Sohan Seth

Sohan Seth

Lead Data Scientist

University of Edinburgh

Biography

I am the Lead Data Scientist (Senior Research Fellow equivalent) at the School of Informatics, University of Edinburgh. I lead the Data Science Unit (DSU) for Science, Health, People and Environment (SHaPE).

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Researchers

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Anirban Chakraborty

Research Associate

Information Retrieval, Contextual Recommendation, Machine Learning

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Chima Eke

Research Associate

Machine Learning, Data Science

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Daga Panas

Data Scientist

Machine Learning, Artificial Intelligence, Reinforcement Learning, Computational & Theoretical Neuroscience, Sleep

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Karthik Mohan

Data Scientist

Data Science, Machine Learning, Artificial Intelligence, NLP

Grad Students

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Abbas Rizvi

PhD Student (with Pankaj Pankaj)

Failure Prediction, Machine Learning, Healthcare Technologies

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Alex Adams

Precision Medicine PhD Student

Machine Learning, Precision Medicine, Healthcare Technologies

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Iris Ho

CDT Biomedical AI student

Multimorbidity, Trajectory, Machine Learning

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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

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Samuel Fielding

Heliophysics SENSE PhD Student (with Kathy Whaler)

Machine Learning, Space Weather, Satellite Data

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.

Recent Publications

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Distinct clinical symptom patterns in patients hospitalised with COVID-19 in an analysis of 59,011 patients in the ISARIC-4C study

Abstract COVID-19 is clinically characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. …

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