Artificial Intelligence and Data Science for Health

Developing novel statistical machine learning techniques to improve health and wellbeing through collaboration with the biological and medical sciences

What we do

Our research seeks to develop novel statistical and machine learning methodologies by working in partnership with the biological and medical sciences. Inspired by real applications, we are creating artificial intelligence technologies that can make a real impact on improving human health.

Meet the Team

Researchers

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Christopher Yau

Professor of Artificial Intelligence

Statistical Machine Learning, Computational Genomics, Digital Health

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Andrea Rodriguez Delherbe

Grad Student

Computational Biology

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Charles Gadd

Postdoc

Statistical Machine Learning, Cancer Genomics

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Franziska Gunther

Grad Student

Psychology, Digital Health

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Natalia Hong

Grad Student

Statistical Machine Learning, Public Health

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Woojung Kim

Grad Student

Bayesian Statistics, Machine Learning

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Sara Matijevic

Grad Student

Statistical Machine Learning

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Bhavesh Soni

Grad Student

Computational Biology

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Ellen Visscher

Grad Student

Statistical Machine Learning

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Hanwen Xing

Postdoc

Statistical Machine Learning

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Wenhua Zhou

PhD Student

Risk Prediction Models

Affiliates

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Joel Nulsen

Postdoctoral Researcher

Bioinformatics, Cancer Genomics

Recent Posts

Read the latest news about the group and its members.

MLTC Update

We continue to contribute to the MUM-PREDICT and OPTIMAL project over the last six months including: Artificial Intelligence for …

On the problem of early disease detection

The early detection of diseases is a highly desirable approach for addressing conditions where late stage disease cannot be easily …

MUM-PREDICT: HDRUK Team of the Year

We are proud to have been part of a glittering array of publications arising from our contribution to the MUM-PREDICT projects over the …

Projects

Read about what projects we are involved in.

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MuM-Predict

Research to improve care for pregnant women with two or more health conditions

OPTIMAL

AI for Multiple Long-Term Conditions

Roche ML for Antibody Design

Machine Learning Models for the Development of Antibodies

BIRM-CAM

Bringing Innovative Research Methods to Clustering Analysis of Multimorbidity (BIRM-CAM) project between the Universities of …

Breaking Free with Artificial Intelligence

The Data Science of Digital Behaviour Intervention

Convolutional filtering for mutation signatures

A novel convolutional filter-based approach for learning mutation signature representations.

Health Data Research UK-Turing PhD Programme

To develop future leaders in health data science.

Multi-Omics Network inference

A novel Bayesian nonparametric regression approach for high-dimensionality multi-modal data integration.

Neural Decomposition

Developing interpretable deep neural network approaches for dimensionality reduction that embed functional ANOVA decompositions within …

Precision medicine and individual-level cancer driver genes

Identification of cancer-promoting genetic alterations using genome sequencing and machine learning.

RLevolution

Unravelling the history of genomic instability through deep reinforcement learning.

The Automatic BioData Scientist

Developing automated learning frameworks for reproducible and transferable biological data analysis.

Ovarian Cancer

Computational genomics to tackle the silent killer

Join Us

We are a decentralised group of researchers operating across institutional or disciplinary barriers. We do science to truly benefit the greater good.

We are looking for researchers who buy into this philosophy to become involved with our research activities. You can join as directly as a PhD student or postdoctoral researcher or, alternatively, you can collaborate on a less formal basis as an Affiliate or Visitor.

Postdoctoral

Postdoctoral opportunities are available at the University of Oxford. Please contact the Principal Investigator to enquire about availability.

PhD

Graduate studies within the group can be supported through direct or indirect entry via the following study programmes at the University of Oxford, please see the websites for more details:

Potential applicants are encouraged to contact the Principal Investigator before making applications.

Affiliates / Visitors

If your study programme involves external laboratory placements then we would be very interested to see how we can host and support a visit to our group. We have welcomed numerous visitors from across the world over the years. Contact the Principal Investigator for further information.

The Alan Turing Institute provides an Enrichment programme that allows students to take up to a year of their PhD to be based in London at the Turing Institute. If you would like to apply for the Enrichment scheme and to work with the group during the placement then contact the Principal Investigator.

Contact

  • Professor Christopher Yau, Nuffield Department of Women's & Reproductive Health, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford, OX3 9DU
  • DM Me

Former team members

See what past members and affiliates of the group have done and gone on to do.

Alumni

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Kieran Campbell

Grad Student

Statistical Machine Learning, Genomics

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Constantin Ahlmann-Eltze

Grad Student

Statistical Machine Learning

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Dominic Danks

Grad Student

Statistical Machine Learning

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Donatien Chedom-Fotso

Postdoc

Statistical Machine Learning, Cancer Genomics

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Yun Feng

Grad Student

Statistical Machine Learning, Cancer Genomics

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Zhiyuan Hu

Postdoctoral Researcher

Cancer Genomics, Ovarian Cancer

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Kaspar Maertens

Research Fellow

Statistical Machine Learning

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Paul Kirk

Postdoctoral Research Fellow

Statistic modelling, Approximate Inference, Genomics

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Leon Law

Grad Student

Statistical Machine Learning, Kernel Methods

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Hrvoje Misetic

Grad Student

Bioinformatics, Cancer Genomics

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Emma Pierson

Grad Student

Statistical Machine Learning

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Anas Rana

Lecturer

Statistical Machine Learning, Cancer Genomics

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Tammo Rukat

Grad Student

Statistical Machine Learning

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Siddharth Ramchandran

Visiting Student

Statistical Machine Learning

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Siyu Chen

Grad Student

Statistical Machine Learning, Bioinformatics

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Xiaole Zhang

Alumni

Monte Carlo Methods, Approximate Inference

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Justina Zurauskiene

Postdoctoral Researcher

Statistical Machine Learning, Genomics