Energy and Environment Group

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

01May

Is There Hope for the Climate?

Srinivasan Keshav, University of Cambridge

*Abstract*
Stay tuned!

*Bio*

Srinivasan Keshav is the Robert Sansom Professor of Computer Science at the University of Cambridge, focusing on the intersection of computer science and sustainability. He earned his PhD from UC Berkeley and has held roles at Bell Labs, Cornell University, and the University of Waterloo. A Fellow of the Royal Society of Canada, ACM, and IEEE, Keshav is recognized for his contributions to networking and sustainability. His research includes innovations in energy systems, carbon footprint reduction, and forest conservation using remote sensing. Keshav emphasizes practical applications of computer science to global challenges, fostering collaborative solutions in smart grids and biodiversity conservation.
08May

Machine Learning for Building-Level Heat Risk Mapping

Andrea Domiter, University of Cambridge

*Title*

Machine Learning for Building-Level Heat Risk Mapping

*Abstract*

Climate change is intensifying the frequency and severity of heat waves, increasing risks to public health and energy systems worldwide. However, many existing heat vulnerability assessments focus primarily on outdoor temperatures, overlooking indoor conditions that directly affect occupants. Although building simulations can reveal the types of buildings whose occupants are most at risk, they rarely pinpoint the exact locations of these vulnerable buildings. In this presentation, I will present a data-driven workflow that locates high-risk buildings and discuss the labeling strategies we have explored for classifying real-world structures using satellite imagery.

*Bio*

Andrea is a first-year PhD student in the Department of Computer Science and Technology at the University of Cambridge. She is supervised by Prof Srinivasan Keshav. Her research bridges machine learning with civil and environmental engineering, focusing particularly on its applications within the built environment.