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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

Michaelmas term

  • 24Oct
    Yihang She, University of Cambridge

    *Abstract*

    Accurate tree segmentation is a key step in extracting individual tree metrics from forest laser scans, and is essential to understanding ecosystem functions in carbon cycling and beyond.
    Over the past decade, tree segmentation algorithms have advanced rapidly due to developments in AI. However existing, public, 3D forest datasets are not large enough to build robust tree segmentation systems.
    Motivated by the success of synthetic data in other domains such as self-driving, we investigate whether similar approaches can help with tree segmentation.
    In place of expensive field data collection and annotation, we use synthetic data during pretraining, and then require only minimal, real forest plot annotation for fine-tuning.

    We have developed a new synthetic data generation pipeline to do this for forest vision tasks, integrating advances in game-engines with physics-based LiDAR simulation.
    As a result, we have produced a comprehensive, diverse, annotated 3D forest dataset on an unprecedented scale.
    Extensive experiments with a state-of-the-art tree segmentation algorithm and a popular real dataset show that our synthetic data can substantially reduce the need for labelled real data.
    After fine-tuning on just a single, real, forest plot of less than 0.1 hectare, the pretrained model achieves segmentations that are competitive with a model trained on the full scale real data.
    We have also identified critical factors for successful use of synthetic data: physics, diversity, and scale, paving the way for more robust 3D forest vision systems in the future.
    Our data generation pipeline and the resulting dataset are available at https://github.com/yihshe/CAMP3D.git.

    *Bio*

    Yihang She is a third-year PhD student in Computer Science at the University of Cambridge. His research focuses on advancing computer vision in the novel context of forest monitoring, spanning both close-range and satellite-based observations.

  • 14Nov
    Frank Feng, University of Cambridge

    *Abstract*

    Stay Tuned!

    *Bio*

    Frank Feng is a second-year Ph.D. student in the Department of Computer Science and Technology at the University of Cambridge. His research interests lie at the intersection of machine learning and earth sciences, with a particular focus on the application of self-supervised learning in remote sensing.

  • 21Nov
    David Moffat, AI Data Science Lead at Plymouth Marine Laboratory

    *Abstract*
    Stay Tuned

    *Bio*

    Dr. David Moffat is the AI Data Science Lead at Plymouth Marine Laboratory. With a strong foundation in computer science and extensive experience in applying AI and data science techniques across diverse datasets, David is currently focused on advancing environmental and marine science research. His work involves leveraging both modern and traditional AI methodologies to enhance our understanding of the natural world.

    David’s expertise lies in applied AI, particularly in interdisciplinary research, such as signal processing and time series data analysis. He holds a BSc in Artificial Intelligence and Computer Science from the University of Edinburgh and an MSc. in Signal Processing and PhD. in Computer Science from Queen Mary University of London. Following his academic journey, he has held roles as a postdoctoral researcher, university lecturer, and now leads innovative AI-driven initiatives at Plymouth Marine Laboratory. David has authored 17 peer-reviewed articles, 13 peer-reviewed conference papers, and four book chapters. He has also delivered AI and Earth Observation training courses to over 500 participants. In addition to his academic accomplishments, he has a background in technical support, project management, and research leadership.

  • 28Nov
    Jakob Poffley, University of Cambridge

    *Abstract*

    Stay Tuned.

    *Bio*

    Stay Tuned.

  • 05Dec
    Jovana Knezevic, University of Cambridge

    *Abstract*

    Stay Tuned!

    *Bio*

    Stay Tuned!