Mobile health (mHealth) applications are revolutionizing healthcare by continuously monitoring environmental factors (e.g., light intensity) and personal attributes (e.g., heart rate, motion, location) to provide health-related predictions (e.g., diagnostics) and analytics. This technology enables long-term, out-of-clinic monitoring without requiring active user participation. These applications need to be safe in terms of:
Despite the popularity of machine learning in mHealth, ensuring safety is open as data distribution naturally shifts in-the-wild. Following types of shifts become more complex in mHealth as the data is sequential.
In this project we will enhance the accuracy and reliability of mHealth predictions, with long-term goals of translating findings into clinical practice. Key Research Questions:
This project brings together expertise in time series modeling, mobile sensing, and machine learning reliability to address these critical challenges in mHealth.
Supervisors: Dr. Ting Dang (University of Melbourne) and Dr. Abhirup Ghosh (University of Birmingham)
Anticipated start date: Early 2025
Note: This Joint PhD project will be primarily based at the University of Melbourne with a minimum 12-month stay at the University of Birmingham.
To apply for this position, please send the following documents to both Dr. Ting Dang (ting.dang@unimelb.edu.au) and Dr. Abhirup Ghosh (a.ghosh.1@bham.ac.uk):
I am looking for highly motivated PhD students to work on wearable sensing and computing for health monitoring.
Continuous glucose monitoring (CGM) has revolutionized the management of type 1 diabetes. The continuous acquisition of glucose readings, combined with other physiological and biochemical data from electronic medical records, provides extensive and detailed insights into the daily fluctuations of glucose levels and associated physiological changes. Artificial intelligence (AI) has been increasingly employed to analyze these health data sets, facilitating personalized care for individuals with diabetes and enabling the adaptation of treatments for complex clinical presentations.
This PhD project aims to investigate the application of AI in analyzing glucose signals to identify individuals at high risk and diagnose those who may develop severe hypoglycemia or other diabetes-related complications. Specifically, the objectives of this project are:
Supervisors: Prof. Elif Ekinci and Dr. Ting Dang, University of Melbourne
Anticipated start date: Early 2025
Note: Only local students are considered for this scholarship.
To apply for this position, please send the following documents to Ting Dang (ting.dang@unimelb.edu.au):