Professor Akeel Shah graduated with a PhD in Applied Mathematics from University of Manchester Institute of Science and Technology in 2001. He is currently a Professor in the School of Energy and Power Engineering at the University of Chongqing, with expertise in electrochemical energy conversion, computational engineering and applied machine learning. He previously held positions at University of Southampton and University of Warwick. His work is primarily focused on the modelling and simulation of energy-conversion devices (flow batteries, metal-air batteries, organic/inorganic fuel cells), including computational modelling, and the development of fast algorithms for computer codes in science and engineering based on machine learning and computational statistics. Between 2004 and 2006, he held a joint Pacific Institute of Mathematics Sciences (PIMS) and Mathematics of Information Technology and Complex Systems (MITACS) Fellowship. He is the author of over 70 publications in leading, international peer-reviewed journals.
1. Fuel cells and energy storage, especially hydrogen fuel cells and redox flow batteries, with an emphasis on the development of new systems and computational modelling
2. Computational models for science and engineering with applications to energy technologies
3. Machine learning and emulation, with an emphasis on high-dimensional problems related to input and output spaces
Masters and PhD candidates interested in any of these or related topics are encouraged to contact me for discussions.
1. Fuel cells and energy storage, especially hydrogen fuel cells and redox flow batteries, with an emphasis on the development of new systems and computational modelling
2. Computational models for science and engineering with applications to energy technologies
3. Machine learning and emulation, with an emphasis on high-dimensional problems related to input and output spaces
Masters and PhD candidates interested in any of these or related topics are encouraged to contact me for discussions.
1. Fuel cells and energy storage, especially hydrogen fuel cells and redox flow batteries, with an emphasis on the development of new systems and computational modelling
2. Computational models for science and engineering with applications to energy technologies
3. Machine learning and emulation, with an emphasis on high-dimensional problems related to input and output spaces
Masters and PhD candidates interested in any of these or related topics are encouraged to contact me for discussions.
1. Fuel cells and energy storage, especially hydrogen fuel cells and redox flow batteries, with an emphasis on the development of new systems and computational modelling
2. Computational models for science and engineering with applications to energy technologies
3. Machine learning and emulation, with an emphasis on high-dimensional problems related to input and output spaces
Masters and PhD candidates interested in any of these or related topic...
Machine Learning for Engineers, Graduate course
Numerical Methods for Engineers, Undergraduate course
Introduction to Machine Learning, Summer School
(1) Sponsor: EPSRC (awarded 08/2016; Warwick share of a consortium bid led by University of Exeter)
Title: Zinc-Nickel Redox Flow Battery for Energy Storage (SUPERGEN Energy Storage Grand Challenge) EP/P003494/1
Funds: £298,000 UOW share of £1.049m total,
Period: 2016–2020
Partners: Dr Xiaohong Li (PI, Exeter), Prof. Nigel Brandon (Imperial), Prof Tapas Mallick (Exeter)
(2) Sponsor: EPSRC (awarded 09/2016)
Title: Surrogate Assisted Approaches For Fuel Cell And Battery Models (Overseas Travel Grant) EP/P012620/1
Funds: £82,500
Period: 2016–2017
(3) Sponsor: EPSRC
Title: Real-Time H2 Purification and Monitoring for Efficient and Durable Fuel Cell Vehicles (SUPERGEN Hydrogen Fuel Cells Challenge) EP/L018330/1
Funds: £568,000 share of £1.257m total,
Period: 2014–2018
Partners: University College London (Prof Xiao Guo, UCL (PI))
(4) Sponsor: European Union FP7
Title: ‘NECOBAUT’: New Concept of Metal-Air Battery for Automotive Application based on Advanced Nanomaterials (Grant No. 314159)
Funds: EUR 199,000 share of EUR 2.12m total
Period: 2012–2016
Partners: Fundacion Tecnalia Research & Innovation, University of Southampton, Con- siglio Nazionale delle Ricerche, Institut National de l’environnement Industriel et des Risques, Tecnicas Reunidas SA, Timcal SA, Saft Baterias SL
(5) Sponsor: Technology Strategy Board/Department of Energy and Climate Change
Title: Improvements to Soluble Lead Redox Flow Battery Components
Funds: £204,000 share of £500,500k total
Period: 2013–2015
Partners: C-Tech Innovation Ltd, E.On UK, University of Southampton.
(6) Sponsor: Technology Strategy Board (TSB)
Title: Build and test the world’s first 10kW membrane-based fuel cell backup power system incorporating a novel platinum-free liquid regenerating cathode (‘ApPLES’). TSB Grant TP/BH039G 100773.
Funds: £128,000 share £974,000 total,
Period: 200...
1. W. Sun, Y. Zheng, K. Yang, Q. Zhang, A.A. Shah, Z. Wu, Y. Sun, L. Feng, D. Chen, Z. Xiao, S. Lu, Y. Li, K. Sun* (2019), Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials
Science advances 5 (11), eaay4275
2. C. Gadd, W.W. Xing, M. Mousavi Nezhad, A.A. Shah* (2019), A surrogate modelling approach based on nonlinear dimension reduction for uncertainty quantification in ground- water flow models, Transport in Porous Media, 126(1), 39-77
3. J. Xu, Q. Ma, H. Su, F. Qiao, P. Leung, A. Shah, Q. Xu* (2019), Redox characteristics of iron ions in different deep eutectic solvents, Ionics, 1-10
4. D. Crevillén-García, P.K. Leung, A. Rodchanarowan, A.A. Shah* (2019), Uncertainty quantification for flow and transport in highly heterogeneous porous media based on simultaneous stochastic model dimensionality reduction, Transport in porous media 126 (1), 79-95
5. D.Trudgeon, K. Qiu, O. Taiwo, B. Chakrabarti, V. Yufit, D. Crevillen-Garcia, A.A. Shah, T. Mallick, N. Brandon, X. Li* (2019), Screening of effective electrolyte additives for zinc- based redox flow battery systems, Journal of Power Sources 412, 44-54
6. Q. Xu*, L.Y. Qin, Y.N. Ji, P.K. Leung, H.N. Su, F. Qiao, W.W. Yang, A.A. Shah, H.M. Li, (2019) A deep eutectic solvent (DES) electrolyte-based vanadium-iron redox flow battery enabling higher specific capacity and improved thermal stability, Electrochimica Acta 293, 426-431
7. C. Gadd*, S. Wade, A.A. Shah, D. Grammatopoulos (2018), Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models, http://arxiv.org/abs/ 1803.10746
8. Q Xu*, Y. Ji, L. Qin, P.K. Leung, A.A. Shah, H. Su, H. Li (2018), Influence of carbon dioxide additive on the characteristics of a deep eutectic solvent (DES) electrolyte for non- aqueous redox flow batteries, Chemical Physics Letters, 708, 48-53, https://doi.org/ 10.1016/j.cplett.2018.07.060
9. D. Crevillen-Garcia, P.K. Leung and A.A. Shah* (2018), An
