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
- PhD projects : current and future
- PhD training : Autumn 2019, Spring 2020
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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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,
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 (application deadline 12 April 2020)
- Stipend 15,285 GBP per year (53,497.50 GBP over 3.5 years) according to the UKRI guidelines
- Tuition Fees (at the UK/EU level) : 4,327 GBP per year (15,144.50 GBP over 3.5 years)
- Conference and travel expenses : 1,000 GBP per year (3,500 GBP over 3.5 years)
- Project :
Application and development of machine learning approaches for automated analysis of brain tumours.
Supervisors : Dr Martin Gairing, Prof Andy Jones, Dr Shivaram Avula.
Partner: Alder Hey Children's Hospital, Liverpool. -
Project :
Rule Learning from Data - Automated Report Writing from Medical Microbiology Data.
Supervisors : Dr Stuart Thomason, Dr Yalin Zheng, Dr 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 : Dr Farnaz Nickpour, Dr Paolo Paoletti, Dr Sebastiano Fichera.
Partner: Marie Curie Hospice, Liverpool. - Project :
Machine learning approaches for the clinical diagnosis of autoimmune disease.
Supervisors : Dr Anna Fowler, Dr Peter Green, Dr Elizabeth Soilleux.
Partner: Addenbrooks Hospital, Cambridge. - Project : A rigorous identification of all metastable polymorphs for better and safer drugs.
Supervisors : Dr Vitaliy Kurlin, Prof Andy Cooper FRS, Dr 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 8th 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 and TDA group are welcome.
- Dr Vitaliy Kurlin runs weekly training sessions between 11.00-14.00 for 25+ PhD students from 3 networks as follows.
- In the spring 2020 the session are in George Holt building, room 223 on Thursdays (online from 19th March 2020).
- The presentation at 11-12.00 will be on AI or Data Science topics given by Dr Vitaliy Kurlin or another speaker.
- The lunch at 12.00-12.30 is provided for free, but should be well-deserved by an active participation in learning.
- The moderated workshop at 12.30-14.00 is for students to discuss exercises or present on-going PhD projects.
- The underpinning modules are Introduction to Data Science (semester 1), Advanced Data Science (semester 2).
- All past slides are in the folder "PhD training resources" on the VITAL web page of the module COMP 229.
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Introductory topics in Data Science 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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Advanced topics in AI and Data Science 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
- 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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