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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.

Goals

The goals of the group are to

  • Disseminate information in energy and environment topic areas through seminars, workshops, and scientific publications
  • Encourage collaboration between group members within this Department and help coordinate interdisciplinary initiatives across University departments.
  • Provide mentoring to students (Part II, III, MPhil, and students in the AI4ER and Sensors Doctoral Training Centres)
  • Create an entity that can attract external funding and donations.
  • Gather equipment and laboratory space required (e.g. sensors, drones)
  • Provide guidance to the Teaching Committee on Computer Science Tripos courses relevant to energy and environment.
  • Contribute to the development of relevant undergraduate and graduate courses within the University.
  • Have a measurable impact on tools and techniques for de-risking our future.

Membership

  • Faculty members in the Department for Computer Science and Technology 
  • Postdocs, PhDs, Lab Visitors and Master’s students primarily from the Department for Computer Science and Technology and other departments, as appropriate

Seminars

Lent 2023

Date

Title of presentation

Speaker name

Affiliation

January 20

Quantifying changes in Above Ground Biomass in degraded and restored forests: Challenges & opportunities Charlotte Wheeler

Cambridge

January 27

Machine Learning in Climate Action

David Rolnick

McGill

February 3

The value of high resolution remote sensing to understand forests Emily Lines

Cambridge

February 10

Ecovisor: A Virtual Energy System for Carbon-Efficient Applications

David Irwin

U. Mass Amherst

February 17

Research Avenues for Net-Zero Carbon Cloud Datacenters Fiodar Kazhamiaka Microsoft

February 24

CityLearn: An OpenAI Gym Framework for Grid-Interactive Buildings

Zoltan Nagy

U. Texas Austin

March 3

TBA Madeline Lisaius Cambridge
March 10 New framework for carbon offsets in global drylands Adam Pellegrini Cambridge

 

Affiliated centres

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