Philosophy: My research follows the SOP of generating an idea, representing the ideas in mathematical forms, and acting on the ideas/models/products based on a philosophy- and inference-learning-oriented loop of “what, why, and how.”

Impact-driven: I’m an engineer developing for real-world impacts and for social good. Real products for people in real-world settings.

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Research Showcase: My recent research fellowship project focused on developing machine-learning methods to predictively monitor and manage the health of gestational diabetic women during pregnancy. This health condition affects one in six pregnant women worldwide. Among these women, 50% will become chronic type 2 diabetics within five years. So it is highly important to identify the high-risk group; therefore, we can delay progression through earlier interventions.

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My current research activities have three folds. One is to develop a health foundation model for time-series data and explore the feasibility of using meta-learning with large language models for explainable AI in health monitoring and disease discovery. Second is to explore the evaluation methods and alignment of AI+GenAI computational linguistics vs. human language linguistics and bias that is introduced by human knowledge + user experience. Third is building in-network ML infrastructure for the future of ubiquitous health and patient monitoring, thereby increasing the capacity of mobile health while reducing digital health disparities, especially for LMICs. The threefold lays the foundation of AI and federated learning for the next generation of mobile health and hospital-at-home.

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Current Projects:

1. SmartEdge, EU Horizon project, University of Oxford. (2024 — Current)

2. 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)

3. MItigating the Risk of developing type 2 diabetes Associated with GEstational diabetes (MIRAGE), Diabetes UK PhD Studentship, funded by Diabetes UK, UK. (2023-2026)

4. 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)

5. 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. Enterprise and Innovation Research Fellowship, the MPLS Division, University of Oxford, UK. (2022 — 2023)

2. Oxford Saïd Business School Idea2Impact Fellowship, University of Oxford, UK. (2023)

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).