Huining Li

Research Abstract:

One of the main hindrances to integrating mobile technologies into real-world healthcare applications is the privacy issue. Specifically, I formulate the privacy challenge in twofold. First, compared with traditional clinical computer systems, mobile health systems encounter a much larger attack surface due to their inherent high accessibility. Round-the-clock data collection in mobile health systems is always at odds with privacy preservation. Second, mobile health data are intrinsically heterogeneous and continuously streaming, which poses a challenge in harmonizing the privacy protection requirements across various data formalities and dynamics. To address these privacy-preserving challenges in the mobile health era, my research has primarily focused on the following aspects: 1) Privacy-by-design Sensing Mechanism: I explore novel sensing mechanisms to directly interrogate desired information and physically isolate privacy, such as mmWave-based vocal sensing, polarized-light-based vasculature sensing. 2) Compression-aware Privacy Computing to tame privacy protection in mobile data heterogeneity and dynamics. 3) Fairness-aware Privacy Computing to solve privacy preservation disparities in mobile health services. In addition, my research innovation has been applied to other mobile health studies, including multi-label neural disease screening, medication adherence detection, and medicine effectiveness assessment for Parkinson's disease self-management.

Bio:

Huining Li is a Ph.D. candidate in the Computer Science and Engineering Department at the University at Buffalo, SUNY, advised by Professor Wenyao Xu. 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).