forecast electricity, finite mixture model, clustering, ecology, Bayes, survival analysis, species distribution model, Kahan's method, neuroscience, fMRI scans, evolving networks, directed networks, adaptive mesh, finite differences, finite elements, tumour growth, ground water flow, diffusion, porous media, semi-implicit time-stepping schemes
+44 1865 611511
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
On strongly connected networks with excitable-refractory dynamics and delayed coupling
Royal Society Open Science issue 4 volume 4 (1 April 2017)
Estimating the extent and structure of trade in horticultural orchids via social media.
Conservation biology : the journal of the Society for Conservation Biology issue 5 volume 30 page 1038-1047 (October 2016)
Medium term forecasts of domestic electricity use
T oky o In t e r n at i o n a l C o n fe r e n c e on E n gi n e e r i n g an d A p pl i e d S c i e n c e s ( E AS 2 0 1 6) I S B N : 9 78 - 9 8 6- 9 3 4 2 1- 2 - 4 (14 August 2016)
Extinct or still out there? Disentangling influences on extinction and rediscovery helps to clarify the fate of species on the edge.
Global change biology (11 July 2016)
Dehorning rhinos as a theoretical game
Ecological Modelling (24 June 2016)
- Correctly predicting the electricity use of households and small to medium enterprises (SMEs) is vital to ensure that customers reliably receive safe electricity. However, forecasting electricity usage is becoming even more challenging due to low carbon technologies such as electric vehicles, heatpumps and photovoltaics. The project aims to develop methods to anticipate, forecast and reduce peak-demand using smart meter data, tariff incentives and energy storage. This work will contribute to planning and real-time management of the energy distribution in the electricity network. This research is funded by Ofgem's low carbon network fund (LCNF) project the 'New Thames Valley Vision', led by the distribution network operator (DNO) Scottish and Southern Energy power Distribution.
A three minute video detailing the project is available here.
- Mathematical ecology, such as inferring when a species is extinct based upon the sighting record, or predicting which 'extinct' species are likely to be rediscovered based upon their traits and demographics. Please see http://tamsinleeresearch.wordpress.com/ for further details.
Lectures on graph theory, networks, Bayesian, MCMC, and algorithm analysis. Short industry focused projects.
First year PhD students.
March 2012 - October 2012: Tutor at Monash University.
Modules included Partial Differential Equations, Advanced Ordinary Differential Equations (third years), Advanced Engineering Mathematics (second years) and Functions and their applications (first years)
June 2006 - October 2011: Teacher at Reading Prison and Young Offenders Institute.
Preparing and teaching various classes, remedial mathematics, English, music, art and healthy living.
Working with young offenders requires diplomacy, and remaining calm under challenging circumstances.
October 2006 - July 2011: Teaching assistant
Topics taught include Matlab tutorials, Maple tutorials, Mathematics for Computer Scientists, Vectors and matrices, Algebra, Linear Algebra, Calculus Methods and Analysis.
October 2006 - July 2011: Private tutorials.
The level ranged from remedial mathematics to second year undergraduate meteorology, and tutees ranged from high school students to mature students. I would teach two or three undergraduates at a time.
Major / Recent Publications:
Grindrod, P., & Lee, T. E. (2016).
On strongly connected networks with excitable-refractory dynamics and delayed coupling. Submitted. Preprint available here.
Grindrod, P., & Lee, T. E. (2016). Comparison of social structures within cities of very different sizes. Open Science, 3(2), 150526. Press release here.
Lee, T. E., Fisher, D., Blomberg, S. & Wintle, B. (2016). Extinct or still out there? Disentangling influences on extinction and rediscovery helps to clarify the fate of species on the edge. Global change biology. DOI:10.1111/gcb.13421. Press release here.
Lee, T. E., Haben, S. A. & Grindrod, P. Modelling the weekly electricity consumption of small to medium enterprises. Mathematics in Industry. In press Here.
Hinsley, A., Lee, T. E., Harrison, J. R., & Roberts, D. L. (2016). Estimating the extent and structure of trade in horticultural orchids via social media. Conservation Biology.
Lee, T. E., Black, S. A., Fellous, A., Yamaguchi, N., Angelici, F., Al Hikmani, H., Reed, J. M., Elphick, C. S & Roberts, D. L. (2015). “Assessing uncertainty in sighting records:
an example of the Barbary lion (No. e1283). PeerJ PrePrints.
Poghosyan, A., Greetham, D. V., Haben, S., & Lee, T. E. (2015). Long term individual load forecast under different electrical vehicles uptake scenarios. Applied Energy, 157, 699-709. Here.
Thompson, C. J., Lee, T. E., & McCarthy, M. A. (2014). Species distributions and area relationships. Journal of theoretical biology, 363, 129-133. Here.
Clements, C., Lee, T. E., & McCarthy, M. A. (2014). An experimental test of a Bayesian method for inferring extinction with varying search efforts (No. e466v1). PeerJ PrePrints. Here.
Lee, T. E., Baines, M. J. & Langdon, S. A moving mesh approach for modelling time dependent partial differential equations. Applied Numerical Mathematics. Here.
Lee, T. E. (2014). A simple numerical tool to infer whether a species is extinct. Methods in Ecology and Evolution, 5(8), 791-796. Here.
Baines, M. J. & Lee, T. E. (2014). A large time-step implict moving mesh scheme for moving boundary problems. Numerical Methods for Partial Differential Equations, 30(1), 321-338. Here.
Thompson, C. J., Lee, T. E., Stone, L., Burgman, M. A. & McCarthy, M. (2013). Inferring extinction risks from sighting records. Journal of Theoretical Biology, 338, 16-22. Here.
Lee, T. E., McCarthy, M. A., Wintle, B., Bode, M., Roberts, D. L. & Burgman, M. A. (2013). Inferring extinctions from sighting records of variable reliability. Journal of Applied Ecology, 51 251-258. Here.
Lee, T. E., Baines, M. J., Langdon, S., & Tindall, M. J. (2013). A moving mesh approach for modelling avascular tumour growth. Applied Numerical Mathematics, 72, 99-114. Here.