Prof. Peter Grindrod CBE
Founding Trustee of The Alan Turing Institute
Chairman of GTTA Limited
Chairman of Hare Analytics Limited
Ex member of MOD DSAC, EPSRC Council and BBSRC Council
President of the IMA 2006-7
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Founding Trustee of The Alan Turing Institute; Chairman and founder of GTTA Limited; Chairman and founder of Hare Analytics Ltd; Director and founder of CoutingLab Ltd; Founder and former Director of Numbercraft Ltd. Former Independent member of MOD Defence Scientific Advisory Council. Former member of EPSRC Council. Former member of BBSRC Council. Former member for RCUK Digital Economy programme advisory board.
Former President of the Instutute of Mathematics and its Applications
Peter Grindrod (2022), There is something it is like to be me, https://www.researchgate.net/publication/359716177_There_is_something_t…
Alexandre Bovet and Peter Grindrod (2022) Structure and evolution of the UK far-right network on Telegram.
Clive E. Bowman and Peter Grindrod (2022), Desperately seeking something, https://www.researchgate.net/publication/361142547_Desperately_Searchin…
Lyuba V. Bozhilova, Peter Grindrod, Gesine Reinert, and Tadas Temcinas (2022), Principles and Consequences of Evolutionary ’Omic Development: a Topological Perspective, https://www.researchgate.net/publication/361212687_Principles_and_Conse…
Peter Grindrod and Martin Brennan (2022), Cortex-Like Systems via Range-Dependent Networks of Phase-Resetting k-Dimensional Clocks, https://www.researchgate.net/publication/359202653_Cortex-Like_Systems_…
Peter Grindrod (2022), Scaling laws for properties of random graphs that grow via successive combination, https://www.researchgate.net/publication/359013394_Scaling_laws_for_pro…
Peter Grindrod (2021), Unconvetional AI, https://www.researchgate.net/publication/351840329_Unconventional_AI
Peter Grindrod (2021), Research Strategy and Strategic Options for Mathematics Departments, https://www.researchgate.net/publication/352093015_Research_Strategy_an…
Alain Goriely and Peter Grindrod (2021), Lessons to be Learned from the Covid-19 Experience in the UK https://www.researchgate.net/publication/349645530_Lessons_to_be_Learne…
Peter Grindrod (2020), On MOD AI and Data Science, https://www.researchgate.net/publication/343416359_On_MOD_AI_and_Data_S…
Peter Grindrod (2020), Rigorously Rethinking Data, https://www.researchgate.net/publication/341179399_Rigorously_Rethinkin… .
Peter Grindrod (2020), Lock-Down Decompression: Opportunities for management and differential control, https://www.researchgate.net/publication/341113709_Lock-Down_Decompress… .
Clive E. Bowman, Robert C. Brown, Peter Grindrod, Heather Wardle (2020), Online Gambling: Hidden Markov Models for Behavioural Changes, https://www.researchgate.net/publication/341271000_Online_Gambling_Hidd… .
Clive E. Bowman and Peter Grindrod (2019), Kinship, familial searching and biometrics, https://www.researchgate.net/publication/333982366_Kinship_Familial_Sea…
Clive E. Bowman and Peter Grindrod (2019), Trust Limitation, Conflation and Hype, https://www.researchgate.net/publication/334425107_Trust_Limitation_Con…
Peter Grindrod, Leading Within Digital Worlds, (2020) Emreald Points, Emerald Publishing Limited (18 Mar. 2020)
Peter Grindrod, Mathematical Underpinnings of Analytics, (2014) OUP, Oxford. ISBN: 9780198725091.
Peter Grindrod, Patterns and Waves, The Theory and Applications of Reaaction Diffusion Equations, OUP, Oxford.
Chapters in a books
P. Grindrod, (2015) Evolving social networks, attitudes and beliefs and counter terrorism. In: The Princeton Companion to Applied Mathematics. Edited by Nicholas J. Higham; Mark R. Dennis, Paul Glendinning, Paul A. Martin, Fadil Santosa & Jared Tanner, associate editors, 2015.
P. Grindrod, D.J. Higham and P. Laflin, (2016) The Graph Whisperers, UK Success Stories in Industrial Mathematics. Edited by P.J. Aston, A.J. Mulholland, K.M.M. Tant, Springer. 2016.
Most Papers (since 2010)
P. Grindrod Peter, C. Lester (2021), Cortex-Like Complex Systems: What Occurs Within? , Frontiers in Applied Mathematics and Statistics, 7, p 51 https://www.frontiersin.org/article/10.3389/fams.2021.627236
C. Singleton, P. Grindrod (2021), Forecasting for Battery Storage: Choosing the Error Metric. Energies 2021,14,6274. https://www.mdpi.com/1996-1073/14/19/6274/pdf
P. Grindrod, On human consciousness: A mathematical perspective, Network Neuroscience issue 1 volume 2 page 23-40 (January 2018)
P. Grindrod, D.J. Higham, High Modularity Creates Scaling Laws, Scientific reports issue 1 volume 8 page 9737-(27 June 2018)
P. Grindrod, D.J. Higham, and V. Noferini (2017) The deformed graph Laplacian and its applications to network centrality analysis, accepted to appear, SIAM Journal on Matrix Analysis and Applications SIAM Journal on Matrix Analysis and Applications, issue 1 volume 39 page 310-341(2018)
F. Arrigo, P. Grindrod, D.J. Higham, and V. Noferini, (2018) Non-backtracking walk centrality for directed networks, Journal of Complex Networks issue 1 volume 6page 54-78 (1 February 2018)
P. Grindrod and T.E. Lee, (2017) On strongly connected networks with excitable-refractory dynamics and delayed coupling, Royal Society Open Science, 5th April 2017. http://rsos.royalsocietypublishing.org/content/4/4/160912
P. Grindrod (2016) Beyond Privacy and Exposure: Ethical Issues within Citizen-Facing Analytics, Roy. Soc. Phil. Trans. A, Published 14 November 2016.DOI: 10.1098/rsta.2016.0132 http://rsta.royalsocietypublishing.org/content/374/2083/20160132
P. Grindrod and T.E. Lee, (2016) Comparison of social structures within cities of very different sizes, Royal Society Open Science, 24 February 2016.DOI: 10.1098/rsos.150526 http://rsos.royalsocietypublishing.org/content/3/2/150526
P. Grindrod, D.J. Higham, P. Laflin, A. Otley, J.A. Ward, (2016) Inverse network sampling to explore on-line brand allegianvce, European Journal of Applied Mathematics, CJO 2016 doi:10.1017/S0956792516000085
P. Grindrod and E.L. Patel, (2015) Phase locking to the n-torus, IMA Journal of Applied Mathematics 10/2015; DOI: 10.1093/imamat/hxv031.
S. Haben, C.P. Singleton and P. Grindrod, (2015) Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data, IEEE TRANSACTIONS ON SMART GRID, JANUARY 2015, DOI: 10.1109/TSG.2015.2409786.
Grindrod, P. and Higham, D.J., (2014) A dynamical systems view of network centrality. Proc. R. Soc. A 470: 20130835. http://dx.doi.org/10.1098/rspa.2013.0835.
Ward, J.A and Grindrod, P. (2014) Aperiodic dynamics in a deterministic adaptive network model of attitude formation in social groups Physica D: Nonlinear Phenomena Volume 282, 15 July 2014, Pages 27–33.
Haben, S., Ward, J., Greetham, D., Grindrod, P. and Singleton, C. (2014) A new error measure for forecasts of household-level, high resolution electrical energy consumption. International Journal of Forecasting, 30, pp. 246--256, 2014.
D.V. Greetham , Z.Stoyanov, P.Grindrod (2014) On the radius of centrality in evolving communication networks, Journal of Combinatorial Optimization 10/2014; 28(3).
Grindrod, P. and Higham, D. J. (2013) A matrix iteration for dynamic network summaries. SIAM Review, 55, 2013, 118--128.
Laflin, P, Mantzaris, AV, Ainley, F, Otley, A, Grindrod, P & Higham, D (2013), Discovering and validating influence in a dynamic online social network, Social Network Analysis and Mining, vol 3, no. 4, pp. 1311-1323., 10.1007/s13278-013-0143-7.
P. Grindrod, Z.V. Stoyanov, G.M. Smith, J.D. Saddy, (2013) Primary evolving networks and the comparative analysis of robust and fragile structures, Journal of Complex Networks, doi: 10.1093/comnet/cnt015, 2013.
Parsons, M. C. and Grindrod, P. (2012) Competing edge networks, PHYSICS LETTERS A 376(32):2167–2173, ISSN 0375-9601 (2012).
Grindrod, P., Higham, D. J. and Parsons, M. C. (2011), Bistability through triadic closure. Internet Mathematics, 8, 2012, 402--423.
Grindrod, P. and Higham, D. J. (2012) Models for evolving networks: with applications in telecommunication and online activities. IMA J of Management Maths, 23.
Grindrod, P. and Parsons, M. (2011) Social networks: evolving graphs with memory dependent edges. Physica A, 390, 2. pp. 3970-3981.
Grindrod, P., Parsons, M. C., Higham, D. J. and Estrada, E. (2011) Communicability across evolving networks. Physical Review E, 83 (4). 046120. ISSN 1539-3755.
Grindrod, P. (2011) Mathematical modelling for the digital society. IMA Journal of Applied Mathematics, 76 (3). pp. 475-492.
Grindrod, P. and Pinotsis, D. (2011) On the spectra of certain integro-differential-delay problems with applications in neurodynamics., Physica D, 240 (1), pp13-20.
Grindrod, P. and Higham, D. J. (2010) Evolving graphs: dynamical models, inverse problems and propagation. Proceedings of the Royal Society A, 466 (2115).
Grindrod, P., Higham, D. J. and Kalna, G. (2010) Periodic Reordering. IMA Journal of Numerical Analysis, 30 (1). pp. 195-207.
Grindrod, P. and Higham, D. (2010) Models for Evolving Networks: with Applications in Telecommunication and Online Activities. IMA J of Management Maths.
N.Otter, M.A. Porter, U.Tillmann, P. Grindrod, H.A. Harrington, (2015) A roadmap for the computation of persistent homology, arXiv:1506.08903.
Peter Grindrod, Desmond J. Higham, Robert S. MacKay, (2014), Opportunities at the Mathematics/ Future Cities Interface arXiv:1409.1831. 
D.J. Higham, P.Grindrod, A.V. Mantzaris, A. Otley, P. Laflin, (2014) Anticipating Activity in Social Media Spikes, arXiv:1406.2017v1
G. Smith, Z. Stoyanov, D.V. Greetham, P. Grindrod, J.D. Saddy, (2014) Towards the Computer-aided Diagnosis of Dementia based on the Geometric and Network Connectivity of Structural MRI Data, MICCAI 2014 workshop Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data; 09/2014.
S. Haben, M. Rowe, D.V. Greetham, P. Grindrod, W. Holderbaum, B. Potter, C. Singleton, (2013) Mathematical solutions for electricity networks in a low carbon future, Electricity Distribution (CIRED 2013), 22nd International Conference and Exhibition.
P. Laflin, A.V. Mantzaris, F. Ainley, A. Otley, P. Grindrod, D.J. Higham, (2012) Dynamic targeting in an online social medium, Proceedings of the 4th international conference on Social Informatics; 12/2012.
Laflin, P., Mantzaris, A.V., Higham, D.J., Grindrod, P., Ainley, F. and Otley, A., 2012. Twitter’s big hitters. In Proceedings of 2012 Digital Economy All Hands Conference, Aberdeen, Scotland.
Cortex-like comnplex systems: what occurs within? https://vimeo.com/500916728
Leading within Digital worlds. https://www.youtube.com/watch?v=6gv9B0dSpKU&t=1987s
Urban Analytics https://www.youtube.com/watch?v=xAhzQQ5Hc0A
Why get into Data Science? Data Science at work https://www.youtube.com/watch?v=mH4mjF9ONy8
"The Mathematical Underpinnings of Analytics" OUP (2015) see video at http://www.amazon.com/Peter-Grindrod/e/B001KD6IK2/ref=dp_byline_cont_bo… The book is aimed at final year and graduate students as well as early career professionals within analytics teams.
The theory and applications of dynamically evolving networks, including nonlinear node-based dynamics, fully coupled through time dependent network dynamics. Stochastic modelling and classification of behaviour within evolving peer-to-peer communication and social networks. Memory dependent network dynamics. Generalisations of centrality to continuous time networks.
Applications of mathematics to social media, digital media and marketing, and the digital economy. Design of algorithms that run in real time over vast peer-to-peer networks. Applications of mathematics to the emergence of social norms and attitudes.
Dynamical systems and Delay Differential Equations. Theory and appications of semilinear parabolic systems. Non-Fickian dispersion.
Analysis of fMRI scans of human brains, including measures of network "fragility" as predictors of future performance and early cognitive degradation. The human brain as a complex information processing system: implications for novel computing paradigms.
Modelling, analysis and forecasting of domestic and small business energy consumption on low voltage networks including dynamic behaviour driven segmentations of consumers via smart meter data; novel methods of forecasting peaks in demand; and future scenarios for energy use and uptake to technologies. Probabilistic forecasting of spiky timeseries.
Inference and forecasting problems for the retail, consumer goods, and telecommunications sectors. Behaviour-based risk measures and targetted-marketing applications.
Models for counter terrorism and real time recognition of anomalies within vast communications data sets.
Strategy for investment in science and technology research and innovation. Knowledge exchange and balancing open public research with confidential commercial interests through open innovation.