Climate and air quality - students | AI for the study of Environmental Risks (AI4ER)

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Students

Student	Department	Topic area	PhD project title
Herbie Bradley	Yusuf Hamied Dept of Chemistry	Climate	Chemical mechanism emulation in climate model simulations for exascale supercomputing applications
Luke Cullen	Dept of Engineering	Climate	Reducing emissions uncertainties using data fusion within graph representations
Seb Hickman	Yusuf Hamied Dept of Chemistry	Air quality, climate, environmental hazards	Determining the risks and drivers of extreme ozone events during heatwaves with machine learning and causal inference
Yilin Li	Yusuf Hamied Dept of Chemistry	Air quality	Using advanced sensor technologies, detailed health outcomes and AI techniques to investigate the underlying mechanisms of air pollution on health
Raghul Parthipan	British Antarctic Survey and Dept of Computer Science and Technology	Climate	Probabilistic machine learning, using GANs and Gaussian Processes, to reduce climate model computational costs, improve parameterisations and improve accuracy
Ira Shokar	Dept of Applied Math and Theoretical Physics	Climate	Data-Driven Exploration of Parameterisation Schemes within Models of the Tropospheric Mid-Latitudes
Tudor Suciu	Dept of Computer Science and Technology	Climate	AI for Climate related Business Risks – Partner WTW
Kenza Tazi	British Antarctic Survey and Dept ofEngineering	Climate	Local alpine precipitation predictions from largescale data: a probabilistic machine learning approach
Sophie Turner	Yusuf Hamied Dept of Chemistry	Climate	Optimising Atmospheric Photolysis Simulations for Climate Models
Anna Vaughan	Dept of Computer Science and Technology	Weather, environmental hazards	On-board physics informed learning for severe weather nowcasting
Mala Virdee	Dept of Computer Science and Technology	Climate	AI for climate risks in sustainable development
Michelle Wan	Yusuf Hamied Dept of Chemistry	Air quality	Machine learning applications to the study of air pollution exposure & human health