Dr
Huiqi Yvonne Lu
DPhil
Associate Member of Faculty
Associate Fellow of Somerville College
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Email:
Tel: 01865 617670
College: Somerville College
Location: Institute of Biomedical Engineering
Research Building: Old Road Campus Research Building

Huiqi Yvonne Lu is a biomedical data scientist, an Associate Member of Faculty and the Co-Chair of Researchers Committee at the Department of Engineering Science, University of Oxford. She also holds an honorary Research Fellow position at the George Institute for Global Health, Imperial College London. Her research focuses on clinical machine learning, sensor signal processing, and wearable devices for patient monitoring, especially on digital health innovations for global women’s health and chronic health conditions such as diabetes. Her current research interest is to develop health foundation model for time-series data, and exploring the feasibility of using meta-learning with large language models for explainable AI for health monitoring, disease discovery, thereby reduce digital health disparities, especially for LMICs. One of her recent research adventures is to develop reasoning-informed model to enhance clinical capacity in India using large language models, funded by the Bills and Melinda Gates Foundation Global Challenge Grant and the George Institute for Global Health.

Dr Lu obtained her DPhil in mobile computing and pattern recognition at the Centre of Signal Processing and Industrial Informatics, University of Sussex, UK, sponsored by the UKRI Oversea Research Student Scholarship and the Sussex GTA Scholarship. During her doctoral study, she developed a commercial iris-identification system for mobile devices, which led to a patent and her work was presented at the SET for Britain, UK Parliament. After finishing her DPhil, Dr Lu moved onto the biomedical & clinical research on electrical impedance tomography for breast cancer at the Oxford John Radcliff Hospital (funded by GE Health) and diabetic retinopathy imaging at the Institute of Chronic Diseases, University of Liverpool. In 2019, after a five-year career break, Dr Lu joined the Institute of Biomedical Engineering at the University of Oxford with a Daphne Jackson Trust Career Re-entry Research Fellowship, sponsored by the Royal Academy of Engineering and the University of Oxford. Her Fellowship project focused on the development of machine learning methods for robustly tracking patient condition using home-monitoring systems for chronic disease, with a special focus on maternal health and diabetes. During her journey at the Computational Health Informatics (CHI) Lab, with great honour, Dr Lu was mentored by Prof David Clifton (AI in health — Engineering Science) and Prof Lucy MacKillop (clinical — Oxford University Hospitals and and industrial — EMIS Health). She was honored to attain the Somerville College Fulford Junior Research Fellowship (2020-2023), the Oxford MPLS Enterprise and Innovation Research Fellowship (2021-2022), and the Oxford Saïd Business School Idea2Impact Research Fellowship (2023). In recognition of her academic progression, she was promoted to the Associate Member of Faculty in 2023. Dr Lu has led and co-led clinical AI and mobile biometrics projects in both academic and commercial settings, and filed one patent.

Dr Lu is an Associate Editor of Nature npj Women’s Health, a Chief Editor of the special collection of Advances in AI for women’s health, reproductive health, and maternal care: bridging innovation and healthcare, and a guest editor of Frontier Signal Processing. She has served as a workshop committee member and junior round table chair at at notable conferences, including ICLR (PMLDC), NeurIPs (ML4H), IJCAI(KDHD), and the PHME. Dr Lu is an active contributor in the IEEE Standard Committee for P3191: Performance Monitoring of Machine Learning-enabled Medical Device in Clinical Use.

  • Machine learning and health foundation models
  • Digital health innovations for patient monitoring
  • Signal processing on time-series sensor data
  • Physiological and psychological modelling with inference learning and LLMs
  • Robotics and human interactions

Computational Health Informatics Lab

Current Projects:

  1. Large Language Model (LLM) to Build Frontline Healthcare Worker Capacity in Rural India, Grand Challenge Project funded by the Bills & Melinda Gates Foundation and the George Institute for Global Health. (2023 — Current)
  2. MItigating the Risk of developing type 2 diabetes Associated with GEstational diabetes (MIRAGE), Diabetes UK PhD Studentship, funded by Diabetes UK, UK. (2023-2026)
  3. Clinical trial: “Gestational Diabetes Predictive Monitoring and Management“, University of Oxford, UK, in kind support by the NIHR CRN on NHS data and staff costs. (2021 — 2024)
  4. 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:

  1. Oxford Saïd Business School Idea2Impact Fellowship, EMBA module of Innovation Projects and MBA module of Enterprise Finance, University of Oxford, UK. (2023)
  2. Enterprise and Innovation Research Fellowship, Division of Math, Physics and Life Science, University of Oxford, UK. (2021 — 2022)
  3. 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 lead by collaborators at Peru, funded by the Royal Academy of Engineering, UK. (2021 — 2023)
  4. Gestational Predictive Monitoring and Management, Oxford John Fell COVID Support Fund, University of Oxford, UK. (2021 — 2022)
  5. Wearable Vital Signs Monitoring for Patients with Asthma, Dr Stephanie Dalley Fund for student internship, Somerville College, University of Oxford, UK. (2021)

Selected Research Papers:

  1. Lu, H.Y., Lu, P., Hirst, J.E., Mackillop, L., Clifton, D.A. ‘A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus’, Sensors 2023, 23, 7990. doi:10.3390/s23187990
  2. Ghadban Y., Lu, H, Adavi U., Gara, S., Ankita Sharma, A., John, R., Praveen D., Hirst, J., ‘Transforming Healthcare Education: Harnessing Large Language Models for Frontline Worker Capacity Building using retrieval-augmented generation’, NeurIPs workshop in LLM for Education, 2023. doi: 10.1101/2023.12.15.23300009
  3. Lu, H., Ding, X., Hirst J., Yang, Y., Yang, J., Mackillop L., Clifton, D., ‘Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes’, IEEE Review of Biomedical Engineering, 2023. doi: 10.1109/RBME.2023.3242261
  4. Lu, P., Creagh, A.P., Lu, H.Y., Hai, H.B., VITAL Consortium; Thwaites, L., Clifton, D.A., ‘2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries’, Sensors 2023, 23, 7705. doi:10.3390/s23187705
  5. Hirst, J., Lu, H., Mackillop, L., Al Ghadban, Y., Saravanan, P. and White, S., ‘Complex clinical data and Gestational Diabetes Mellitus‘, National Centre for Research Methods, 2023. doi: 10.5258/NCRM/NCRM.00004930
  6. Xin, Q. -Y., Pei, Y. -C., Lu, H., Clifton, D., Wang, B., Chuan, Q., Luo, M. -Y., ‘A distribution-based selective optimization method for eliminating periodic defects in harmonic signals’, Mechanical Systems and Signal Processing, 185, 2023. doi:10.1016/j.ymssp.2022.109781
  7. Talyor, L., Ding, X., Clifton, D., Lu, H., ‘Wearable vital signs monitoring for patients with asthma: a review’, IEEE Sensors Journal, 02/2023. doi:10.1109/JSEN.2022.3224411
  8. Lu, H., Hirst, J., Yang, J., Mackillop, L., Clifton, D., ‘Standardising the assessment of caesarean birth using an oxford caesarean prediction score for mothers with gestational diabetes’, Healthcare Technology Letters 9 (1-2), 1-8, 2022. doi:10.1049/htl2.12022
  9. Yang, J., Clifton, D., Hirst, J., Mackillop, L., Lu, H., ‘Machine Learning-Based Risk Stratification for Gestational Diabetes Management‘, Sensors, 22 (13), 4805, 2022. doi:10.3390/s22134805
  10. Pei, Y., Wu, J., Wang, B., Wang, C., Guan, J., Lu, H., ‘A Machine Learning Empowered Shape Memory Alloy Gripper with Self-sensing of Displacement-Force-Stiffness’, IEEE Transactions on Industrial Electronics, 2022. doi:10.1109/tie.2022.3222655

Selected Book Chapter: HY Lu, MedMetrics: Biometrics Passports in Medical and Clinical Healthcare That Enable AI and Blockchain. Recent Advances in Biometrics, IntechOpen. Link

Full publication list (including publications before the career break): Google Scholar

  1. Time-Series Analysis and Deep Learning module (DPhil): Lecturer and Lab demonstrator, EPSRC Centre for Doctoral Training in Health Data Science, Department of Computer Science, University of Oxford. (2022)
  2. Mathematics in Engineering Science (2nd year UG): Tutorial Lecturer, Somerville College, University of Oxford. (2022–2023)
  3. Medical Imaging (3rd year UG): Tutor, Department of Engineering Science, University of Oxford. (2020–2021)
  4. Wearable Sensors (3rd year UG): Tutor, Department of Engineering Science, University of Oxford. (2020–2021)
  5. Structural Biomaterials (MSc): Guest Lecturer of the medical imaging module, Institute of Ageing and Chronic Diseases, University of Liverpool. (2015)
  6. Advanced Signal Processing and Analysis (MSc): Workshop supervisor, Department of Engineering and Design, University of Sussex. (2006–2007)
  7. Digital Systems and Microprocessor (1st year UG): Lecturer (2005–2007) and lab supervisor (2004–2007), Department of Engineering and Design, University of Sussex.
  8. Digital Systems and Design (2nd year UG): Lab supervisor, Department of Engineering and Design, University of Sussex. (2005–2006)
  9. Electronics (2nd year UG): Lab supervisor, Department of Engineering and Design, University of Sussex. (2005–2006)
  10. Embedded Software Project (2nd year UG): Lab supervisor, Department of Engineering and Design, University of Sussex. (2004–2006)
  11. UML (2nd year UG): Lab supervisor, Department of Engineering and Design, University of Sussex. (2004–2005)

  • 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 Lightening Presentation. Audio slides

Royal Academy of Engineering STEM Ambassador (2020 — current)

 

Recent Publications by Dr Lu
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