Huiqi Yvonne Lu DPhil

Dr Huiqi Yvonne Lu is a researcher and a principal investigator with research expertise in AI for health informatics and computational infrastructure, a Senior Member of IEEE, and a Fellow of the Higher Education Academy. Her research focuses on multimodal time-series modelling, LLM-guided biological modelling, and federated learning (swarm intelligence) for the health monitoring of humans, machines, and the environment, especially on edge and in-network computing devices. She holds a College Lectureship in Engineering at the University of Oxford, and an Honorary Research Fellowship at the George Institute for Global Health, Imperial College London.

Yvonne completed her DPhil at the University of Sussex in pattern recognition and mobile computing, supported by the UKRI Overseas Research Scholarship and the Sussex GTA Scholarship. Her doctoral work was presented as a finalist at the SET for Britain at the UK Parliament and led to a patent.

Following her DPhil, Yvonne undertook postdoctoral research in medical device development and machine learning for biomedical imaging, including breast cancer early diagnostics with electrical impedance tomography at the Oxford John Radcliffe Hospital (funded by GE Health), automated vessel segmentation methods on colour fundus and optical coherence tomography for diabetic retinopathy at the University of Liverpool, and an oxygen-fused MRI biomarker study at the Centre of Cancer Research, University of Manchester.

After a five-year career break, Yvonne returned to academia with a Royal Academy of Engineering Daphne Jackson Fellowship at the University of Oxford (2019—2022), focusing on machine learning for maternal and chronic disease monitoring. She has since received several prestigious national and international research awards, as well as the Oxford Somerville College Fulford Junior Research Fellowship (2020—2023), Oxford MPLS Enterprise and Innovation Fellowship (2022—2023), and Oxford Saïd Business School Idea2Impact Fellowship (2023). She has led and co-led interdisciplinary AI for health projects across academia and industry. In recognition of her academic progression, she was promoted to the Associate Member of the Faculty at the Department of Engineering Science in 2023.

Dr Lu believes in the impact of an innovative end-to-end digital platform for AI for social goods, for everyone, anywhere. Therefore, in 2024, she made a strategic move from the Oxford Computational Health Informatics Lab (led by Prof David Clifton) to the Oxford Computational Infrastructure Group (led by Prof Noa Zilberman) to extend her research in federated intelligence, low-code computing, and in-network machine learning. Meanwhile, she obtained the Worcester College Lecturer in Engineering at the University of Oxford in 2024.

In professional service, Dr Lu serves organising committees for ICLR (PMLDC), NeurIPS (ML4H), IJCAI (KDHD) and is the Tutorial Co-Chair of PHME 2024 and the TPC Chair of PHME 2026. She is Associate Editor (academic) for npj Women’s Health and a Co-Editor of the special Nature collection Advances in AI for Women’s Health, Reproductive Health, and Maternal Care. She also actively contributes to the AI standard. She is a member of the IEEE Standards Committee and a key contributor to the IEEE P3191: Performance Monitoring of Machine Learning-enabled Medical Devices in Clinical Use.

Dr Lu is a STEM Ambassador and advocate for women in engineering.

• Federated Machine learning and health foundation models
• Digital innovations for human, machine and environmental health monitoring
• Time-series multimodal modelling and inference learning
• Physiological and psychological modelling with LLM and human in the loop 
• Robotics and human interactions

Current Projects:

• EU Horizon Semantic Low-code Programming Tools for Edge Intelligence (SMARTEDGE) Project, Computational Infrastructure Group, Department of Engineering Science, University of Oxford. (09/2024—current)
• MItigating the Risk of developing type 2 diabetes Associated with GEstational diabetes (MIRAGE), Diabetes UK PhD Studentship, funded by Diabetes UK, UK. (2023—current)
• Clinical study: “Gestational Diabetes Predictive Monitoring and Management“, University of Oxford, UK, in kind support by the NIHR CRN on NHS data and staff costs. (2021—current)
• Blood Glucose Monitoring for Gestational Diabetes Health and Care: from reactive treatment to preventative medicine, Fellowship project funded by the Royal Academy of Engineering and University of Oxford. (2019—current)

Completed Projects:

• Large Language Model (LLM) to Build Frontline Healthcare Worker Capacity in Rural India, Grand Challenge Project funded by the Bill & Melinda Gates Foundation and the George Institute for Global Health. (2023—2024)
• Oxford Saïd Business School Idea2Impact Fellowship, EMBA module of Innovation Projects and MBA module of Enterprise Finance, University of Oxford, UK. (2023)
• Enterprise and Innovation Research Fellowship, Division of Mathematical, Physical and Life Sciences, University of Oxford, UK. (2021—2022)
• Development and validation of a non-invasive device for measuring oxygen saturation with automatic adjustment according to altitude and skin color using machine learning algorithms, Enterprise Fellowship project led by collaborators in Peru, funded by the Royal Academy of Engineering, UK. (2021—2023)
• Gestational Predictive Monitoring and Management, Oxford John Fell COVID Support Fund, University of Oxford, UK. (2021—2022)
• Wearable Vital Signs Monitoring for Patients with Asthma, Dr Stephanie Dalley Fund for student internship, Somerville College, University of Oxford, UK. (2021)

• Joint-author Best Paper Award, International Conference on Gender and Technology. (2025)
• Runners-up of the NCRM Impact Prize 2023, National Centre for Research Methods (2023)
• Bills and Melinda Gates Foundation Global Challenge Project Award for “Large Language Model (LLM) to Build Frontline Healthcare Worker Capacity in Rural India” (2023)
• UK national winner of the CAETS High Potential Innovations Prizes, nominated by the Royal Academy of Engineering, UK. (2021) Public Engagement Champion, Computational Health Informatics Lab, University of Oxford (2020)
• Royal Academy of Engineering Daphne Jackson Trust Career Re-entry Research Fellowship award (2019)
• National finalist of the SET for Britain, House of Commons, London, UK (2007)
• Research Council (UKRI) Overseas Research Studentship (2005)
• Honourable Mention Award in the USA International Interdisciplinary Contest in Modelling (MCM / ICM), administered by the USA Consortium for Mathematics and Its Applications. (2003)
• First Prize Winner (2002) and Second Prize Winner (2003) of the China National University Student Mathematics Modelling Competition, organized by the Ministry of Education, P.R. China.

Oxford Teaching & Supervision

• Guest Lecturer, Executive Diploma in AI for Business, Saïd Business School (2025—current).

• College Lecturer and Tutor, Mathematics and Engineering Science modules, Worcester College (2024— current).

• Lecturer, Time-Series Analysis and Deep Learning (DPhil), EPSRC CDT in Health Data Science, Department of Computer Science (2022—2023).

• Lab lecturer of B14 Information Engineering, Tutor of B18 Medical Imaging and Wearable Sensors modules, Department of Engineering Science (2021—current). 

• Supervisor of MSc, DPhil, and early-career researchers across the Departments of Engineering Science, Computer Science, Primary Care, and Women’s and Reproductive Health, as well as college summer intership.

• 28/11/2025, "From Sensors to Systems: Building Low-Latency, Privacy-Preserving Patient Health Monitoring with In-Network Machine Learning", Oxford Digital HealthTalk: Oxbridge Women in Engineering Symposium.
• 04/06/2025, "Reimagining Women’s Health with AI: Equity, Intelligence, and Empowerment", Keynote presentation, The Alan Turing Institute and CRASSH Event Series: AI in Women's Health: Bridging Research and Patient Voices.
• 03/02/2025-07/02/2025, Session Co-Chair, Royal Academy of Engineering Frontier Symposium “Catalysing global healthcare: innovations and strategies for scalable impact”, Kenya. Slides
• 01/2025, "Clinical AI and Remote Monitoring for Women with Gestational Diabetes", guest speaker at Alan Turing Institute Clinical AI meetings on AI in Women's Health. Slides
• 10/09/2024 "FemTech and AI for women's health", guest speaker at Cambridge Tech Week's "FemTech and the Women's Health Gap: A Sleeping Giant" meeting. This event was inspired by celebrations earlier in the year to mark 40 Years since the admission of women to Peterhouse College, University of Cambridge. Slides
• 03/07/2024-05/07/2024 "Transformers: from attention mechanisms to large language models", Tutorial Co-Chair and Speaker at PHME 2024. Slides available upon request. 
• 04/02/2024 “Craft Your Path: Using the Business Canvas for Academic Projects and Career Goals”, invited talk in the Generation Programme for the high school students, Oxford Suzhou Advanced Research Centre. Slides available upon request.
• 17/11/2023 “Machine Learning in Medical Devices for Diabetic Blood Glucose Monitoring”. Case study talk at the IEEE SA P3191 Working Group of machine learning-enabled medical device (MLMD) in clinical use. Slides available upon request.
• 14/06/2023 Somerville MCR-SCR symposium:”Clinical Machine Learning and Artificial Intelligence in Medicine”
• 03/10/2023 Freshers’ tutorial induction talk by invitation, Somerville College, University of Oxford.
• 15/02/2023 Academic outreach talk: “Machine Learning for the Next Generation of Health Informatics”, Somerville College, University of Oxford.
• 09/12/2022 Co-Chair of NCRM National Workshop of Complex clinical data and Gestational Diabetes Mellitus, Oxford, UK.
• 09/11/2022 “Clinical Machine Learning in Gestational Diabetes Monitoring”, presentation at the Daphne Jackson Trust Annual Conference, Royal Society, UK.
• 09/07/2022 “Machine learning methods — the essentials”, Tutorial at the European Conference of the Prognostics and Health Management Society. Video link.
• 23/06/2022 International Women in Engineering flash interview at the Department of Engineering Science, University of Oxford. Video link.
• 16/05/2022 Lightning talk by invitation: “Machine Learning for the Next Generation of Health Informatics – A journey in gestational diabetes”, Royal Academy of Engineering Annual Founder’s Day.
• 11/05/2022 Academic outreach talk: “Machine Learning for the Next Generation of Health Informatics”, Somerville College, University of Oxford.
• 01/12/2021 Academic outreach talk: “Machine Learning for the Next Generation of Health Informatics”, Somerville College, University of Oxford.
• 10/11/2021 Tutorial by invitation: “Challenges in Data Science Application in Healthcare”, PHME.
• 26/02/2021 Somerville MCR-SCR symposium: “Clinical Machine Learning in Patient Health and Care – at a turning point”, Somerville College, University of Oxford.
• 10/11/2020 Tutorial by invitation:”Machine Learning for the Next Generation of Health Informatics”, US Conference of the Prognostics and Health Management Society.
• 09/07/2020 Tutorial by invitation:”Machine Learning for the Next Generation of Health Informatics”, PHME.
• 09/07/2020 Standard Committee Panel Talk by invitation: “Regulatory Framework on Artificial Intelligence”, European Conference of the Prognostics and Health Management Society (PHME). Slides
• 17/11/2020 Thinktank talk by invitation: “Machine Learning for the Next Generation of Health Informatics”, UCB, Belgium.
• 29/06/2020 Tutorial by invitation:”Machine Learning for the Next Generation of Health Informatics”, International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2020.
• 30/05/2020 “Blood Glucose Monitoring for Gestational Diabetes Health and Care”, Oxford Women in Computer Science Lightning Presentation.

Leago Sebesho is a medical doctor and Rhodes Scholar pursuing an MSc in Applied Digital Health at the University of Oxford. She combines clinical experience with interdisciplinary research at the convergence of digital health and health systems.

Her work focuses on enhancing health system equity and sustainability through digital innovation. For her dissertation, she collaborates with the Department of Engineering Science on the SUSTAIN project. This project will explore methodologies for transforming unstructured multimodal healthcare data into structured data, with a focus on UK NHS primary care records.

Leago aims to shape health policy through evidence-based research and implementation strategies that strengthen healthcare delivery, particularly in resource-limited environments. Outside academia, she is committed to community engagement and youth development. She enjoys reading literature, exploring new destinations, and following sports.

Yasmina is a third year DPhil student from Beirut, Lebanon. She is a recipient of an industrial CASE (iCASE) studentship and is mentored by an interdisciplinary team at Oxford and EMIS, as her industry partner. Prior to attending Oxford, she studied Bioengineering and Psychology at the University of Pennsylvania. She then completed the MPhil in Population Health Sciences with a focus on Health Data Science at the University of Cambridge. For her DPhil, she combines her engineering background with her passion for improving reproductive health to develop predictive models of adverse outcomes of gestational diabetes using electronic health records, and large-language models for women's health.