Sohan Seth

Sohan Seth

Senior Data Scientist

University of Edinburgh

Biography

I am a Senior Data Scientist at the School of Informatics, University of Edinburgh. I lead the Data Science Unit. My research interests include data science for science, health, people and environment. I organize the Data Science Clinic, and Data to Discovery seminar series.

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Researchers

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Chima Eke

Research Associate

Machine Learning, Data Science

Grad Students

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Alex Adams

Precision Medicine PhD Student

Machine Learning, Precision Medicine, Healthcare Technologies

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Lara Johnson

ACRC Phd Student

Statistics, Machine Learning, Ageing, Healthcare Technologies

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Nick Homer

SENSE PhD Student (with Robert Bingham)

Remote Sensing, Computer Vision, Climate Science

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Nikos Avramidis

One Health Models of Diseases PhD Student (with Kenneth Baillie)

Bioinformatics, Genomics

Alumni

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Isaac Neal

Research Assistant

Machine Learning

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Nia Jenkins

Research Assistant

Machine Learning

Projects

Understanding Clusters of Multimorbidity using Machine Learning

Develope stable, consistent, operationalisable, reproducible, and explaninable clusters of multimorbidities. We will analyze these clusters in Clinical Practice Research Datalink and validate them in DataLoch to find their genetic basis and in Scottish Longitudinal Study to infer their socio-economic basis.

Child Poverty and Access to Services in Uganda

Develop understanding on the causes of childhood poverty. We are focussing on whether children’s physical accessibility to services, such as health facilities or schools contribute to their deprivation.

Interpretable Descriptors for Materials Machine Learning

Develop new concise and informative crystal structure descriptors, focussed on maintaining chemical interpretability and generalisability.

Census-Independent Population Density Estimation in Mozambique

Develop sustainable machine learning models to improve intercensal population estimates in Mozambique. We are using satellite images and microcensus data to estimate population in rural and semi-unban areas independent of census data.

Data-driven Insight and Prediction in Later Life Care

Develop, validate and disseminate a suite of new risk prediction models for a set of adverse outcomes such as mortality, increased care needs or hospitalisation. In the context of population ageing and resource constrained services, risk prediction tools have great potential to ensure the delivery of the right care to the right person in the most cost-effective way.

Machine Learning for Spectroscopy

Develop machine learning models for analysing fluorescence, Raman, and time-resolved spectroscopy signals in the context of interventional pulmonology, in particular cancer delineation.

Segmentation of Micro-tomography Images for Understanding Deformation of Rocks

Develop segmentation algorithms for very large scale 4D microtomography images without human supervision. The segmented images will inform critical parameters to understand the mechanism of rock deformation.

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