Staff
Department/Affiliation |
Themes and Techniques |
|
| Luke Abraham | Yusuf Hamied Dept of Chemistry | Atmospheric chemistry, climate , interactions and earth-system modelling (weather, climate) |
| Alex Archibald | Yusuf Hamied Dept of Chemistry | Atmospheric chemistry, biosphere-atmosphere, volatile organic compound (air quality), modelling |
| Mike Bithell | Department of Geography, Centre for Science and Policy, Cambridge Centre for Data-Driven Discovery | Numerical modelling of spatially distributed systems |
| Carl Henrik Ek | Dept of Computer Science and Technology | Uncertainty quantification, bayesian non-parametrics, active learning, approximate Inference (climate) |
| Hamza Fawzi | Dept of Applied Math and Theoretical Physics | Convex optimisation and applications |
| Jennifer Gabrys | Department of Sociology | Air quality, forests, digital social research, interviews, participation |
| Chiara Giorio | Yusuf Hamied Dept of Chemistry | xploring the present and past of the earth's atmosphere using advanced analytical tools |
| Michael Herzog | Dept of Geography | Development of atmospheric models from local to global scales, modelling of convective clouds and plumes, role of convection in the climate system, understanding of the hydrological cycle, understanding of the role of aerosols, impact of aerosols on dynamical and microphysical processes |
| Mateja Jamnik | Department of Computer Science and Technology | Artificial intelligence, reasoning and machine learning, explainability (climate) |
| Ali Mashayek | Department of Earth Sciences | Climate Dynamics, Geophysical Fluid Dynamics, Marine Ecosystems, Data Science |
| Tracy Moffat-Griffin | British Antarctic Survey | Space, polar science, weather |
| Carl Rasmussen | Dept of Engineering | Inference and learning in non-parametric models, and their application to problems in non-linear adaptive control |
| Carola-Bibiane Schönlieb | Dept of Applied Math and Theoretical Physics | Nonlinear PDEs, inverse problems in imaging, sparse regularisation, machine learning for inverse problems |
| Emily Shuckburgh | Dept of Computer Science and Technology | Communication and public attitudes to climate change and climate science, linking climate change and sustainability, improving predictions of future climate change using theoretical approaches, observational studies and numerical modelling, transport and dynamics of the atmosphere, oceans and climate, role of the polar oceans in the global climate system, artificial intelligence, data science, machine learning |
| Koen Steemers | Dept of Architecture | Air quality, biodiversity, buildings, energy, data science, modelling |
| Liz Thomas | British Antarctic Survey | Climate, polar science, modelling |
| Richard Turner | Department of Engineering | Machine learning, computer perception, statistical signal processing, machine learning for climate science |
| Damon Wischik | Dept of Computer Science and Technology |
Visualisation (programming languages), simulation (climate) |
| Eiko Yoneki | Dept of Computer Science and Technology | Research methods, social networks, social media, cryptography, simulation, uncertainty quantification |