Abstract: Mobile health technologies are increasingly recognized as a means to bridge health disparities due to their high accessibility, cost-effectiveness, and global connectivity. However, research indicates that if these technologies are not implemented thoughtfully, they could exacerbate health disparities and raise issues, such as privacy and fairness. In my talk, I will discuss my recent efforts in addressing these challenges. Firstly, I will present a novel mobile health framework designed to protect privacy while efficiently managing and assessing neural disorders, such as Parkinson's disease. Secondly, I will introduce innovative, fairness-aware computational methods for mobile mental health applications. Both of these applications have been successfully deployed in real-world settings, actively involving participants in delivering equitable and responsible mobile health services.

Short Bio: Huining Li is a Ph.D. candidate in the Computer Science and Engineering Department at the University at Buffalo, SUNY. Her research interest lies broadly in internet-of-things, cybersecurity, and mobile computing. Especially, her recent focus is on applying research advancement to the field of mobile health. She has authored 28 papers in top-tier conferences and journals, including ACM MobiCom, MobiSys, SenSys, UbiComp, IEEE TMC, NDSS, ICHI, Elsevier Smart Health, and BodyNet. Her work has received three Best Paper Awards (SenSys’19, BodyNet’21, and ICHI’22) and one Best Paper Candidate (SenSys’22). Also, her research work has been recognized in various scholarly venues, including one 2023 IEEE EPICS Award (Elderly care wearables), Best Design Award Runner-up in the 2021 IEEE Healthcare Summit (COVID-19 Data Hackathon), and several research competition awards (e.g., UB Blackstone LaunchPad). In 2023, she was selected for EECS Rising Star.