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Spatiotemporal Machine Learning Foundations for Urban Digital Twins

Spatiotemporal Machine Learning Foundations For Urban Digital Twins

Dr Theo Damoulas (Department of Computer Science)


Real-world processes unfold over space and through time in a complex, non-stationary, fashion with non-trivial dynamics and interdependencies.

We also partially observe these through multiple, heterogeneous sensing modalities that have varying signal-to-noise ratios and sampling periods. Furthermore, we constantly act and intervene on these systems, nudging them towards different regimes.

Dr Damoulas will describe our past and ongoing work in this area, as part of his Turing fellowship, aiming to tackle some of these key challenges and set the foundations for digital twinning with an application focus on urban and environmental settings.

Dr Theo Damoulas

Date and time

Date: November 10th, 2021

Time: 10.00 to with 5 minute Q&A

Location: Online - Secure your place

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