Professor Tim Green, FREng, is a professor of power engineering in the Department of Electrical and Electronic Engineering at Imperial College London. He is also the Academic Leader for Sustainability at the university. Green graduated with a BSc, MSc, and PhD, and is a fellow of the Royal Academy of Engineering, the Institute of Electrical and Electronics Engineers, the Institution of Engineering and Technology, and the Chinese Society for Electrical Engineering.
Green's research focuses on the analysis and technology required to develop a zero-carbon electricity supply system that is dominated by variable renewable sources and other inverter-based resources (IBR) such as battery energy storage. He aims to address the challenges posed by the absence of traditional synchronous generators in ensuring the regulation of frequency and voltage, fault detection and location, and the damping of unstable modes. Green's work in this area involves the development of data-led models for assessing whole-system stability, as opposed to traditional physics-led models, due to the opaqueness of the models of IBR control software available from vendors.
In the past, Green contributed power electronics circuit and converter designs for HVDC that reduce losses while also providing control functions to assist integration into AC systems. He also pioneered the use of so-called soft open points in distribution networks. Green's work has been supported by EPSRC, Hitachi Energy, and National Grid ESO, and he collaborates with ESIG and G-PST.
Professor Tim Green, FREng, is a professor of power engineering at Imperial College London. He is also the Academic Leader for Sustainability at the university, where he works to make the university's activities more sustainable.
Green's research focuses on the analysis and technology required to develop a zero-carbon electricity supply system that is dominated by variable renewable sources and other inverter-based resources (IBR) such as battery energy storage. He aims to address the challenges posed by such a system, including regulation of frequency and voltage, detection and location of faults, and damping of unstable modes.
Professor Tim Green, FREng, is a professor of power engineering at Imperial College London's Faculty of Engineering, Department of Electrical and Electronic Engineering. He also serves as the Academic Leader for Sustainability at the university. Green graduated with a BSc, MSc, and PhD, and is a fellow of the Royal Academy of Engineering, the Institute of Electrical and Electronics Engineers, the Institution of Engineering and Technology, and the Chinese Society for Electrical Engineering.
Green's research focuses on the analysis and technology required to develop a zero-carbon electricity supply system that is dominated by variable renewable sources and inverter-based resources (IBR). He aims to address the challenges posed by the absence of traditional synchronous generators in ensuring the regulation of frequency and voltage, fault detection and location, and the damping of unstable modes. Green's work in this area involves developing data-led models for assessing whole-system stability, as opposed to traditional physics-led models, due to the opaqueness of the IBR control software available from vendors.
Previously, Green contributed power electronics circuit and converter designs for HVDC, reducing losses and providing control functions for integration into AC systems. He also pioneered the use of "soft open points" in distribution networks. Green's work has been supported by EPSRC, Hitachi Energy, and National Grid ESO, and he collaborates with ESIG and G-PST.
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Tim Green
Google DeepMind
http://tfgg.me/
Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … nature 596 (7873), 583-589, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:AzKEL7Gb_04C
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models M Varadi, S Anyango, M Deshpande, S Nair, C Natassia, G Yordanova, … Nucleic acids research 50 (D1), D439-D444, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:hvmnpdAuIbkC
The kinetics human action video dataset W Kay, J Carreira, K Simonyan, B Zhang, C Hillier, S Vijayanarasimhan, … arXiv preprint arXiv:1705.06950, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:RJOyoaXV5v8C
Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, … Nature 577 (7792), 706-710, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:T_ojBgVMvoEC
Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, … Nature 596 (7873), 590-596, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:H_jBuBxbQIAC
Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, … biorxiv, 2021.10. 04.463034, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:JP7YXuLIOvAC
Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, … arXiv preprint arXiv:2312.11805, 2023 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:nPT8s1NX_-sC
Human-level performance in 3D multiplayer games with population-based reinforcement learning M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, … Science 364 (6443), 859-865, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:tHtfpZlB6tUC
Population based training of neural networks M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, … arXiv preprint arXiv:1711.09846, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:C33y2ycGS3YC
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, … Proteins: structure, function, and bioinformatics 87 (12), 1141-1148, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:SAZ1SQo2q1kC
Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:NMlhSUseqAsC
High accuracy protein structure prediction using deep learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, … Fourteenth critical assessment of techniques for protein structure …, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:-yGd096yOn8C
Accurate structure prediction of biomolecular interactions with AlphaFold 3 J Abramson, J Adler, J Dunger, R Evans, T Green, A Pritzel, … Nature, 1-3, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:jtI9f0ekYq0C
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, … arXiv preprint arXiv:2403.05530, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:JWITY9-sCbMC
De novo structure prediction with deeplearning based scoring R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, A Zidek, … Annu Rev Biochem 77 (363-382), 6, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:D_tqNUsBuKoC
Elucidation of the Al/Si ordering in Gehlenite Ca2Al2SiO7 by combined 29Si and 27Al NMR spectroscopy/quantum chemical calculations P Florian, E Véron, T Green, JR Yates, D Massiot Chemistry of Materials, 2012 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:zdjWy_NXXwUC
Visualization and processing of computed solid-state NMR parameters: MagresView and MagresPython S Sturniolo, TFG Green, RM Hanson, M Zilka, K Refson, P Hodgkinson, … Solid state nuclear magnetic resonance 78, 64-70, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:eGYfIraVYiQC
AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences M Varadi, D Bertoni, P Magana, U Paramval, I Pidruchna, … Nucleic acids research 52 (D1), D368-D375, 2024 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:YTuZlYwrTOUC
Relativistic nuclear magnetic resonance J-coupling with ultrasoft pseudopotentials and the zeroth-order regular approximation TFG Green, JR Yates J. Chem. Phys., 234106, 2014 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:kJDgFkosVoMC
Computational predictions of protein structures associated with COVID-19 J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, the AlphaFold team N/A Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=deRDLTMAAAAJ&citationforview=deRDLTMAAAAJ:EsEWqaRxkBgC
John Jumper googlescholarauthorid johnjumper.md:a5goOh8AAAAJ
Kathryn Tunyasuvunakool googlescholarauthorid kathryntunyasuvunakool.md:eEqNGagAAAAJ
Alexander Pritzel googlescholarauthorid alexanderpritzel.md:GPgAyU0AAAAJ
Pushmeet Kohli googlescholarauthorid pushmeetkohli.md:3pyzQQ8AAAAJ
Jonathan Yates googlescholarauthorid jonathanyates.md:42SIq7YAAAAJ
Michael Figurnov googlescholarauthorid michaelfigurnov.md:qd0tOpQAAAAJ
Dominique Massiot googlescholarauthorid dominiquemassiot.md:XMU0LqMAAAAJ
Sharon E Ashbrook googlescholarauthorid sharone_ashbrook.md:hje7wnUAAAAJ
Richard Evans googlescholarauthorid richardevans.md:32NVaxYAAAAJ