Sohan Seth

Sohan Seth

Senior Data Scientist

University of Edinburgh


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.



  • 14.03.2022: Abbas Rizvi joins as PhD Student
  • 07.03.2022: Anirban Chakraborty joins as PDRA
  • 03.03.2022: Paper accepted at Scientific Reports
  • 01.03.2022: Paper accepted at Journal of Separation Science
  • 28.02.2022: Karthik Mohan joins as Data Scientist
  • 28.02.2022: Abstract accepted at EGU'22
  • 28.02.2022: Paper accepted at Scientific Data
  • 14.02.2022: Paper accepted at Scientific Reports
  • 14.12.2021: Sam Fielding starts as a PhD student
  • 13.12.2021: PDRA position available in bioinformatics in critical care
  • 26.11.2021: PhD position available in machine learning in critical care
  • 22.11.2021: Daga Panas joins as a Data Scientist
  • 12.10.2021: PhD position available in predictive models in unscheduled care
  • 12.10.2021: PhD position available in longitudinal clustering in multimorbidity
  • 24.09.2021: Associate Editor for Frontiers in AI: Medicine and Public Health
  • 02.09.2021: Lara Johnson starts as PhD student in ACRC
  • 17.08.2021: Abstract accepted at ICUr'21
  • 11.08.2021: PDRA position available in Unsupervised & Explainable Learning
  • 05.08.2021: Nikos Avramidis starts as PhD student
  • 23.07.2021: Data Scientist position available at the Data Science Unit
  • 17.08.2021: Paper accepted at BMJ
  • 20.05.2021: Nick Homer starts as PhD student
  • 25.03.2021: Paper accepted at AI4PH at ICLR'21 workshop
  • 22.03.2021: Population Estimation Project in Scotland’s AI Strategy
  • 05.03.2021: Principal’s Medal as part of StopCovid Team
  • 15.12.2020: Chima Eke starts as PDRA with ACRC
  • 14.09.2020: PDRA position available at Informatics
  • 14.09.2020: Isaac Neal starts as RA
  • 14.09.2020: Nia Jenkins starts as RA
  • 07.09.2020: Alex Adams starts PhD in Precision Medicine
  • 17.08.2020: Paper accepted at The BMJ
  • 29.07.2020: PDRA position available in GeoSciences
  • 15.07.2020: PDRA position available in Chemistry
  • 23.01.2020: Paper accepted at iScience
  • 17.09.2018: Paper accepted at Bayesian Analysis
  • 19.01.2018: Cover Story in Journal of Imaging
  • 18.12.2017: Paper accepted at Journal of Imaging




Anirban Chakraborty

Research Associate

Information Retrieval, Contextual Recommendation, Machine Learning


Chima Eke

Research Associate

Machine Learning, Data Science


Daga Panas

Data Scientist

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


Karthik Mohan

Data Scientist

Data Science, Machine Learning, Artificial Intelligence, NLP

Grad Students


Abbas Rizvi

PhD Student (with Pankaj Pankaj)

Failure Prediction, Machine Learning, Healthcare Technologies


Alex Adams

Precision Medicine PhD Student

Machine Learning, Precision Medicine, Healthcare Technologies


Lara Johnson

ACRC Phd Student

Statistics, Machine Learning, Ageing, Healthcare Technologies


Nick Homer

SENSE PhD Student (with Robert Bingham)

Remote Sensing, Computer Vision, Climate Science


Nikos Avramidis

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

Bioinformatics, Genomics


Samuel Fielding

Heliophysics SENSE PhD Student (with Kathy Whaler)

Machine Learning, Space Weather, Satellite Data



Isaac Neal

Research Assistant

Machine Learning


Nia Jenkins

Research Assistant

Machine Learning


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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