for maritime surveillance. Proc IET Radar Conference, 2022. (pdf)
• C Taylor, S Maskell and J F Ralph. Optimizing sonobuoy placement using multiobjective machine learning. Proc SSPD 2022.(video), (pdf)
• A Narykov, M Wright, A Garcia-Fernandez, S Maskell and J F Ralph. Poisson multi-Bernoulli mixture filtering with an active sonar using BELLHOP simulation. Proc Fusion 2022.(pdf)
• C Rosato, J Harris, J Panovska-Griffiths and S Maskell. Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal. Proc Fusion 2022. (pdf)
• A Garcia-Fernandez, J F Ralph, P Horridge and S Maskell. Gaussian trajectory PMBM filter with nonlinear measurements based on posterior linearisation. Proc Fusion 2022. (pdf)
• M Fontana, A Garcia-Fernandez and S Maskell. A vehicle detector based on notched power for distributed acoustic sensing. Proc Fusion 2022. (pdf)
• E Drousiotis, L Shi, P G Spirakis, S Maskell. Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Method. Proc EANN 2022. (pdf)
• 2021
• C Rosato, R Moore, M Carter, J Heap, J Storopoliz and S Maskell. Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths. In Joint Statistical Meetings: American Statistical Association. 2021.(arXiv preprint)
• J F Ralph, S Maskell, M Ransom and H Ulbricht. Classical Tracking for Quantum Trajectories. Proc Fusion 2021.(pdf)
• M Hernandez, M Ransom and S Maskell. Posterior Cramer-Rao Bounds for Tracking Intermittently Visible Targets in Clutter. Proc Fusion 2021. (pdf)
• A Garcia-Fernandez, M Hernandez and S Maskell. An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA. Proc Fusion 2021. (pdf)
• M Ransom, M Hernandez, J F Ralph and S Maskell. Track-before-detect Bernoulli filters for Combining Passive and Active Sensors. Proc Fusion 2021. (pdf)
• A Chatzopoulou, A Garcia-Fernandez, E Pyzer-Knapp and S Maskell. SMC samplers for Bayessian Optimisation and Discovery of Additive Kernel Structure. Proc Fusion 2021. (pdf)
• E Drousiotis, L Shi, and S Maskell. Early Predictor for Student Success Based on Behavioural and Demographical Indicators. Proc International Conference on Intelligent Tutoring Systems. 2021.(preprint)
• M Uney, A Narykov, J F Ralph and S Maskell. Modelling bi-static uncertainties in sequential Monte Carlo with the GLMB model. Accepted for publication in Proc SSPD 2021.
• D Cook, M Zilka, S Maskell and L Alison. A Psychology-Driven Computational Analysis of Political Interviews. Proc. Interspeech 2021.(pdf)
• 2020
• A Phillips, M Wright, M Kiss-Toth, I Read, I Riou, S Maddox, S Maskell, and J F Ralph. Augmented Inertial Navigation Using Cold Atom Sensing. Proc SPIE Photonex and Vacuum Expo: Cold Atoms for Quantum Technologies. 2020.(pdf)
• W Huang, Y Zhou, Y Sun, J Sharp, S Maskell, X Huang. Practical Verification of Neural Network Enabled State Estimation System for Robotics. Proc IROS 2020. (pdf)
• M Ransom, L Vladimirov, P Horridge, J F Ralph and S Maskell. Integrated Expected Likelihood Particle Filters. Proc Fusion 2020. (pdf)
• L Vladimirov and S Maskell. A SMC Sampler for Joint Tracking and Destination Estimation from Noisy Data. Proc Fusion 2020. (pdf)
• A Garcia-Fernandez and S Maskell. Continuous-discrete trajectory PHD and CPHD filters. Proc Fusion 2020. (pdf)
• M Fontana, A Garcia-Fernandez and S Maskell. Bernoulli merging for the Poisson multi-Bernoulli mixture filter. Proc Fusion 2020. (pdf)
• Y Zhou and S Maskell. Robust and Efficient Image Alignment Method Using the Student-t Distribution. Proc Fusion 2020. (pdf)
• R Song, J Wetherall, S Maskell and J F Ralph.Weather Effects on Obstacle Detection for Autonomous Car. Proc VEHITS 2020. (pdf)
• Y Sun, Y Zhou, S Maskell, J Sharp and X Huang. Reliability Validation of Learning Enabled Vehicle Tracking. Proc ICRA 2020. (pdf)(arXiv preprint)(Youtube video)
• 2019
• Y Zhou, J Wright and S Maskell. A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data. Proc SDF 2019. (pdf)
• R Piroddi, E Griffith, Y Goulermas, S Maskell and J F Ralph. Using Manifold Embedding for Automatic Threat Detection: An Alternative Machine Learning Approach. Proc BMVC 2019. (pdf)
• Y Zhou and S Maskell. Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs). Proc Fusion 2019. (pdf)
• R Song, P Horridge, S Pemberton, J Wetherall, S Maskell, J F Ralph. A Multi-Sensor Simulation Environment for Autonomous Cars. Proc Fusion 2019. (pdf)
• 2018
• P Horridge and S Maskell. Fusing Bearing-Only Measurements with and Without Propagation Delays Using Particle Trajectories. Proc Fusion 2018.(pdf)
• R Piroddi, Y Goulermas, S Maskell and J F Ralph. Comparing Interrelationships Between Features and Embedding Methods for Multiple-View Fusion. Proc Fusion 2018.(pdf)
• A Soleimani, N Nasrabadi, E Griffith, J F Ralph and S Maskell. Convolutional Neural Networks for Aerial Vehicle Detection and Recognition. Proceedings of IEEE National Aerospace & Electronics Conference (NAECON), 2018.(pdf),(arXiv preprint)
• 2017
• A Varsi, L Kekempanos, J Thiyagalingam and S Maskell.Parallelising Particle Filtering for Deterministic Runtimes on Distributed Memory Systems. Proc ISP 2017.(pdf)
• C Barrett-Pink, L Alison, and S Maskell. The Air Defence Task: Understanding the cognitions that underpin automation usage to support classification decisions in practice. Proceedings of the 13th International Conference on Naturalistic Decision Making. Pages 291-297. 2017.(pdf)
• Y Zhou and S Maskell.Moving Object Detection using Background Subtraction for a Moving Camera with Pronounced Parallax. Proc SDF 2017. (pdf)
• E Clark, E Griffith, S Maskell, J F Ralph. Nonlinear Kinematics for Improved Helicopter Tracking. Proc Fusion 2017.(pdf)
• Y Zhou and S Maskell. RB²-PF: A Novel Filter-based Monocular Visual Odometry Algorithm. Proc Fusion 2017.(pdf)
• 2016
• Q M Nunes, B Lane, W Huang, K Altaf, L Rainbow, J Armstrong, W Greenhalf, D Fernig, C Hertz-Fowler, A Cossins, F Falciani, S Maskell, A Morris, R Sutton. Accurate Admission Transcriptomic Signature of the Severity of Acute Pancreatitis. Presented at 7th Meeting of the American Pancreatic Association, October 2016. (abstract)
• P Green and S Maskell.Parameter estimation from Big Data using a sequential Monte Carlo sampler. Proceedings of ISMA International Conference on Noise and Vibration Engineering. 2016.(pdf)
• J Raphael, S Maskell and E Sklar. An Empirical Investigation of Adaptive Traffic Control Parameters. Proceedings of the Workshop on Agents in Traffic and Transportation (ATT) at IJCAI 2016. 2016.(pdf)
• R Anderson, N Hare and S Maskell. Using a Bayesian Model for Confidence to Make Decisions That Consider Epistemic Regret. Proc Fusion 2016.(pdf)
• M Mehta, E Griffith, S Maskell and J F Ralph. Geometric Separation of Superimposed Images. Proc Fusion 2016.(pdf)
• 2015
• R Young, S Maskell and S Parsons. Towards Tasking Sensors in a Way that Adapts to Online Learning of when Sensors Adhere to their Performance Specifications. Proc Maths In Defence 2015.(pdf)
• J Raphael, S Maskell and E Sklar. From Goods to Traffic: First Steps Toward an Auction-based Traffic Signal Controller. Proc PAAMS 2015.(pdf)
• 2014
• C Liu, Y Zhou, F de Melo and S Maskell. Probabilistic Graphical Detector Fusion for Localization of Faces and Facial Parts. Proc SDF 2014.(pdf)
• R Lane, M Briers, T Cooper and S Maskell. Efficient Data Structures for Large Scale Tracking. Proc Fusion 2014.(pdf)
• F de Melo and S Maskell. Hybrid Gauss-Hermite filter. Proc IET Data Fusion and Target Tracking Conference. 2014.(pdf)
• 2013
• S Maskell and S Julier. Optimised Proposals for Improved Propagation of Multi-modal Distributions in Particle Filters. In Proc Fusion 2013.(pdf)
• 2012
• A Kountouriotis and S Maskell. Maneuvering Target Tracking Using an Unbiased Nearly Constant Heading Model. In Proc Fusion 2012. (pdf)
• P Kent, S Maskell, O Payne, S Richardson and L Scarff. Robust Background Subtraction for Automated Detection and Tracking of Targets in Wide Area Motion Imagery. Proc SPIE Conference on Optics and Photonics for Counterterrorism, Crime Fighting and Defence, 2012. (pdf)
• S Maskell. An Application of Sequential Monte Carlo Samplers: an Alternative to Particle Filters for Non-linear Non-Gaussian Sequential Inference with Zero Process Noise. Proc IET Data Fusion and Target Tracking Conference, 2012.(pdf).(IET.TV)
• 2011
• P Horridge and S Maskell. Using a Probabilistic Hypothesis Density Filter to Confirm Tracks in a Multi-target Environment. In Proc SDF 2011.(pdf)
• 2010
• D Nevell, S Maskell, P Horridge and H Barnett. Fusion of Data Sources with Different Levels of Trust. Proc Fusion 2010.(pdf)
• S Maskell, P Horridge, T Cooper and M Celand. Exchanging Multi-level Maps with Transformations to Support Multi-Modal Alignment. Proc SEAS DTC Conf 2010.(pdf)
• 2009
• S Maskell, P Horridge, D Nevell and T Cooper. Fast Multi-Level Maps and Modelling of Complex Transformations. Proc SEAS DTC Conf 2009.(pdf)
• G Price, V Calloway, S Maskell and K Morgan. Tools and Datapaths to Support Implementation of Divide-and-Conquer Algorithms within an FPGA Vector Co-processor Methodology. Proc EMRS DTC Conf 2009.(pdf)
• P Horridge and S Maskell. A scalable method of tracking targets with dependent distributions. Proc Fusion 2009.(pdf)
• P Horridge and S Maskell. Searching for, initiating and Tracking Multiple Targets Using Existence Probabilities. Proc Fusion 2009.(pdf)
• 2008
• T Cooper and S Maskell. Exchanging Uncertain Multi-Level Maps. In Proc SEAS DTC conference. 2008.(pdf)
• K Morgan and S Maskell. An FPGA Vector Co-Processing Core for Rapid Algorithm Development.In Proc EMRS DTC conference. 2008.(pdf)
• A Gning, L Mihaylova, S Maskell, S K Pang, S Godsill.Ground Target Group Structure and State Estimation with Particle Filtering. Proc. 11th International Conf. on Information Fusion, 2008.(pdf)
• A Gning, L Mihaylova, S Maskell, S K Pang, S Godsill. Evolving Networks for Group Object Motion Estimation. Proc. of the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, April 2008.(pdf)
• H Bhaskar, L Mihaylova, S Maskell. Population-based Particle Filtering. Proc. of the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, April 2008.(pdf).(IET.TV)
• P Horridge, S Maskell. Tracking with Inter-visibility Variables. Proc. of the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, April 2008.(pdf).(IET.TV)
• J Hill, S Maskell and M Cole. Using Ship Tracking Methods to Assist in Quality Controlling and Bias Adjusting Meteorological Observations in a Marine Environment. Proc. of the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, April 2008.(pdf)
• H Bhaskar, L Mihaylova, S Maskell. Human Body Parts Tracking Using Pictorial Structures and a Genetic Algorithm. Proc. of the IEEE International Conf. on Intelligent Systems, 6-8 Sept. 2008.(pdf)
• H Bhaskar, L Mihaylova, S Maskell. Multiple Body Part Tracking Using a Probabilistic Data Association Filter. NATO Symposium on "Sensors and Technology for Defence Against Terrorism", 22-25 April, 2008.(pdf)
• 2007
• J Hill, S Maskell, M Cole. Using Ship Tracks to Bias Adjust the Marine Air Temperature Record. Royal Meteorological Society Conference, 2007.(pdf)
• H Bhaskar, L Mihaylova, S Maskell. Automatic Target Detection Based on Background Modeling Using Adaptive Cluster Density Estimation. 3rd German Workshop on Sensor Data Fusion: Trends, Solutions, Applications 2007.(pdf)
• 2006
• S Maskell, B Alun-Jones and M Macleod. A Single Instruction Multiple Data Particle Filter. In Proceedings of Nonlinear Statistical Signal Processing Workshop 2006.(pdf)
• M Strens, J Baxter, M Hernandez, G Moon, S Kapetanakis and S Maskell. Autonomous Decision-Making for Sensor Allocation and Management. Moving Autonomy Forward Conference 2006.
• M Klaas, M Briers, N de Freitas, A Doucet, S Maskell and D Lang. Fast Particle Smoothing: If I Had a Million Particles. ICML 2006. (pdf)
• P Horridge and S Maskell. Real-Time Tracking Of Hundreds Of Targets With Efficient Exact JPDAF Implementation. Proceedings of Fusion 2006.(pdf)
• G Powell, D Marshall, P Smets, B Ristic, S Maskell. Joint Tracking and Classification of Airbourne Objects using Particle Filters and the Continuous Transferable Belief Model. Proceedings of Fusion 2006.(pdf)
• S Maskell, K Weekes and M Briers. Distributed Tracking of Stealthy Targets Using Particle Filters. 2006 IEE Seminar on Target Tracking: Algorithms and Applications.(pdf)
• M Briers, A Doucet, and S Maskell. Fixed-lag Sequential Monte Carlo Data Association. SPIE 2006.(pdf)
• 2005
• K Gilholm, S Godsill, S Maskell, and D Salmond. Poisson models for extended target and group tracking. Proc. SPIE 5913, 59130R (2005). (pdf)
• M Briers, S Maskell, S Reece, S Roberts, I Rezek, VD Dang, A Rogers, NR Jennings. Dynamic sensor coalition formation to assist the distributed tracking of targets: Application to wide-area surveillance. IEE Conference on Homeland Security, 2005. (pdf)
• J Vermaak, S Maskell, M Briers, and P Perez. Bayesian visual tracking with existence process. In proceedings of International Conference Image Processing, 2005.(pdf)
• J Vermaak, S Maskell and M Briers. A Unifying Framework for Multi-Target Tracking and Existence. In proceedings of Fusion 2005.(pdf)
• P Minvielle, A Marrs, S Maskell and A Doucet. Joint Target Tracking and Identification – Part I: Sequential Monte Carlo Model-Based Approaches. In proceedings of Fusion 2005.(pdf).
• P Minvielle, A Marrs, S Maskell and A Doucet. Joint Target Tracking and Identification – Part II: Shape video computing. In proceedings of Fusion 2005.(pdf).
• J Vermaak, S Maskell and M Briers. Online Sensor Registration. In proceedings of IEEE Aerospace Conference, 2005.(pdf)
• JMC Clark, S Maskell, R Vinter and M Yaqoob. A Comparison of the Particle and Shifted Rayleigh Filters in their Application to a Multisensor Bearings-only Problem. In proceedings of IEEE Aerospace Conference, 2005.(pdf)
• 2004
• M Rutten, N Gordon, and S Maskell. Particle-based Track-Before-Detect in Rayleigh Noise. Proceedings of SPIE Conference on Signal Processing of Small Targets, 2004. (pdf)
• M Rutten, S Maskell, M Briers, and N Gordon. Multi-path Track Association for Over-the-Horizon Radar Using Lagrangian Relaxation. Proceedings of SPIE Conference on Signal Processing of Small Targets, 2004. (pdf)
• S Maskell, N Gordon, N Everett, and M Robinson. Tracking Manoeuvring Targets Using a Scale Mixture of Normals. Proceedings of SPIE Conference on Signal Processing of Small Targets, 2004. (pdf)
• S Maskell, M Briers, and R Wright. Fast Mutual Exclusion. Proceedings of SPIE Conference on Signal Processing of Small Targets, 2004. (pdf)
• M Rutten, N Gordon, and S Maskell. Efficient Particle Based Track-Before-Detect in Rayleigh Noise. Proceedings of 7th International Conference on Information Fusion, 2004.(pdf)
• S Maskell, R Everitt, R Wright, and M Briers. Multi-target Out-of-Sequence Data Association. Proceedings of 7th International Conference on Information Fusion, 2004 (pdf).
• S Maskell, M Briers, and R Wright. Tracking Using a Radar and a Problem Specific Proposal Distribution in a Particle Filter. Proceedings of IEE Tracking Conference: Algorithms and Applications, 2004.(pdf)
• 2003
• M Briers, S Maskell, and R Wright. A Rao-Blackwellised Unscented Kalman Filter. In Proceedings of 6th International Conference on Information Fusion, 2003. (pdf)
• M Briers, S Maskell, and M Philpott. Two-dimensional Assignment with Merged Measurements using Lagrangian Relaxation. Proceedings of SPIE Conference on Signal Processing of Small Targets, pages 283-292, 2003.(pdf)
• 2002
• R Wright, S Maskell, M Briers, S Lycett. Robust Tracking of Stealthy Targets and Multi-Sensor Fusion. RAES Classified conference on Data Fusion, 2002.
• A Marrs, S Maskell, and Y Bar-Shalom. Expected Likelihood for Tracking in Clutter with Particle Filters. In O Drummond, editor, Proceedings of SPIE Conference on Signal Processing of Small Targets, pages 230-239, 2002.(pdf)
• S Maskell, N Gordon, M Rollason, and D Salmond. Efficient particle filtering for multiple target tracking with application to tracking in structured images. Proceedings of SPIE Conference on Signal Processing of Small Targets, pages 251-262, 2002.(pdf)
• X Lin, T Kirubarajan, Y Bar-Shalom, S Maskell. Comparison of EKF, Pseudo-measurement Filter and Particle Filter for a Bearings Only Tracking Problem. In Procedings of SPIE: Signal and Data Processing of Small Targets, 2002.(pdf)
• N Gordon, S Maskell, and T Kirubarajan. Efficient Particle Filters for Joint Tracking and Classification. In Procedings of SPIE: Signal and Data Processing of Small Targets, pages 439-449, 2002. (pdf)
• M Hernandez, A Marrs, N Gordon, S Maskell, and C Reed. Cramer-Rao Bounds for Nonlinear Filtering with Measurement Origin Uncertainty. Proceedings of 5th International Conference on Information Fusion, 2002. (pdf)
• M Hernandez, A Marrs, S Maskell, and M Orton. Tracking and Fusion for Wireless Sensor Networks. Proceedings of 5th International Conference on Information Fusion, 2002. (pdf)
• M Mallick, S Maskell, T Kirubarajan, N Gordon. Littoral Tracking using Particle Filter. In Proceedings of Fusion 2002. (pdf)
• S Maskell and N Gordon. A Tutorial on Particle Filters for On-line Nonlinear/Non-Gaussian Bayesian Tracking. In Proceedings of IEE Colloquium on Tracking, 2002 (pdf).

Media and Public Engagement

• Featured in Pioneering solutions for a better world: space engineering at the University of Liverpool. (YouTube)
• Featured in Why Planes Vanish: the Hunt for MH370, which aired on 6 March 2024 on BBC1 and was mentioned in an article in the Times on 6 March 2024, an article in the Telegraph on 8 March 2024, Radio 4's World Tonight on 5 March 2024, BBC1's Morning Live on 6 March 2024 and a University of Liverpool News item. I was also interviewed live on BBC News on 8 March 2024.
• COVID: how scientists can help tell if someone caught the virus at a nightclub. The Conversation. July 2021. (link)
• More Than Meets the Eye, Episode 3 of Future Horizons: The Tempest Podcast. May 2021. (podcast)
• I see data as a tool for making decisions: scientist Simon Maskell on tackling Covid-19. E&T Magazine. May 2020.(link)
• Data and AI: The future of healthcare. New Statesman. 2020. (link)
• Samples of the Past, the Present and an Uncertain Future. Inaugural Lecture. 2018. (video)
• Big Data and the Search for MH370. 2017. (Podcast)
• The Big Data Revolution. P4-5 of Realise Magazine. 2014.(magazine)
• Big Data in Healthcare. An executive briefing as part of the "Liverpool Big Data Collaborative for Health". 10 March 2014. (youtube)
• How Statistics can Help in the Mission to Find MH370. The Conversation. 27 March 2014.(link) This is a journalistically edited version of a University of Liverpool News Article.(link)
• Viewpoint: Big Data and the Budget. University of Liverpool News Article. 20 March 2014.(link)
• The Future of Cyber Security is in the Mind. University of Liverpool News Article. 3 March 2014.(link)
• How Can We Exploit the Opportunities of Big Data whilst Safeguarding the Interests of Citizens? 2013.(pdf)
• I was interviewed for People in Science, Technology, Engineering & Mathematics. The video is on display at the National Space Centre's multi-touch Table.(transcript)

Patents / Thesis / Freely Available (ie not internal to QinetiQ) Technical Reports / Other

• S Maskell, M Carter, S Lakra, L Devlin and A Varsi. Running Multiple Short MCMC Chains on a GPU Using JAX for Fast Inference with Stan. Presented at StanCon2024. (YouTube)
• E Kontsioti, S Maskell, M Pirmohamed, I Anderson. Identifying drug-drug interactions in spontaneous reports utilizing signal detection and biological plausibility aspects. (preprint)
• K Kim, S Maskell and J F Ralph. Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound. (ArXiV paper)
• S Maskell. Towards Using Large Scale Sequential Monte Carlo to Get Big Information out of Small Data. Plenary talk at SDF 2022.
• S Maskell. Control Variates: Post-processing samples to Significantly Reduce Monte-Carlo Variance. Plenary talk at MFI 2022.
• Ruth Hunter, Sarah Rodgers, Jeremy Hilton, Mike Clarke, Leandro Garcia, Catharine Ward Thompson, Rebecca Geary, Mark A. Green, Ciaran O'Neill, Alberto Longo, Rebecca Lovell, Alex Nurse, Benedict Wheeler, Sarah Clement, Ana Porroche-Escudero, Rich Mitchell, Ben Barr, John Barry, Sarah Bell, Dominic Bryan, Iain Buchan, Olly Butters, Tom Clemens, Natalie Clewley, Rhiannon Corcoran, Lewis Elliott, Geraint Ellis, Cornelia Guell, Anna Jurek-Loughrey, Frank Kee, Aideen Maguire, Simon Maskell, Brendan Murtagh, Grahame Smith, Timothy Taylor. GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health - a new initiative. Welcome Open Research (Pre-review Open Letter)
• A Varsi and S Maskell. Method of Parallel Implementation in Distributed Memory Architectures. August 2022. Patent ref: WO2022162386. (pdf)
• A Phillips, M Wright, I Riou, S Maddox, S Maskell and J F Ralph. Position fixing with cold atom gravity gradiometers. (arXiv preprint)
• S Maskell, A Varsi and P Spirakis. Fast Distributed Resampling and Putting More Emphasis on Modelling in Multi-target Tracking. Presentation at UDRC Theme meeting on Multi-target Tracking and Decentralised Processing. 14 January 2022. (video)
• E Kontsioti, S Maskell, A Bensalem, B Dutta, M Pirmohamed. Similarity and Consistency Assessment of Three Major Online Drug-Drug Interaction Resources. (Authorea preprint)
• S Maskell. Particles 2.0: Non-linear Non-Gaussian Inference for 2021. Plenary talk at Fusion 2021.
• L Devlin, P Horridge, P L Green and S Maskell. The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel. (arXiv preprint)
• E Kontsioti, S Maskell and M Pirmohamed. Design criteria for reference sets in pharmacovigilance - The case of drug-drug interactions. Accepted for poster presentation at 2021 Global Observational Health Data Sciences and Informatics (OHDSI) Symposium, 2021.(poster)(abstract)
• J Wu, L Wen, P L Green, J Li and S Maskell. Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference. (arXiv preprint)
• K Kim, S Maskell and S Park. Evaluating the Strong Scalability of Parallel Markov-Chain Monte Carlo Algorithms. (pdf)
• S Maskell and A Varsi. Towards an Interface for Streaming Stan. Presented at StanCon 2020. (YouTube).
• S Jenkins, M Bull, S Abbs, S Maskell and P Horridge. Biosurveillance and Data Fusion for Early Outbreak Detection and Classification. Presented at DTRA CBD S&T 2019. (abstract)
• A Varsi, L Kekempanos, J Thiyagalingam and S Maskell. A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler. (arXiv preprint)
• P Thomas, J Barr, S Hiscocks, C England, S Maskell, B Balaji, and J Williams. Stone Soup: An Open-Source Framework for Tracking and State Estimation. In ISIF Perpspectives Magazine. 2017. (pdf)
• S Maskell, R Sloane, S Perkins, J Heap, J Hajne, A Jones and M Pirmohamed. Estimating the Pertinent Information Present in Social Media, not just what an Algorithm Detects. Presented at ISOP 2017.
• S Maskell, R Sloane, J Hajne, J Heap, S Perkins, E Griffith, A Jones and M Pirmohamed. Looking Longitudinally in Twitter: Reading More than 140 Characters. Presented at ISOP 2017.
• Particle Filters - Learning from the Past, Tracking the Present and Predicting the Future. School of ICASSP presentation, 2015.(video)
• The Ubiquitous Utility of the General Linear Model and Monte-Carlo Methods. Talk at Bill Fitzgerald's Memorial Day, 2015. (video)
• Associate editor for IEEE Transacations of Aerospace and Electronic Systems as well as for IEEE Signal Processing Letters.
• S Maher, S Maskell, S Syed and Stephen Taylor. Method and Apparatus for Determining a Composition of a Spectrum. November 2015. Patent Reference: WO2017081454A1.(pdf)
• S Maskell, F de Melo and F Daum. MOP: Particles without Resampling. Invited Talk at Isaac Newton Institute event on Monte Carlo Inference for Complex Statistical Models. 15 May 2014. (video)
• Invited Discussant: C Andrieu, A Doucet, and R Holenstein. Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology). Volume 72, Number 3, pp 269-342, 2010.(pdf)
• Co-chair for International Conference on Information Fusion 2018, Fusion 2018, and General chair for Fusion 2010 (conference website), where I organised a plenary session with John Lavery (from the US Army Research Organisation, ARO): the uncertainty forum.(IET.TV)
• M Briers, A Doucet, and S Maskell. Smoothing Algorithms for State-Space Models. Cambridge University Engineering Department Technical Report, CUED/F-INFENG/TR.498, August 2004. (pdf)
• M Cusack and S Maskell. Particle filters for location-aware services. 2003. (pdf)
• S Maskell. Signal Processing with Reduced Combinatorial Complexity. July 2003. Patent Reference:0315349.1. An EPL licenced version is available for download on github with associated documentation on read the docs. (pdf)
• S Maskell. Sequentially Structured Bayesian Solutions. PhD thesis, Cambridge University Engineering Department, 2004. Chapters can be briefly described as:
  • A review of the tracking literature with a few new extensions.
  • An algorithm for the difficult tracking problem of joint tracking and classification of targets using semi-Markov models.
  • An approach for deriving the models needed for tracking algorithms from SDEs.
  • An efficient (patented) method for exploiting an imposed ordering of multiple targets to improve the efficiency of algorithms such as the JPDAF.
  • A technique for exploiting tracking algorithms in inference in markov meshes and so the analysis of images.
• S Maskell, M Orton, and N Gordon. Efficient Inference for Conditionally Gaussian Markov Random Fields. Technical report, Cambridge University Engineering Department, 2002.
• S Maskell. Multi-Sensor Management. First Year PhD Report. Technical Report, Cambridge University Engineering Department, June 2001.

Fun Stuff

• Red-Riding Hood
• Image Search
• Parkour (aka `Free-running')
• Mobius Gear
• Rob Carter
• Move
• Scale