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Department of Computer Science and Technology



Research Theme: Energy Systems 

PV Sizing and Operation

Researcher: Anaïs Berkes

Supervisor: Srinivasan Keshav


We address the optimal sizing and operation of home energy systems integrating photovoltaic (PV) panels, electric vehicles (EVs), and home energy management systems (HEMS) in the context of increased remote working. Using our SOPEVS framework, we analyze the impact of commuting habits and HEMS preferences on system sizing. Our findings suggest that remote-working homeowners with bidirectional EVs can eliminate the need for separate home storage, reducing system costs significantly, with potential savings of up to 80% when maximizing solar energy charging.

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Datacentre /LLM Energy Use

Researcher: Grant Wilkins

Supervisor: Srinivasan Keshav, Richard Mortier


Research Theme: Heatwave Impact

Topic: Buildings

Researcher: Livia Capol

Supervisors: Zoltan Nagy, Srinivasan Keshav


Topic: Transmission Grid

Researcher: Enming Liang

Supervisors: Minghua Chen, Srinivasan Keshav


Topic: Urban Centres

Researchers: Andrea Domiter, Andrés Zúñiga-González

Supervisors: Srinivasan Keshav, Anil Madhavapeddy, Ronita Bardhan,


Research Theme: Biodiversity Monitoring


Researcher: Emilio Luz-Ricca

Supervisors: Andrew Balmford, Michael Dales, Anil Madhavapeddy, Tom Swinfield


In response to the global biodiversity crisis and to meet demand for tools to quantify biodiversity loss or gain, members of the Cambridge Centre for Carbon Credits (4C) have developed the Land-cover change Impacts on Future Extinctions (LIFE) metric—a spatial metric that quantifies and aggregates the effect of land use changes on species extinction risk for nearly 30,000 terrestrial vertebrate species.

My work is primarily focused on improving LIFE extinction risk estimates. One aspect of this is examining the distribution and intensity of various anthropogenic threats (e.g., fragmentation, edge effects, hunting, invasive species). Another avenue for improving LIFE estimates is through the input data products; to this end, I will experiment with data-driven methods for improving species range maps with an eye towards integrating plant species into LIFE.

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Topic: Insect Monitoring

Researchers: Sachin Matthews, Matteo Redana

PIs: Lynn Dicks, Srinivasan Keshav

Summary from Matteo Redana:

Insects are facing significant declines in biomass, abundance and diversity, raising concerns about biodiversity loss and ecosystem impacts. Recent studies suggest insect population trends are influenced by complex relationships with environmental factors like climate and land use. While methods to monitor environmental conditions at high spatiotemporal resolutions have advanced rapidly, insect monitoring techniques still largely rely on manual sampling with lower resolution and standardization. To address this mismatch, researchers at the University of Cambridge are developing an accessible flying insect monitoring system using machine learning algorithms to track insect biomass and dynamics in near real-time at unprecedented temporal resolution (seconds). The low-cost, simple, and energy-autonomous system aims to enable large-scale monitoring, even in remote areas. Current limitations around camera standardization and taxonomic resolution are being addressed through proposed improvements like stereoscopic cameras for precise size estimation and a separate close-range imaging system for higher taxonomic classification. These standardized, non-harmful monitoring approaches could significantly advance insect population research and conservation efforts.

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Topic: Real-Time Wildlife Monitoring

Researcher: Tom Ratsakatika

Supervisors: Ruben Iosif, Srinivasan Keshav


Machine Learning Analysis of Camera Trap Photos for an Automated Wildlife Alert System in the Romanian Carpathian Mountains.

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Topic: Plant SDM

Researcher: Onkar Gulati

Supervisors: David Coomes, Sadiq Jaffer, Anil Madhavapeddy


Research Theme: Remote Sensing

Topic: Forest Degradation

Researcher: Amelia Holcomb

Supervisors: David Coomes, Srinivasan Keshav


Topic: Crop Detection

Researcher: Maddy Lisaius

Supervisors: Clement Atzberger, Andrew Blake, David Coomes, Srinivasan Keshav


Research Theme: Forest Carbon Monitoring

Topic: TMF 2.0

Researcher: Patrick Ferris

Supervisors: Michael Dales, Anil Madhavapeddy, Srinivasan Keshav, Thomas Swinfield

PIs: Anil Madhavapeddy, Srinivasan Keshav


Topic: Trunk Diameter

Researcher: Frank Feng

Supervisor: Srinivasan Keshav


In forestry and ecological studies, accurately measuring tree trunk diameter is essential for monitoring forest health, estimating biomass, and conducting various environmental assessments. Traditional methods are often time-consuming and labor-intensive. While some smartphone-based tree-trunk diameter measurement techniques have been proposed in recent years, most of these solutions require high-end depth sensors.

In response to these challenges, we have developed a novel smartphone application capable of measuring tree trunk diameter without the need for specialized depth sensors, thereby expanding accessibility to both high-end and low-end smartphone users. Our app leverages advanced neural networks and image processing algorithms to accurately estimate tree trunk diameter from images taken by the optical camera.

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Topic: Impact of Fires

Researcher: Jovana Knezevic

Supervisors: David Coomes, Srinivasan Keshav, Anil Madhavapeddy, Srinivasan Keshav


Topic: 3D Reconstruction

Researcher: Yihang She

Supervisors: Andrew Blake, David Coomes, Srinivasan Keshav


Topic: Regrowth

Researcher: Felipe Begliomini

Supervisors: David Coomes, Srinivasan Keshav, Charlotte Wheeler




AI for Conservation Evidence


Research Theme: Planetary Computing

Topic: Data Pipeline

Researchers: Michael Dales, Patrick Ferris, Sadiq Jaffer, Anil Madhavapeddy

PI: Anil Madhavapeddy


Topic: Digital ID

Researcher: Jessica Man

PI: Anil Madhavapeddy


Topic: Spatial Naming

Researcher: Ryan Gibbs

Supervisor: Anil Madhavapeddy


Topic: Human Rights

Researcher: Patrick Ferris

Supervisor: Anil Madhavapeddy


Topic: Compressive Streaming for Geospatial Pipelines

Researcher: Omar Tanner

Supervisor: Sadiq Jaffer, Anil Madhavapeddy