Qijia Shao is currently a Ph.D. candidate in Computer Science at Columbia University, advised by Professor Xia Zhou and Professor Fred Jiang. His research interest lies broadly in the application-driven aspects of Mobile Computing and Human-Computer Interaction, with a recent focus on the development of unobtrusive physical and physiological sensing systems. Qijia’s work has been recognized with the NSF Awesome Discoveries, Best Demo Awards at MobiCom 2023 and HotMobile 2020, as well as a Best Teaser Award at UbiComp 2023. He was named one of the MobiSys Rising Stars in 2024. Qijia has worked at Philips Research, Snap Research, and Samsung Research, where several of his proposed algorithms are being deployed to real-world products.
Accurate and continuous monitoring of human physical and physiological signals is critical for enhancing healthcare, personalizing education, and facilitating human interaction with the physical environment. However, current methods for acquiring human data frequently rely on cumbersome environmental instrumentation, extensive manual inputs, or uncomfortable rigid or adhesive wearable sensors. In this context, existing approaches to human data acquisition are increasingly inadequate, unable to meet the dynamic and complex demands of many real-world applications. My research focuses on developing novel sensing systems and robust data analytic techniques to lower the barriers to acquiring and interpreting human physical and physiological signals. On the system side, I have explored the use of everyday fabrics as a natural and pervasive sensing modality, enabling unobtrusive human sensing with minimal manual overhead. On the analytics side, my work drives algorithm designs tailored to practical needs from specific applications, overcoming challenges related to limited computational resources, data availability, and the need for generalizability across diverse user populations and environments.