Doctoral Network in Artificial Intelligence for Future Digital Health is a doctoral training centre funded by the University of Liverpool from October 2019 to train the next generation of world-leading experts in Artificial Intelligence to solve data intensive problems in healthcare.
- Vision of the doctoral network
- Leadership team of the network
- Call for PhD projects from October 2021
- PhD projects : 2019 cohort | 2020 cohort
- PhD training : Spring 2021 | Autumn 2020 | Spring 2020 | Autumn 2019
Vision of the doctoral network
- The network will create a community of AI health care experts who will develop new AI tools for medical applications.
- The vision is to establish a world-class centre providing high-quality doctoral training in AI for Future Digital Health.
- The cohort-based training in a collaborative environment will feature peer-to-peer and cohort-to-cohort learning.
- Each PhD project is carefully co-created in collaboration with a health provider and/or a healthcare commercial partner.
- On completion students will be well-placed to take up rewarding careers within the domain of AI and Digital Health.
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Leadership team of the network
- Prof Frans Coenen (director) supervised 50+ successful PhDs in Computer Science.
- Dr Vitaliy Kurlin (co-director for the PhD training) leads the TDA group in the MIF.
- Prof Marta Garcia-Finana (equality, diversity, inclusion) is an expert in Biostatistics.
- Dr Paolo Paoletti is a Senior Lecturer in Control in the School of Engineering.
- Dr Roberto Ferrero is a Lecturer in Electrical Engineering and Electronics.
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Call for supervisors to submit PhD projects to start in October 2021
- Deadline : 23:59 GMT on 22 January 2021
- E-mail all proposals to Prof Frans Coenen
- Call for PhD projects in 2021 (doc, 46K)
- Example project proposal in 2019 (pdf, 122K)
- Blank advert for FindAPhD.com in 2021 (doc, 192K)
- Example advert for FindAPhD.com in 2019 (pdf, 94K)
PhD projects: first cohort from Autumn 2019
| Student : Muizdeen Raji. Supervisors :
Nagesh Kalakonda,
Vitaliy Kurlin,
Joseph Slupsky. Project : A study of cellular diversity in health and disease using mass cytometry. Partner : Clatterbridge Cancer Centre, Wirral. |
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Student : Peter Wright. Supervisors :
Paolo Paoletti,
Shan Luo,
Iain Hennessey.
Project : Machine Learning for the next generation of paediatric wheelchairs.
Partner : Alder Hey Children's Hospital, Liverpool. -
Student : Omar Elnaggar. Supervisors :
Shan Luo,
Paolo Paoletti,
Andrew Hopkinson.
Project : Deep Feature Learning for Distributed Rehabilitation Robots with Wearable Tactile Sensing.
Partner : Sensor City, Liverpool. -
Student : Aidan Watmuff. Supervisors :
Mark Green,
Daniel Arribas-Bel,
Vitaliy Kurlin,
Huw Jenkins.
Project : Using AI to leverage new forms of data in modelling cycling behaviours in the Liverpool City Region.
Partner: Liverpool City Region Combined Authority. -
Student : Weiqiang Chen. Supervisors :
Yaochun Shen,
Yalin Zheng,
Stephen Kaye.
Project : An intelligent imaging technology for automatic characterisation of the refractive power of human eye.
Partner: Royal Liverpool and Broadgreen University Hospital Trust. -
Student : Yanhua Xu. Supervisors :
Dominik Wojtczak,
Neil French,
Roberto Vivancos.
Project : Machine Learning of Epidemic Models.
Partner : Public Health England. -
Student : Jing Qi. Supervisors :
Girvan Burnside,
Frans Coenen,
Paul Charnley.
Project : An Intelligent Assistant for Medical Doctors when Prioritising Pathology Results.
Partner: Wirral University Teaching Hospital. -
Student : Mohamed Elhalwagy. Supervisors :
Frans Coenen,
Philip Johnson,
Vinzent Rolny.
Project : Application of AI systems to develop novel diagnostic and prognostic tools for Hepatocellular Carcinoma.
Partner : Roche Diagnostics.
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PhD projects: second cohort from Autumn 2020
- Project :
Application and development of machine learning approaches for automated analysis of brain tumours.
Supervisors : Martin Gairing, Andy Jones, Shivaram Avula.
Partner: Alder Hey Children's Hospital, Liverpool. -
Project :
Rule Learning from Data - Automated Report Writing from Medical Microbiology Data.
Supervisors : Stuart Thomason, Yalin Zheng, Alex Howard.
Partner: Liverpool University Hospital Foundation Trust. - Project :
Towards `Hospices Without Walls'; How Could Human Centred Design and Robotics facilitate `Enhanced Independence' and `Alternative Access' in Future Palliative Care Scenarios?
Supervisors : Farnaz Nickpour, Paolo Paoletti, Sebastiano Fichera.
Partner: Marie Curie Hospice, Liverpool. - Project :
Machine learning approaches for the clinical diagnosis of autoimmune disease.
Supervisors : Anna Fowler, Peter Green, Elizabeth Soilleux.
Partner: Addenbrooks Hospital, Cambridge. - Project : A rigorous identification of all metastable polymorphs for better and safer drugs.
Supervisors : Vitaliy Kurlin, Andy Cooper, Angeles Pulido.
Partner: Cambridge Crystallographic Data Centre.
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Cohort-based training for PhD students
- Dr Vitaliy Kurlin leads the following cohort-based PhD training programme in the doctoral network from October 2019.
- Students with complementary skills can be paired to encourage peer learning, e.g. PhD students from the networks DTC Materials Chemistry, EPSRC CDT in Distributed Algorithms, Technologies for Healthy Ageing, DSTA group are welcome.
- If you would like to join or supervise PhD students who need will benefit from our training, please e-mail Dr Vitaliy Kurlin.
- Anonymous student feedback (pdf, 1M) on all lectures and tutorials by Dr Vitaliy Kurlin and Dr Olga Anosova in 2019.
- In Autumn 2020 the training will be between 9-12 on Thursdays from 8th October 2020 via MS Teams.
- 09.00 - 09.50 : a lecture on AI or Data Science topics given by Dr Vitaliy Kurlin or colleagues.
- 10.00 - 10.50 : a tutorial on the topic of the lecture given by Dr Vitaliy Kurlin or PhD students.
- 11.00 - 11.50 : 20-min presentations by PhD students followed by feedback and discussions.
- The underpinning modules are Introduction to Data Science and Advanced Data Science.
- All slides are in the section Files of the MS Team "PhD training in AI and Data Science".
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Advanced topics in Data Science and AI covered in Spring 2021
- 11 February 2021 : correlation and regression
- 18 February 2021 : matrices of linear maps
- 26 February 2021 : invariants of linear maps
- 4 March 2021 : how to change a linear basis
- 11 March 2021 : Principal Component Analysis
- 18 March 2021 : graph representations
- 25 March 2021 : graph visualisations
- 15 April 2021 : single-edge clustering
- 22 April 2021 : Voronoi diagrams of clouds
- 29 April 2021 : skeletons of point clouds
Introductory topics in Data Science and AI covered in Autumn 2020
- 8 October 2020 : introduction to Data Science (Dr Vitaliy Kurlin)
- 15 October 2020 : descriptive statistics (Dr Vitaliy Kurlin)
- 22 October 2020 : introduction to probability (Dr Vitaliy Kurlin)
- 29 October 2020 : probabilistic paradoxes (Dr Olga Anosova)
Talk 'Learning to Prioritise Pathology Data in the Absence of a Ground Truth’ by PhD student Jing Qi
Talk `Wearable Sensing for Non-invasive Human Pose Estimation during Sleep' by PhD student Omar Elnaggar - 5 November 2020 : probability distributions (Dr Olga Anosova)
Talk ‘The Maintenance of Trials Methodology Research Using Machine Learning’ by PhD student Iqra Muhammad - 12 November 2020 : statistical hypotheses (Dr Olga Anosova)
Talk `Machine learning for influenza A viral host classification' by PhD student Yanhua Xu - 19 November 2020 : equivalences and metrics (Dr Vitaliy Kurlin)
Talk `Machine learning for mass cytometry data of chronic lymphocytic leukemia' by PhD student Muizdeen Raji - 26 November 2020 : Earth Mover's distance (Cameron Hargreaves)
Talk `Using k-modes clustering to identify different types of cyclists' by PhD student Aidan Watmuff - 3 December 2020 : talk `Biomarkers-based detection of liver cancer' by PhD student Mohamed Elhalwagy
- 10 December 2020 : introduction to Bayesian statistics (Dr Olga Anosova)
- 17 December 2020 : frequentist vs Bayesian approaches (Dr Olga Anosova)
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Advanced topics in Data Science and AI covered in Spring 2020
- 6 February 2020 : the Bayes theorem with examples
- 13 February 2020 : AI for Health (Prof Frans Coenen),
Student presentations by Matthew Carter and Vincent Beraud - 20 February 2020 : conditional probabilities (Dr Olga Anosova)
- 27 February 2020 : frequentist vs Bayesian (Dr Olga Anosova),
Student presentations by Jing Qi and Theofilos Triommatis - 12 March 2020 : graph representations (with a tutorial by Dr Olga Anosova)
- 19 March 2020 : graph classifications (online between 11.00-12.40 via Zoom)
- 26 March 2020 : graph visualisations (online between 11.00-12.40 via Zoom)
- 30 April 2020 : single-edge clustering of point clouds (online at 10.00)
- 7 May 2020 : Voronoi diagrams of point clouds (online at 10.00)
- 14 May 2020 : skeletons of point clouds (online at 10.00)
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Introductory topics in Data Science and AI covered in Autumn 2019
- 8 October 2019 : descriptive statistics
- 15 October 2019 : probability theory
- 22 October 2019 : statistical hypotheses
- 29 October 2019 : correlation and regression
- 5 November 2019 : clustering problems and k-means
- 12 November 2019 : equivalence relations and vectors
- 19 November 2019 : linear maps (Dr Olga Anosova)
- 26 November 2019 : invariants of linear maps
- 3 December 2019 : how to change a linear basis
- 10 December 2019 : Principal Component Analysis
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