Publications
2026
AIFL: A Global Daily Streamflow Forecasting Model Using Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS, ML Taccari, K Tazi, OM Morrison, A Grafberger, J Colonese, CC de Wiart, arXiv preprint arXiv:2602.16579.
Goldstein, J. E., Brockington, D., Sandbrook, C., Meyfroidt, P., Geldmann, J., Kuemmerle, T., ... & Unks, R. (2026). Environmental data justice is key for developing more effective area-based conservation approaches. Nature Reviews Biodiversity, 1-11.
Ten years on: are raw material criticality assessments making more sense?, B Andrieu, B Adams, M Heydari, P Mitchell, L Cullen, A Noskov, Resources, Conservation and Recycling 229, 108875.
Critical minerals requirements for meeting net zero pathways in the United Kingdom, S Stephenson, L Cullen, JM Cullen, AC Serrenho, Renewable and Sustainable Energy Transition, 100146.
Deciphering the impacts of meteorology on surface ozone variability in eastern China using explainable machine learning models, X Ye, L Zhang, X Wang, N Lu, S Hickman, G Luo, AT Archibald, EGUsphere 2026, 1-26.
Embedding-based Crop Type Classification in the Groundnut Basin of Senegal, MC Lisaius, S Keshav, A Blake, C Atzberger, arXiv preprint arXiv:2601.16900.
Roberts, J. et al. How Long Is a Piece of String? A Brief Empirical Analysis of Tokenizers. arXiv, 2026. https://arxiv.org/abs/2601.11518.
Roberts, J. et al. How Long Is a Piece of String? A Brief Empirical Analysis of Tokenizers. arXiv, 2026. https://arxiv.org/abs/2601.11518.
The path to robust evaluation of carbon credits generated by forest restoration and REDD+ projects, CE Wheeler, F Begliomini, A Holcomb, S Keshav, A Madhavapeddy, Remote Sensing of Environment 332, 115041.
Geospatial foundation models enable data-efficient tree species mapping in temperate mountain forests, JGC Ball, JA Wicklein, Z Feng, J Knezevic, S Jaffer, A Madhavapeddy, bioRxiv, 2026.02. 23.707022
Nath P, Schemm S, Moss H, Haynes P, Shuckburgh E, Webb MJ. Making Tunable Parameters State-Dependent in Weather and Climate Models with Reinforcement Learning. arXiv; 2026. ArXiv:2601.04268 [cs]. Available from: https://arxiv.org/abs/2601.04268.
Informing conservation problems and actions using an indicator of extinction risk: A detailed assessment of applying the LIFE metric, A Eyres, A Arnell, TS Ball, RJ Cuthbert, M Dales, A Guizar-Coutiño.
2025
A foundation model for the earth system, C Bodnar, WP Bruinsma, A Lucic, M Stanley, A Allen, J Brandstetter, Nature, 1-8.
End-to-end data-driven weather prediction, A Allen, S Markou, W Tebbutt, J Requeima, WP Bruinsma, TR Andersson, Nature 641 (8065), 1172-1179.
Artificial intelligence for methane detection: from continuous monitoring to verified mitigation, A Allen, G Mateo-Garcia, I Irakulis-Loitxate, MMS Martin, M Watine, arXiv preprint arXiv:2511.21777.
On the effective resolution of AI weather prediction models, T Selz, W Bruinsma, GC Craig, S Markou, R Turner, A Vaughan, Authorea Preprints.
Operational machine learning for remote spectroscopic detection of CH point sources, V Růžička, G Mateo-García, I Irakulis-Loitxate, JE Johnson, MMS Martín, arXiv preprint arXiv:2511.07719.
Refined climatologies of future precipitation over High Mountain Asia using probabilistic ensemble learning, K Tazi, S Woo P Kim, M Girona-Mata, RE Turner, Environmental Research Letters 20 (8), 084045.
Precipitation prediction over the upper Indus Basin from large-scale circulation patterns using Gaussian processes, K Tazi, A Orr, JS Hosking, RE Turner, Environmental Data Science 4, e46.
Refined climatologies of future precipitation over High Mountain Asia using probabilistic ensemble learning, K Tazi, S Woo P Kim, M Girona-Mata, RE Turner, Environmental Research Letters 20 (8), 084045.
Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya, M Girona-Mata, A Orr, M Widmann, D Bannister, GH Dars, S Hosking, Hydrology and Earth System Sciences 29 (14), 3073-3100.
Diving into AI? Exploring the potential for AI to help deliver clean rivers, lakes and seas in England, E Borgomeo, CG Billari, S Garg, M Girona-Mata, J Tlhomole, A Marinoni, University of Cambridge.
Machine Learning for Reconstructing Streamflow Time Series: An Application to the Nile River, CG Billari, M Girona-Mata, K Wheeler, A Marinoni, E Borgomeo, EGU General Assembly Conference Abstracts, EGU25-7429.
Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal, P West, MWL Wan, A Hepburn, E Simpson, R Santos-Rodriguez, JN Clark, arXiv preprint arXiv:2510.16233.
Zero-shot time series forecasting with covariates via in-context learning, A Auer, R Parthipan, P Mercado, AF Ansari, L Stella, B Wang, arXiv preprint arXiv:2506.03128.
Regularization of ML models for Earth systems by using longer model timesteps, R Parthipan, M Anand, HM Christensen, F Vitart, DJ Wischik, arXiv preprint arXiv:2503.18023.
Bayesian inversion of satellite altimetry for Arctic sea ice and snow thickness, E René-Bazin, M Tsamados, SSBA Raziuddin, JP Ferrer, T Suciu, C Nab, EGUsphere 2025, 1-29.
Treatment of Temporary GHG Removals in Voluntary Carbon Markets, 2025-09-30 | Preprint, DOI: 10.33774/coe-2025-5sbl5, Contributors: Henning Zschietzschmann; Jennifer Hawkin; Jonathan P. Rosser; Mala Virdee; Harriet Hunnable.
Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperatures, Atmospheric Science Letters, 2025-02 | Journal article, DOI: 10.1002/asl.1290, Contributors: Mala Virdee; Ieva Kazlauskaite; Emma J. D. Boland; Emily Shuckburgh; Alison Ming.
Artificial intelligence for science in quantum, atomistic, and continuum systems, X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, Foundations and Trends® in Machine Learning 18 (4), 385-849.
Accelerating Biomolecular Modeling with AtomWorks and RF3, N Corley*, S Mathis*, R Krishna*, MS Bauer, TR Thompson, W Ahern, bioRxiv, 2025.08. 14.670328
De novo design of all-atom biomolecular interactions with rfdiffusion3, J Butcher, R Krishna, R Mitra, RI Brent, Y Li, N Corley, PT Kim, J Funk, bioRxiv
Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design, LL Team, A Bridgland, J Crabbé, H Kenlay, D Pretorius, SM Schmon, arXiv preprint arXiv:2507.19375.
Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2, LL Team, H Kenlay, D Pretorius, J Crabbé, A Bridgland, SM Schmon, arXiv preprint arXiv:2512.20263.
Generative inverse design of RNA structure and function with gRNAde, CK Joshi, E Gianni, SLY Kwok, SV Mathis, P Liò, P Holliger, bioRxiv, 2025.11. 29.691298
Flows, straight but not so fast: Exploring the design space of Rectified Flows in Protein Design, J Chen, S Mathis, C Harris, K Didi, P Lio, arXiv preprint arXiv:2510.24732.
Straight but not so fast: Challenges with Rectified Flows in Protein Design., J Chen, SV Mathis, C Harris, K Didi, P Lio, ICML 2025 Generative AI and Biology (GenBio) Workshop.
RNA FrameFlow: Flow Matching for de novo 3D RNA Backbone Generation, R Anand, CK Joshi, A Morehead, AR Jamasb, C Harris, SV Mathis, K Didi.
Inverse Constitutional AI: Compressing Preferences into Principles, A Findeis, T Kaufmann, E Hüllermeier, S Albanie, R Mullins, International Conference on Learning Representations (ICLR).
Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge?, A Findeis, F Weers, G Yin, K Ye, R Pang, T Gunter, ACL 2025.
Feedback Forensics: A Toolkit to Measure AI Personality, A Findeis, T Kaufmann, E Hüllermeier, R Mullins, arXiv preprint arXiv:2509.26305.
Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning, J Suárez, KW Choe, D Bloomin, J Gao, Y Li, Y Feng, S Pola, K Zhang, arXiv preprint arXiv:2508.12524.
Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems, IJS Shokar, PH Haynes, RR Kerswell, arXiv preprint arXiv:2502.20603.
Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics, IJS Shokar, RR Kerswell, PH Haynes, arXiv preprint arXiv:2509.09599.
A systematic review of resilience in the critical minerals supply chains, needed for the low-carbon energy transition, M Heydari, P Mitchell, L Cullen, B Andrieu, AC Serrenho, J Cullen, Renewable and Sustainable Energy Transition, 100127.
A robust framework for estimating theoretical minimum energy requirements for industrial processes, N Bolson, L Cullen, J Cullen, Energy 322, 135411.
Improving operational use of post-disaster damage assessment for Urban Search and Rescue by integrated graph-based multimodal remote sensing data analysis, S Selvakumaran, I Rolland, L Cullen, R Davis, J Macabuag, Progress in Disaster Science 25, 100404.
Is the Concentration of Battery Manufacturing in China an Inevitable Hazard to Global Lithium Ion Battery Supply?, L Cullen, B Andrieu, S Jeen, AC Serrenho, JM Cullen, Available at SSRN 5303310.
Applications of machine learning and artificial intelligence in tropospheric ozone research, SHM Hickman, MM Kelp, PT Griffiths, K Doerksen, K Miyazaki, Geoscientific Model Development 18 (22), 8777-8800.
Causal climate emulation with bayesian filtering, S Hickman, I Trajkovic, J Kaltenborn, F Pelletier, A Archibald, Y Gurwicz, arXiv preprint arXiv:2506.09891.
Directed acyclic graphs in perioperative observational research-A systematic review and critique against best practice recommendations (vol18, e0281259, 2023), ML Watson, SHM Hickman, KM Dreesbeimdiek, K Kohler, DJ Stubbs.
Correction: Directed acyclic graphs in perioperative observational research–A systematic review and critique against best practice recommendations, ML Watson, SHM Hickman, KM Dreesbeimdiek, K Kohler, DJ Stubbs, e0334522.
Causal climate emulation, J Boussard, S Hickman, I Trajkovic, J Kaltenborn, Y Gurwicz, P Nowack, EGU General Assembly Conference Abstracts, EGU25-13307.
Understanding Ozone, Climate and Their Interactions With Causal Machine Learning, SHM Hickman, PQDT-UK & Ireland.
AI4O3: A Foundational Data Collection for Artificial Intelligence in Tropospheric Ozone Research, M Kelp, S Hickman, K Miyazaki, KL Chang, P Griffiths, Q Zhu, G Koren, NeurIPS 2025 AI for Science Workshop.
Machine learning methods for domestic energy prediction and retrofit potential for small-neighbourhoods at national scales in England and Wales, G Colverd, R Bardhan, J Cullen, Energy and Buildings, 116388.
NEBULA: A National Scale Dataset for Neighbourhood-Level Urban Building Energy Modelling for England and Wales, G Colverd, R Bardhan, J Cullen, arXiv preprint arXiv:2501.09407.
Tessera: Temporal embeddings of surface spectra for earth representation and analysis, Z Feng, C Atzberger, S Jaffer, J Knezevic, S Sormunen, R Young, arXiv preprint arXiv:2506.20380.
TESSERA: precomputed FAIR global pixel embeddings for earth representation and analysis, Z Feng, C Atzberger, S Jaffer, J Knezevic, S Sormunen, R Young, arXiv preprint arXiv:2506.20380.
Roberts, J. et al. ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models. arXiv (under review), 2025. https://arxiv.org/abs/2502.09696
Lin, W., Roberts, J. et al. GAMEBOT: Gaming Arena for Model Evaluation — Battle of Tactics. ACL, 2025. https://openreview.net/forum?id=dr0s6aGYb7
Roberts, J. et al. GRAB: GRaph Analysis Benchmark. ICCV, 2025. https://openaccess.thecvf.com/content/ICCV2025/papers/Roberts_GRAB_A_Challenging_GRaph_Analysis_Benchmark_for_Large_Multimodal_Models_ICCV_2025_paper.pdf
Roberts, J. et al. Needle Threading: Can LLMs Follow Threads through Near-Million-Scale Haystacks? ICLR, 2025. https://arxiv.org/abs/2411.05000
Airborne assessment uncovers socioeconomic stratification of urban nature in England, AC Zúñiga-González, A Madhavapeddy, R Bardhan, arXiv preprint arXiv:2510.13861.
Accuracy assessment of PlanetScope SuperDove products for aquatic reflectance retrieval over Brazilian inland and coastal waters, RG Chasles, DA Maciel, CCF Barbosa, EMLM Novo, VS Martins, ISPRS Journal of Photogrammetry and Remote Sensing 227, 678-690.
A novel hybrid cyanobacteria mapping approach for inland reservoirs using Sentinel-3 imagery, TMA de Lima, CCF Barbosa, CSF Nordi, FN Begliomini, VS Martins, Harmful Algae 144, 102836.
A New Remote Sensing Algorithm for Unveiling the Amazon Floodplain Lakes' Phytoplankton Biodiversity from Space, DA Maciel, CN Kraus, E Novo, M Paule-Bonnet, C Barbosa.
Advancing High Spatiotemporal Resolution Satellites for Freshwater Remote Sensing, R Chasles, DA Maciel, C Barbosa, E Novo, VS Martins, R Paulino.
Tessera: Temporal embeddings of surface spectra for earth representation and analysis, Z Feng, C Atzberger, S Jaffer, J Knezevic, S Sormunen, R Young, arXiv preprint arXiv:2506.20380.
Thyroid-gut-axis: how does the microbiota influence thyroid function? Nutrients. 2020; 12 (6): 1769, J Knezevic, C Starchl, A Tmava Berisha, K Amrein.
TESSERA: precomputed FAIR global pixel embeddings for earth representation and analysis, Z Feng, C Atzberger, S Jaffer, J Knezevic, S Sormunen, R Young, arXiv preprint arXiv:2506.20380.
Early warming over the southern ocean during the last deglaciation, P Zheng, T Bauska, M Osman, Geophysical Research Letters 52 (17), e2025GL117155.
Estimating how Site-Level Differences in Acoustic Environments Affect Species Detection by Machine Learning Models, R Marshall-Hawkes, S Gillings, MW Wilson, AS Wetherhill, LV Dicks, bioRxiv, 2025.08. 31.673350
SPREAD: A large-scale, high-fidelity synthetic dataset for multiple forest vision tasks, Z Feng, Y She, S Keshav, Ecological Informatics, 103085
PILA: Physics-Informed Low Rank Augmentation for Interpretable Earth Observation, Y She, A Blake, C Atzberger, A Gualandi, S Keshav, arXiv preprint arXiv:2405.18953
Scaling Up Forest Vision with Synthetic Data, Y She, A Blake, D Coomes, S Keshav, arXiv preprint arXiv:2509.11201
Nath P, Schemm S, Moss H, Haynes P, Shuckburgh E, Webb M. FedRAIN-Lite: Federated reinforcement algorithms for improving idealised numerical weather and climate models. 2026 EGU General Assembly (Oral); 2025. EGU26-351 (ITS1.7/CL0.3).
Nath P, Moss H, Shuckburgh E, Webb M. RAIN: Reinforcement algorithms for improving numerical weather and climate models. 2025 EGU General Assembly (Oral); 2025. EGU25-5159 (ITS1.4/CL0.10).
Ren Z, Nath P, Shukla P. Improving tropical cyclone forecasting with video diffusion models. 2025 ICLR workshop on Tackling Climate Change with Machine Learning; 2025. ArXiv:2501.16003 [cs].
GPGreen: Learning Linear Operators with Gaussian Processes, Thomas Cowperthwaite, Henry Moss. 1st Workshop on Differentiable Systems and Scientific Machine Learning @ EurIPS 2025. 06/12/2025
Valé, P.D., Bousfield, C.G., Morton, O., Poffley, J., Lamb, I., Koffi, B.J.-C., Koné, I., Garrett, R.D., Edwards, D.P. (2025) ‘Mining expansion as new driver of deforestation in Côte d’Ivoire’, Environmental Research Letters, 20(12), p. 124081. Available at: https://doi.org/10.1088/1748-9326/ae23e6.
Morton, O., Poffley, J., Laidler, G., & Baruch-Mordo, S. (2025). SC78 Doc. 65.2 Annex 4: ‘Proposed network methodology for the ETIS categorisation of Parties – Technical report’ Seventy-Eighth Meeting of the Standing Committee, CITES, Geneva, Switzerland. https://cites.org/sites/default/files/documents/E-SC78-65-02-R1_0.pdf
Diving into AI? Exploring the potential for AI to help deliver clean rivers, lakes and seas in England, E Borgomeo, CG Billari, S Garg, M Girona-Mata, J Tlhomole, A Marinoni, University of Cambridge.
Machine Learning for Reconstructing Streamflow Time Series: An Application to the Nile River, CG Billari, M Girona-Mata, K Wheeler, A Marinoni, E Borgomeo, EGU General Assembly Conference Abstracts, EGU25-7429.
Will ai tell lies to save sick children? litmus-testing ai values prioritization with airiskdilemmas, YY Chiu, Z Wang, S Maiya, Y Choi, K Fish, S Levine, E Hubinger, arXiv preprint arXiv:2505.14633.
Open character training: Shaping the persona of AI assistants through constitutional AI, S Maiya, H Bartsch, N Lambert, E Hubinger, arXiv preprint arXiv:2511.01689
Improving preference extraction in llms by identifying latent knowledge through classifying probes, S Maiya, Y Liu, R Debnath, A Korhonen, Proceedings of the 63rd Annual Meeting of the Association for Computational
Liars' Bench: Evaluating Lie Detectors for Language Models, K Kretschmar, W Laurito, S Maiya, S Marks, arXiv preprint arXiv:2511.16035
2024
CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery, A Vaughan, G Mateo-García, L Gómez-Chova, V Růžička, L Guanter, Atmospheric Measurement Techniques 17 (9), 2583-2593
Multivariate climate downscaling with latent neural processes, A Vaughan, M Herzog.
Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Karakoram Himalaya, M Girona-Mata, A Orr, M Widmann, D Bannister, GH Dars, S Hosking, EGUsphere 2024, 1-33.
Using Novel Lake-based Snowfall Measurements in the Alps and Himalayas to optimise Cloud and Precipitation processes in a Regional Atmospheric Model (MetUM), S Gumber, A Orr, P Field, H Pritchard, F Covi, P Deb, M Girona-Mata, European Geosciences Union General Assembly 2024 (EGU24), 10043.
Spatially-Coherent Probabilistic Downscaling of Daily Precipitation in Ungauged Mountain Locations: a Transfer Learning Study in the Swiss Alps and the Langtang Valley, Nepal., M Girona-Mata, A Orr, RE Turner, European Geosciences Union General Assembly 2024 (EGU24), 15911.
TraCE: Trajectory counterfactual explanation scores, JN Clark, EA Small, N Keshtmand, MWL Wan, EF Mayoral, E Werner, Northern Lights Deep Learning Conference, 36-45.
A temporal stochastic bias correction using a machine learning attention model, O Nivron, DJ Wischik, M Vrac, E Shuckburgh, AT Archibald, Environmental Data Science 3, e36.
Defining error accumulation in ML atmospheric simulators, R Parthipan, M Anand, HM Christensen, JS Hosking, DJ Wischik, arXiv preprint arXiv:2405.14714.
Machine learning for stochastic parametrization, HM Christensen, S Kouhen, G Miller, R Parthipan, Environmental Data Science 3, e38.
DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised -transform. A Denker*, F Vargas*, S Padhy*, K Didi*, S Mathis*, R Barbano, Advances in Neural Information Processing Systems 37, 19636-19682.
Rna-frameflow for de novo 3d rna backbone design, R Anand, CK Joshi, A Morehead, AR Jamasb, C Harris, SV Mathis, K Didi, ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative.
Improving antibody design with force-guided sampling in diffusion models, P Kulytė, F Vargas, SV Mathis, YG Wang, JM Hernández-Lobato, P Liò, arXiv preprint arXiv:2406.05832.
On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering, M Gantz*, SV Mathis*, FEH Nintzel*, P Lio, F Hollfelder, Faraday Discussions 252, 89-114.
Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering, M Gantz, SV Mathis, FEH Nintzel, M Penner, PJ Zurek, T Knaus, V Tseliou, bioRxiv, 2024.04. 08.588565.
grnade: Geometric deep learning for 3d rna inverse design. bioRxiv, CK Joshi, AR Jamasb, R Viñas, C Harris, SV Mathis, A Morehead, P Liò.
Language Model Crossover: Variation Through Few-Shot Prompting, E Meyerson, MJ Nelson, H Bradley, A Gaier, A Moradi, AK Hoover, ACM Transactions on Evolutionary Learning and Optimization 1.
Visibility into AI Agents, A Chan, C Ezell, M Kaufmann, K Wei, L Hammond, H Bradley, E Bluemke, FAccT '24: Proceedings of the 2024 ACM Conference on Fairness.
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?, R Schaeffer, H Schoelkopf, B Miranda, G Mukobi, V Madan, A Ibrahim, arXiv preprint arXiv:2406.04391.
Towards meta-models for automated interpretability, L Langosco, W Baker, N Alex, H Bradley, D Quarel, D Krueger.
Will Synthetic Data Finally Solve the Data Access Problem?, Z Xu, P Kairouz, H Bradley, R Cummings, G Fanti, L Ramaswamy, C Xie, ICLR 2025 Workshop Proposals.
Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets, IJS Shokar, RR Kerswell, PH Haynes, Journal of Advances in Modeling Earth Systems 11 (6).
Extending deep learning emulation across parameter regimes to assess stochastically driven spontaneous transition events, IJS Shokar, PH Haynes, RR Kerswell, ICLR 2024 workshop on AI4DifferentialEquations in science.
Conditioning deep learning on PDE parameters to generalise emulation of stochastic and chaotic dynamics, I Shokar, P Haynes, R Kerswell, APS Division of Fluid Dynamics Meeting Abstracts, C02. 005.
Longdon, J., Gabrys, J., & Blackwell, A. F. (2024). Taking data science into the forest. Interdisciplinary Science Reviews, 49(1), 82-103.
Evaluating representation learning on the protein structure universe, AR Jamasb, A Morehead, CK Joshi, Z Zhang, K Didi, S Mathis, C Harris, ArXiv, arXiv: 2406.13864 v1.
Longdon, J., Westerlaken, M., Blackwell, A. F., Gabrys, J., Ossom, B., Ashton-Butt, A., & Acheampong, E. (2024, May). Justice-oriented design listening: Participatory ecoacoustics with a Ghanaian forest community. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (p. 1-12).
Sharma, V., Oyewale, C. T., Lazaro Vasquez, E. S., Wani, A. S., Sari, E., Longdon, J., ... & Singh, P. (2024, May). Sustainabilities and HCIs from the Souths. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-5).
Chapman, M., Goldstein, B. R., Schell, C. J., Brashares, J. S., Carter, N. H., Ellis-Soto, D., ... & Boettiger, C. (2024). Biodiversity monitoring for a just planetary future. Science, 383(6678), 34-36.
Reducing uncertainties in greenhouse gas emissions from chemical production, L Cullen, F Meng, R Lupton, JM Cullen, Nature Chemical Engineering 1 (4), 311-322.
Machine learning for gapfilling in greenhouse gas emissions databases, L Cullen, A Marinoni, J Cullen, Journal of Industrial Ecology 28 (4), 636-647.
Energy mapping of existing building stock in Cambridge using energy performance certificates and thermal infrared imagery, Y He, J Pan, R Debnath, R Bardhan, L Cullen, MG Jenkins, E Mackie, Environmental Data Science 3, e44.
A Global Analysis of Pre-Earthquake Ionospheric Anomalies, L Cullen, AW Smith, AH Galib, D Varshney, EJE Brown, PJ Chi, X Chu, arXiv preprint arXiv:2401.01773
Development of machine-learning algorithms to predict attainment of minimal clinically important difference after hip arthroscopy for femoroacetabular impingement yield fair, MH Pettit, SHM Hickman, A Malviya, V Khanduja, Arthroscopy: the Journal of Arthroscopic & Related Surgery 40 (4), 1153-1163. e2.
Machine Learning for Tropospheric Ozone: A Review of Challenges and Opportunities, S Hickman, M Kelp, K Doerksen, G Koren, F Iglesias-Suarez, PT Griffiths, AGU Fall Meeting Abstracts 2024, A52C-05,
Learning Causal Representations of Climate Model Data, S Hickman, J Kaltenborn, J Boussard, C Lange, I Trajkovic, Y Gurwicz, AGU Fall Meeting Abstracts 2024 (33), GC11H-0033.
Estimating the causal effect of temperature on ozone air pollution, S Hickman, P Griffiths, P Nowack, A Archibald, EGU24.
Benchmarking domestic energy consumption using high-resolution neighbourhood energy data and city clustering in the UK, G Colverd, R Bardhan, J Cullen. Proceedings of the 11th ACM International Conference on Systems for Energy.
FloodBrain: Flood disaster reporting by web-based retrieval augmented generation with an LLM. arXiv 2023, G Colverd, P Darm, L Silverberg, N Kasmanoff, arXiv preprint arXiv:2311.02597.
3D-SAR tomography and machine learning for high-resolution tree height estimation, G Colverd, J Takami, L Schade, K Bot, JA Gallego-Mejia, arXiv preprint arXiv:2409.05636.
Evaluating Language Model Character Traits, FR Ward, Z Yang, A Jackson, R Brown, C Smith, GB Colverd, Findings of the Association for Computational Linguistics: EMNLP 2024, 1423-1443.
Tomographic SAR Reconstruction for Forest Height Estimation, G Colverd, J Takami, L Schade, K Bot, JA Gallego-Mejia, arXiv preprint arXiv:2412.00903.
High-resolution domestic energy modelling for national energy and retrofit planning, G Colverd, R Bardhan, J Cullen, NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning.
Tree Height Estimation using Machine Learning and 3D Tomographic SAR-a case study in Northern Europe, JAG Mejia, G Colverd, L Schade, KB Gonçalves, J Takami, Authorea Preprints.
Tree Height Estimation using Machine Learning and 3D Tomographic SAR-a case study in Northern Europe, J Gallego, G Colverd, L Schade, KB Gonçalves, J Takami, AGU24.
Tree Height Estimation using Machine Learning and 3D Tomographic SAR-a case study in Northern Europe, JA Gallego Mejia, G Colverd, L Schade, K Bot Gonçalves, J Takami, AGU Fall Meeting Abstracts 2024 (204), IN31D-204.
Tree Species Classification using Machine Learning and 3D Tomographic SAR-a case study in Northern Europe, JA Gallego Mejia, G Colverd, J Takami, L Schade, K Bot Gonçalves, AGU Fall Meeting Abstracts 2024 (8), NS23D-08.
Evaluating Language Model Character Traits, F Rhys Ward, Z Yang, A Jackson, R Brown, C Smith, G Colverd, arXiv e-prints, arXiv: 2410.04272.
Frontier Development Lab (FDL) Europe Technical Memorandum, G Colverd, J Takami, L Schade, JAG Mejía, K Bot.
Using Barlow twins to create representations from cloud-corrupted remote sensing time series, MC Lisaius, A Blake, S Keshav, C Atzberger, IEEE Journal of Selected Topics in Applied Earth Observations and Remote.
Roberts, J. et al. SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretation. NeurIPS, 2024.
Roberts, J. et al. Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs. CVPR EarthVision Workshop (Spotlight), 2024.
Green Urban Equity: Analyzing the 3-30-300 Rule in UK Cities and Its Socioeconomic Implications, A Zuñiga-Gonzalez, A Madhavapeddy, R Bardhan, European Geosciences Union General Assembly 2024 (EGU24), 20833.
Are State-of-the-Art LULC Maps Able to Track Ecological Restoration Efforts in Brazilian Atlantic Forest?, FN Begliomini, PHS Brancalion, IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium.
Using Multi-Modal Orbital Remote Sensing to Track Ecological Restoration in the Atlantic Forest, F Begliomini, DA Coomes, S Keshav, P Brancalion, AGU Fall Meeting Abstracts 2024 (65), GC21P-0065.
From Spectra to Biophysical Insights: End-to-End Learning with a Biased Radiative Transfer Model, Y She, C Atzberger, A Blake, S Keshav, arXiv preprint arXiv:2403.02922.
Ling Z, Nath P, Quilodrán-Casas C. Estimating atmospheric variables from digital typhoon satellite images via conditional denoising diffusion models. 2024 NeurIPS workshop on Tackling Climate Change with Machine Learning (Spotlight); 2024. ArXiv:2409.07961 [cs]. Available from: http://arxiv.org/abs/2409.07961
Marshall, C. A. M., Wade, K., Kendall, I. S., Porcher, H., Poffley, J., Bladon, A. J., Dicks, L. V., & Treweek, J. (2024). ‘England's statutory biodiversity metric enhances plant, but not bird nor butterfly, biodiversity.’ Journal of Applied Ecology, 61(8), 1918–1931.
Jackson, T. D., Bittencourt, P., Poffley, J., Anderson, J., Muller-Landau, H. C., Ramos, P. A. R., Rowland, L., & Coomes, D. (2024). ‘Wind Shapes the Growth Strategies of Trees in a Tropical Forest.’ Ecology Letters, 27(9), e14527. https://doi.org/10.1111/ele.14527.
Cluster-norm for unsupervised probing of knowledge, W Laurito, S Maiya, G Dhimoïla, OHW Yeung, K Hänni, Proceedings of the 2024 Conference on Empirical Methods in Natural Language.
2023
Semantic segmentation of methane plumes with hyperspectral machine learning models, V Růžička, G Mateo-Garcia, L Gómez-Chova, A Vaughan, L Guanter, Scientific Reports 13 (1), 19999.
Autoregressive conditional neural processes, WP Bruinsma, S Markou, J Requiema, AYK Foong, TR Andersson, arXiv preprint arXiv:2303.14468.
Sim2real for environmental neural processes, J Scholz, TR Andersson, A Vaughan, J Requeima, RE Turner, arXiv preprint arXiv:2310.19932.
Downscaling precipitation over High Mountain Asia using Multi-Fidelity Gaussian Processes: Improved estimates from ERA5, K Tazi, A Orr, J Hernandez-González, S Hosking, RE Turner, EGUsphere 2023, 1-33
Beyond Intuition, a Framework for Applying GPs to Real-World Data, K Tazi, JA Lin, R Viljoen, A Gardner, T John, H Ge, RE Turner, ICML 2023 Workshop on Structured Probabilistic Inference and Generative Modeling.
Extreme precipitation over High Mountain Asia: assessing likelihoods under different climate scenarios using Bayesian Committee Machines, K Tazi, JS Hosking, A Orr, RE Turner, AGU Fall Meeting Abstracts 2023, H23E-03.
Towards more interpretable and robust geospatial modelling with gaussian processes, K Tazi, JA Lin, AS Gardner, ST John, H Ge, RE Turner, AGU Fall Meeting Abstracts 2023 (563), IN11C-0563.
Learning causal drivers of PyroCb, E Díaz, G Varando, F Iglesias-Suarez, G Camps-Valls, K Tazi, K Lamb, EGU General Assembly Conference Abstracts, EGU-16846.
Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality, HZ Sun, J Zhao, X Liu, M Qiu, H Shen, S Guillas, C Giorio, Z Staniaszek, The Innovation 4 (6).
Understanding climate legislation decisions with machine learning, J Clark, M Wan, R Santos-Rodríguez, Tackling Climate Change with Machine Learning: workshop at NeurIPS 2023.
Monitoring Sustainable Global Development Along Shared Socioeconomic Pathways, MWL Wan, JN Clark, EA Small, EF Mayoral, R Santos-Rodríguez, arXiv preprint arXiv:2312.04416.
Understanding global and local multispecies air pollution trends and their interactions with human activities by graph machine learning, A Marinoni, FYF Tai, K Thomas, M Wan, S Selvakumaran, AGU Fall Meeting Abstracts 2023 (27), A33K-27.
Neural network studies of air quality and socioeconomic predictors of mortality, M Wan, A Archibald, EGU General Assembly Conference Abstracts, EGU-1535.
Taylorformer: Probabilistic Modelling for Random Processes including Time Series, O Nivron, R Parthipan, DJ Wischik, arXiv preprint arXiv:2305.1914.
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model, R Parthipan, HM Christensen, JS Hosking, DJ Wischik, Geoscientific Model Development 16 (15), 4501-4519.
Taylorformer: Probabilistic Modelling for Random Processes including Time Series, O Nivron, R Parthipan, DJ Wischik, arXiv preprint arXiv:2305.19141.
Bridging the Gap between Predictive Accuracy and Practical Usability: Enhancing Future Coastal Flooding Prediction with Interpretable Machine Learning Techniques, T Suciu, E Shuckburgh, H Moss, AGU Fall Meeting Abstracts 2023, OS14A-03.
Future Extreme Weather: a Data and AI driven approach to Understand Future Coastal Flooding, T Suciu, E Shuckburgh, N Lane, EGU General Assembly Conference Abstracts, EGU-17463.
A locally time-invariant metric for climate model ensemble predictions of extreme risk Environmental Data Science, Mala Virdee; Markus Kaiser; Carl H. Ek; Emily Shuckburgh; Ieva Kazlauskaite, 2023, DOI: 10.1017/eds.2023.13.
On the expressive power of geometric graph neural networks, CK Joshi, C Bodnar, SV Mathis, T Cohen, P Lio, International conference on machine learning, 15330-15355.
A hitchhiker's guide to geometric gnns for 3d atomic systems, A Duval*, SV Mathis*, CK Joshi*, V Schmidt*, S Miret, FD Malliaros, arXiv preprint arXiv:2312.07511.
Posecheck: Generative models for 3d structure-based drug design produce unrealistic poses, C Harris, K Didi, A Jamasb, C Joshi, S Mathis, P Lio, T Blundell, NeurIPS 2023 Generative AI and Biology (GenBio) Workshop.
Diffhopp: A graph diffusion model for novel drug design via scaffold hopping, J Torge, C Harris, SV Mathis, P Lio, arXiv preprint arXiv:2308.07416.
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design, K Didi*, F Vargas*, SV Mathis*, V Dutordoir*, E Mathieu, UJ Komorowska, arXiv preprint arXiv:2312.09236.
Computational tools for assessing forest recovery with GEDI shots and forest change maps, A Holcomb, SV Mathis, DA Coomes, S Keshav, Science of Remote Sensing 8, 100106.
Multi-state rna design with geometric multi-graph neural networks, CK Joshi, AR Jamasb, R Viñas, C Harris, S Mathis, P Liò, arXiv preprint arXiv:2305.14749.
Dynamics-Informed Protein Design with Structure Conditioning, UJ Komorowska*, SV Mathis*, K Didi, F Vargas, P Lio, M Jamnik, The Twelfth International Conference on Learning Representations.
Predicting protein variants with equivariant graph neural networks, A Boca, S Mathis, arXiv preprint arXiv:2306.12231.
Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation, D Nori, SV Mathis, A Shanehsazzadeh, arXiv preprint arXiv:2312.05273.
Do LLMs selectively encode the goal of an agent's reach?, L Ruis, A Findeis, H Bradley, HA Rahmani, KW Choe, E Grefenstette, First Workshop on Theory of Mind in Communicating Agents.
Pythia: A suite for analyzing large language models across training and scaling, S Biderman, H Schoelkopf, QG Anthony, H Bradley, K O’Brien, E Hallahan, International Conference on Machine Learning, 2397-2430.
Challenges and Applications of Large Language Models, J Kaddour, J Harris, M Mozes, H Bradley, R Raileanu, R McHardy, arXiv preprint arXiv:2307.10169.
Quality-Diversity through AI Feedback, H Bradley, A Dai, H Teufel, J Zhang, K Oostermeijer, M Bellagente, The Twelfth International Conference on Learning Representations (ICLR 2024).
Reclaiming the Digital Commons: A Public Data Trust for Training Data, A Chan, H Bradley, N Rajkumar, AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 2023.
The OpenELM Library: Leveraging Progress in Language Models for Novel Evolutionary Algorithms, H Bradley, H Fan, T Galanos, R Zhou, D Scott, J Lehman, Genetic Programming Theory and Practice 20.
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning, J Suárez, P Isola, KW Choe, D Bloomin, HX Li, N Pinnaparaju, N Kanna, Advances in Neural Information Processing Systems 36 (NeurIPS 2023).
Detecting Backdoors with Meta-Models, L Langosco, N Alex, W Baker, D Quarel, H Bradley, D Krueger, NeurIPS 2023 Workshop on Backdoors in Deep Learning-The Good, the Bad.
Do LLMs selectively encode the goal of an agent's reach?, L Ruis, A Findeis, H Bradley, HA Rahmani, KW Choe, E Grefenstette, First Workshop on Theory of Mind in Communicating Agents, ICML 2023.
Diff Models - A New Way to Edit Code, H Bradley, H Fan, H Saini, R Adithyan, S Purohit, J Lehman, https://carper.ai/diff-model/.
Inverse Tracr: Mapping Neural Network Weights to Code, W Baker, H Bradley, D Krueger.
The NeurIPS 2023 Neural MMO Challenge, J Suárez, P Isola, D Bloomin, KW Choe, HX Li, R Sullivan, N Kanna.
Reduced-Order Modelling of Stochastically Forced Zonal Jets using a 'Stochastic Latent Transformer, IJS Shokar, RR Kerswell, PH Haynes, Bulletin of the American Physical Society.
Learning Stochastic Dynamics with Probabilistic Neural Networks to study Zonal Jets, IJS Shokar, RR Kerswell, PH Haynes, EGU General Assembly, EGU-9121.
Machine learning for gap-filling in greenhouse gas emissions databases, L Cullen, A Marinoni, J Cullen, Tackling Climate Change with Machine Learning at NeurIPS 2023.
Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask RCNN, JGC Ball*, SHM Hickman*, TD Jackson*, XJ Koay, J Hirst, W Jay, Remote Sensing in Ecology and Conservation 9 (5), 641-655.
Short-term forecasting of ozone air pollution across Europe with transformers, SHM Hickman, PT Griffiths, PJ Nowack, AT Archibald, Environmental Data Science 2, e43.
Directed acyclic graphs in perioperative observational research–a systematic review and critique against best practice recommendations, ML Watson, SHM Hickman, KM Dreesbeimdiek, K Kohler, DJ Stubbs, Plos one 18 (2), e0281259.
Using reduced representations of atmospheric fields to quantify the causal drivers of air pollution, S Hickman, P Griffiths, P Nowack, A Archibald, EGU General Assembly Conference Abstracts, EGU-6061.
Floodbrain: Flood disaster reporting by web-based retrieval augmented generation with an llm, G Colverd, P Darm, L Silverberg, N Kasmanoff, arXiv preprint arXiv:2311.02597.
Atlantic ITCZ variability during the Holocene based on high-resolution speleothem isotope records from northern Venezuela, NMM Medina, FW Cruz, A Winter, H Zhang, A Ampuero, M Vuille, Quaternary Science Reviews 307, 108056.
Classifying sea ice in high-resolution SAR imagery using deep learning, A McDonald, J Dimasaka, M Plumridge, J Torry, AC Zúñiga González, EGU General Assembly Conference Abstracts, EGU-9816.
Assessment of estimated phycocyanin and chlorophyll-a concentration from PRISMA and OLCI in Brazilian inland waters: a comparison between semi-analytical and machine learning, TMA Lima, C Giardino, M Bresciani, CCF Barbosa, A Fabbretto, Remote Sensing 15 (5), 1299.
Machine learning for cyanobacteria mapping on tropical urban reservoirs using PRISMA hyperspectral data, FN Begliomini, CCF Barbosa, VS Martins, EMLM Novo, RS Paulino, ISPRS Journal of Photogrammetry and Remote Sensing 204, 378-396.
Roberts, J. et al. SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models. ICCV TNGCV Workshop, 2023.
Roberts, J. et al. GPT4GEO: How a Language Model Sees the World’s Geography. NeurIPS FMDM Workshop, 2023.
2022
Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds. Tazi, Kenza, Emiliano Díaz Salas-Porras, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, Nis Meinert. https://arxiv.org/abs/2211.13052
Identifying the Causes of Pyrocumulonimbus (PyroCb). Salas-Porras, Emiliano Diaz, Kenza Tazi, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, and Nis Meinert. " arXiv preprint arXiv:2211.08883 (2022).
Kernel Learning for Explainable Climate Science. Lalchand, Vidhi, Kenza Tazi, Talay M. Cheema, Richard E. Turner, and Scott Hosking. arXiv preprint arXiv:2209.04947 (2022).
Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model.Parthipan, Raghul, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik. EGUsphere (2022): 1-27.
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation. Parthipan, Raghul, and Damon J. Wischik. arXiv preprint arXiv:2210.04001(2022).
Localized impacts and economic implications from high temperature disruption days under climate change. Summers, Tim, Erik Mackie, Risa Ueno, Charles Simpson, J. Scott Hosking, Tudor Suciu, Andrew Coburn, and Emily Shuckburgh. Climate Resilience and Sustainability 1, no. 2 (2022): e35.
Future Extreme Weather: Assessing how Coastal Flooding will change due to Global Warming, using Data-Driven and Artificial Intelligence approaches, T Suciu, E Shuckburgh, N Lane, AGU Fall Meeting Abstracts 2022, GC23B-09.
Inverse modelling techniques for snow and ice thickness retrievals from satellite altimetry, J Perez Ferrer, M Tsamados, M Fox, T Suciu, H Heorton, C Nab, EGU General Assembly Conference Abstracts, EGU22-12882
Optimisation of a global climate model ensemble for prediction of extreme heat days. Virdee, Mala, Emily Shuckburgh, Carl Henrik Ek, Ieva Kazlauskaite, and Markus Kaiser. arXiv preprint arXiv:2211.16367 (2022).
Cohort-based long-term ozone exposure-associated mortality risks with adjusted metrics: A systematic review and meta-analysis. Sun, Haitong Zhe, Pei Yu, Changxin Lan, Michelle WL Wan, Sebastian Hickman, Jayaprakash Murulitharan, Huizhong Shen, Le Yuan, Yuming Guo, and Alexander T. Archibald. " The Innovation (2022): 100246.
Can simple machine learning methods predict concentrations of OH better than state of the art chemical mechanisms?, S Hickman, P Griffiths, J Weber, A Archibald, EGU General Assembly Conference Abstracts, EGU22-6553
Attention‐Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images. Brown, Edward JE, Filip Svoboda, Nigel P. Meredith, Nicholas Lane, and Richard B. Horne. Space Weather 20, no. 3 (2022): e2021SW002976.
AI applications in forest monitoring need remote sensing benchmark datasets. Lines, E., Allen, M., Cabo, C., Calders, K., Debus, A., Grieve, S., Miltiadou, M., et al.2022 IEEE International Conference on Big Data Workshop BDA4S 2021. https://doi.org/10.17863/CAM.91304
On the Expressive Power of Geometric Graph Neural Networks. Joshi, Chaitanya K., Cristian Bodnar, Simon V. Mathis, Taco Cohen, and Pietro Liò. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations.
Beobench: a toolkit for unified access to building simulations for reinforcement learning, Arduin Findeis, Fiodar Kazhamiaka, Scott Jeen, Srinivasan Keshav. e-Energy '22: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems, June 2022 Pages 374–382.
Accurate tropical forest individual tree crown delineation from RGB imagery using Mask R-CNN. Hickman, Sebastian HM, James George Clifford Ball, Tobias D. Jackson, Xian Jing Koay, James Hirst, William Jay, Melaine Aubry-Kientz, Gregoire Vincent, and David A. Coomes. bioRxiv (2022).
Tree species classification from complex laser scanning data in Mediterranean forests using deep learning. Matthew J. Allen, Stuart W. D. Grieve, Harry J. F. Owen, Emily R. Lines. Methods in Ecology and Evolution, 18 Sep 2022.
Studies of the effect of stratospheric ozone depletion on tropospheric oxidising capacity over the period 1979-2010 using the UKCA Chemistry-Climate model, P Griffiths, J Keeble, S Hickman, YM Shin, NL Abraham, J Pyle, ..., EGU General Assembly Conference Abstracts, EGU22-3754.
Tree crown delineation using detectreeRGB (Jupyter Notebook) published in the Environmental Data Science book, SHM Hickman, A Coca-Castro
Active learning with convolutional gaussian neural processes for environmental sensor placement. TR Andersson, WP Bruinsma, S Markou, DC Jones, JS Hosking, ..., arXiv e-prints, arXiv: 2211.10381
Practical conditional neural processes via tractable dependent predictions, S Markou, J Requeima, WP Bruinsma, A Vaughan, RE Turner, arXiv preprint arXiv:2203.08775.
RaVÆn: unsupervised change detection of extreme events using ML on-board satellites, V Růžička, A Vaughan, D De Martini, J Fulton, V Salvatelli, C Bridges, ..., Scientific reports 12 (1), 16939.
Multimodal Machine Learning for Earthquake Identification and Forecasting, D Varshney, L Cullen, A Galib, AW Smith, E Brown, PJ Chi, X Chu, ..., AGU Fall Meeting Abstracts 2022, INV44A-05.
Open-Source Data Pipelines and Statistical Tool for Studying Pre-Seismic and Post-Seismic Disturbances in the Ionosphere and Geomagnetic Field, L Cullen, A Galib, AW Smith, D Varshney, E Brown, PJ Chi, X Chu, ..., AGU Fall Meeting Abstracts 2022, IN25A-07.
Multimodal Heliophysical/Geophysical Machine Learning Models for Earthquake Identification and Forecasting, D Varshney, L Cullen, A Galib, AW Smith, E Brown, PJ Chi, X Chu, ..., AGU Fall Meeting Abstracts 2022, NG51A-02
Comprehensive Statistical Analysis of Ionospheric and Geomagnetic Signatures Before and After Earthquakes, L Cullen, A Galib, AW Smith, D Varshney, E Brown, PJ Chi, X Chu, ..., AGU Fall Meeting Abstracts 2022, NH13A-04
Remote sensing for search and rescue operations: Two methods studying the 2020 beirut blast, S Selvakumaran, I Rolland, L Cullen, A Marinoni, IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium
EleutherAI: Going Beyond" Open Science" to" Science in the Open", J Phang, H Bradley, L Gao, L Castricato, S Biderman, NeurIPS Workshop on Broadening Research Collaborations 2022.
Carbon fluxes during Dansgaard–Oeschger events as simulated by an Earth system model, M Jochum, Z Chase, R Nuterman, J Pedro, S Rasmussen, G Vettoretti, Journal of Climate 35 (17), 5745-5758.
Different trends in Antarctic temperature and atmospheric CO2 during the last glacial, P Zheng, J Pedro, M Jochum, SO Rasmussen, Z Lai, Authorea Preprints.
Strong regulation of daily variations in nighttime surface urban heat islands by meteorological variables across global cities, Y She, Z Liu, W Zhan, J Lai, F Huang, Environmental Research Letters 17 (1), 014049.
Can We Forecast And Detect Earthquakes From Heterogeneous Multivariate Time Series Data?, L Cullen, AH Galib, AW Smith, D Varshney, E Brown, P Chi, X Chu, I 36th Conference on Neural Information Processing Systems (NeurIPS 2022).
Fast Hierarchical Learning for Few-Shot Object Detection, Y She, G Bhat, M Danelljan, F Yu, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems.
2021
Multivariate climate downscaling with latent neural processes.Vaughan, Anna, Nicholas D. Lane, and Michael Herzog. Climate Change AI Workshop. 2021
Convolutional conditional neural processes for local climate downscaling, A Vaughan, W Tebbutt, JS Hosking, RE Turner, Geoscientific Model Development Discussions 2021, 1-25
Defining Southern Ocean fronts using unsupervised classification. Simon D. A. Thomas, Daniel C. Jones,Anita Faul, Erik Mackie, and Etienne Pauthenet. Ocean Sci., 17, 1545–1562, 2021 https://doi.org/10.5194/os-17-1545-2021
Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990–2019: A Space–Time Bayesian Neural Network Downscaler. Sun, Haitong, Youngsub Matthew Shin, Mingtao Xia, Shengxian Ke, Michelle Wan, Le Yuan, Yuming Guo, and Alexander T. Archibald. Environmental Science & Technology (2021).
Long-term ozone exposure associated cause-specific mortality risks with adjusted metrics by cohort studies: A systematic review and meta-analysis, HZ Sun, P Yu, C Lan, M Wan, S Hickman, J Murulitharan, H Shen, L Yuan, ..., medRxiv, 2021.12. 02.21267196
Vision transformers and techniques for improving solar wind speed forecasts using solar EUV images. Svoboda, Filip, Edward Brown, Nigel P. Meredith, and Nicholas D. Lane. NeurIPs, (2021).
Dropout and Ensemble Networks for Thermospheric Density Uncertainty Estimation, Stefano Bonasera, Giacomo Acciarini, Jorge Pérez-Hernández, Bernard Benson, Edward Brown, Eric Sutton, Moriba Jah, Christopher Bridges, Atilim Gunes Baydin, Bayesian Deep Learning workshop, NeurIPS 2021.
Gauge-invariant quantum circuits for (1) and Yang-Mills lattice gauge theories, G Mazzola, SV Mathis, G Mazzola, I Tavernelli, Physical review research 3 (4), 043209.
Data-Driven Exploration of Mid-Latitude Weather, IJS Shokar, University of Cambridge.
2020
Real world and tropical cyclone world. Part II: Sensitivity of tropical cyclone formation to uniform and meridionally varying sea surface temperatures under aquaplanet conditions, KJE Walsh, S Sharmila, M Thatcher, S Wales, S Utembe, A Vaughan, Journal of Climate 33 (4), 1473-1486.
The stationary banding complex and secondary eyewall formation in tropical cyclones, A Vaughan, KJE Walsh, JD Kepert, Journal of Geophysical Research: Atmospheres 125 (6), e2019JD031515.
Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions, C Poulsen, U Egede, D Robbins, B Sandeford, K Tazi, T Zhu, Remote Sensing of Environment 248, 111999
Toward scalable simulations of lattice gauge theories on quantum computers, SV Mathis, G Mazzola, I Tavernelli, Physical Review D 102 (9), 094501
Environmental data justice, J Longdon, The Lancet Planetary Health 4 (11), e510-e511.
2019
High‐resolution snowline delineation from Landsat imagery to infer snow cover controls in a Himalayan catchment, M Girona‐Mata, ES Miles, S Ragettli, F Pellicciotti, Water Resources Research 55 (8), 6754-6772.