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Department of Computer Science and Technology

 

Energy and Environment Group (EEG)

The Energy and Environment Research Group applies computer science to address renewable energy integration, energy demand reduction, and the assessment and management of environmental impact (e.g. climate change, biodiversity loss, deforestation) from anthropogenic activities.

We operate in an interdisciplinary manner, collaborating with climate scientists, ecologists, engineers, lawyers, regulators, and economists, and conducting wide engagement with external partners to effect evidence-based outcomes.

Goal

Our primary goal is to have a measurable impact on tools and techniques for de-risking our future. To do so, we share recent advances at the intersection of computer science, energy, and the environment through seminars, workshops, and scientific publications. We also help form collaborations between group members to coordinate interdisciplinary initiatives across University departments. 

Membership

EEG members are, in the first instance, faculty members in the Department for Computer Science and Technology and their students. We also invite membership from Postdocs, PhDs, Lab Visitors and Master’s students primarily from other departments, as appropriate.

Seminars

A list of talks for the current term can be found below; talks from prior terms are linked to this page. Seminar details can also be found at Talks.cam. Recordings from the EEG seminar series are available to watch online. We thank the Institute of Computing for Climate Science for their sponsorship of this series.


Partners


Upcoming seminars

Easter term

  • 10Jul
    Robin Young, University of Cambridge

    *Abstract*

    Soil fungal communities are critical drivers of terrestrial ecosystem function, yet their global distribution remains largely unknown due to the challenges of widespread physical sampling. We developed a machine learning pipeline to predict fungal biodiversity across Europe and Asia using high-resolution, temporal satellite imagery. We introduce a novel feature set derived from a self-supervised learning (SSL) model applied to Sentinel time series. We trained a model on roughly 12,000 mycorrhizal fungal richness samples, comparing the predictive power of our SSL features against standard environmental datasets. Our combined model achieves a robust R2 of 0.53-0.55 across 50 cross-validation runs. We show that the SSL features are the single most important predictor group, outperforming traditional datasets and implicitly capturing land cover information. Furthermore, we demonstrate that prediction errors are geographically clustered in sparsely sampled regions, providing a data-driven method for identifying "biodiversity data deserts" and guiding future sampling efforts. This work presents a scalable framework for monitoring an overlooked component of global biodiversity and demonstrates the viability of temporally-rich, self-supervised representations for ecological modeling.

    *Bio*

    Robin Young is a first-year PhD student in Computer Science at the University of Cambridge.

  • 17Jul
    Julia Gschwind ETH Zurich, University of Cambridge

    *Abstract*

    Stay tuned!

    *Bio*

    Stay tuned!