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
- Cohort-based training for PhD students
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, training champion) leads the TDA group in the MIF.
- Prof Katie Atkinson (recruitment) is an expert in AI and the dean of EEECS school.
- Prof Marta Garcia-Finana (equality, diversity, inclusion) is an expert in Biostatistics.
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Cohort-based training for PhD students
- Dr Vitaliy Kurlin will lead the following cohort-based training programme in the doctoral network from 8th October 2019.
- Students with complementary skills will be paired (possibly with more senior students) to encourage peer learning, e.g. all PhD students from the EPSRC Centre for Doctoral Training in Distributed Algorithms and TDA group are also welcome.
- The network runs 3-hour sessions on Tuesdays between 11.00-14.00 for PhD students in the network as follows.
- The presentation at 11-12 will be based on a taught module in Data Science or will be given by an external speaker.
- The lunch at 12-12.30 will be provided for free, but should be well-deserved by an active participation in the network.
- The moderated workshop at 12.30-14.00 is for students to present and discuss their solutions to practical exercises.
- The relevant modules are Introduction to Data Science (term 1), Geometric and Topological Data Analysis (term 2).
- Here is the term 1 timetable jointly with DTC in Materials Chemistry, room 2.08, Ashton building in the image above.
- 8 October 2019 : introductions, descriptive statistics
- 15 October 2019 : elements of probability theory
- 22 October 2019 : statistical hypothesis testing
- 29 October 2019 : correlations and linear regression
- 5 November 2019 : geometric classification problems
- 12 November 2019 : clustering problems and methods
- 19 November 2019 : TBA (Dr Vitaliy Kurlin is away)
- 26 November 2019 : vectors, matrices, determinants
- 3 December 2019 : invariants of linear operators
- 10 December 2019 : Principal Component Analysis
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