Hao Ni, University College London

Tentative title: “Signature-based ML models help sepsis prediction despite the subtle effects of label extraction

Hao Ni is a Professor of Mathematics at University College London (UCL) and a Turing Fellow at The Alan Turing Institute. She finished her DPhil in Mathematics at the University of Oxford. She held postdoctoral positions at ICERM and Department of Applied Mathematics at Brown University (2012 – 2013) and the Oxford-Man Institute of Quantitative Finance (2012 – 2016). She was an associate professor at the financial mathematics group, UCL from 2016 to 2022. Her research interests include stochastic analysis, machine learning and their applications. More specifically, she is interested in non-parametric modelling effects of complex multi-modal data streams through rough path theory and machine learning. Moreover, she has research interests on real-world applications, such as human-computer interface, computer vision and quantitative finance.

Timothy Rittman, Cambridge University

Keynote Title: “AI as the future of memory clinics: hype or happening?

Timothy Rittman is a Senior Clinical Research Associate at the University of Cambridge where he studies neurodegenerative disorders, combining neuroimaging, cognitive assessments and neuropathology to understand how these diseases progress through the brain. He has a particular interest in translating methods from artificial intelligence and big data for use in memory clinics. Tim co-leads the DEMON dementia network’s Imaging Working group and is an adviser to the World Young Leaders in Dementia. He is an Honorary Consultant Neurologist at Addenbrookes hospital, as a consultant in the Addenbrookes Memory Clinic, leading a clinic for people with Progressive Supranclear Palsy and Corticobasal Degeneration, and co-leading a dementia genetics clinic.

Konstantinos Kamnitsas, Oxford University

Keynote Title: “Frameworks for reliable deployment of AI in medical imaging”

Konstantinos Kamnitsas is Associate Professor of Engineering Science (Medical Imaging) at the Department of Engineering of the University of Oxford, and Non-Tutorial Fellow at Wolfson College. He is co-director of the EPSRC CDT in Healthcare Data Science (2024- ). His research focuses on Machine-Learning (ML) and primarily deep neural networks for medical image analysis. His work has two main goals: 1) Develop reliable, transparent and accountable AI models for safe use in healthcare; 2) Empower radiologists, clinicians and researchers with intelligent ML-based tools to better address their research questions and needs of clinical workflows.
Konstantinos completed his PhD at Imperial College London in 2019, where he pioneered development of 3-dimensional neural networks for analysing volumetric medical data, such as MRI and CT, and methods for improving generalization to heterogeneous data. His work won various awards, among which international competitions for segmentation of cancer and stroke lesions. He previously obtained an MSc in Computing Science from Imperial College, and Diploma in Electrical and Computer Engineering from Aristotle University of Thessaloniki, Greece. He has also conducted research in industry, such as at Microsoft Research and Kheiron Medical Technologies. He became Lecturer of Computer Science at the University of Birmingham in 2021, before moving to Oxford in 2022. He sits on the Editorial Board of the Medical Image Analysis (MedIA) journal.

Jacques Fleuriot, Edinburgh University

Jacques Fleuriot is a full Professor in the School of Informatics and hold a Chair of Artificial Intelligence. He is head of the AI Modelling Lab (AIML), within the Artificial Intelligence and its Applications Institute (AIAI), Academic Lead and a member of the core management team for the University of Edinburgh’s £20m Advanced Care Research Centre (ACRC). As part of the ACRC, Jacques lead the Integrated Technologies of Care research theme. He is the AI Lead on the NIHR Grant, Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC), and he is member of the Strategic Opportunities and Futures Board of the newly created University of Edinburgh’s £7.5m Centre for Investing Innovation. His main field of research lies in AI Modelling, which spans areas such as interactive theorem proving, formal verification, process modelling, and AI/machine learning applied to health/care, medicine and other complex domains.

Eiichiro Tanaka, Waseda University, Japan

Keynote title: “Development of life support devices by using Inclusive design

Eiichiro Tanaka is a full Professor at the Graduate School of Information, Production and Systems, Faculty of Science and Engineering, Waseda University, Japan. His research interests include mechanical design, mechanics, mechanical elements, and welfare engineering. He completed his doctoral program at the Tokyo Institute of Technology in 2003, and after working as a researcher at the Mechanical Engineering Research Laboratory of Hitachi, Ltd., he changed to an academic career.  He has been in his current position since 2016.
His current main research interests are damage diagnosis for mechanical elements, especially gears, and the development of various life support devices. He has developed RE-Gait (R), a walking training assit robot for hemiplegic patients, and e.z.UP (R), a lifting motion assit suit for various workers, for which he has received many awards. These products have already been commercialized and sold in Japan, and are already helping many patients, caregivers, and workers in factories and logistics.
The title of this talk is “Development of life support devices by using Inclusive design”. From the start of development, he has been developing various devices that assist human movement, discussing them not only with engineers but also with the people who will actually use the devices, such as patients, their families, and medical staff. However, in order to effectively carry out neurorehabilitation, it is important to provide assistance that is sensitive to the feelings of the subject, rather than simply providing physical assistance. He used neural network technology to estimate the subject’s emotions and fatigue from biological signals in real time, and built a system that induces emotions into a pleasurable and arousal state.He has confirmed its effectiveness and will introduce it in this keynote.