TALK TITLE: “Sense for Less: Physical Informed Cyber-Physical Systems Adaptation for Vibration-Based Occupant Monitoring“
SPEAKER: Shijia Pan, Assistant Professor, Computer Science, and Engineering, UC Merced
BIO: Dr. Shijia Pan is an Assistant Professor at the University of California Merced. She received her bachelor’s degree in Computer Science from the University of Science and Technology of China and her Ph.D. degree in Electrical and Computer Engineering at Carnegie Mellon University. Her research interests include cyber-physical systems, Internet-of-Things (IoT), and ubiquitous computing. She worked in multiple disciplines and focused on indoor human information acquisition through ambient sensing. She received Rising Stars in EECS, Nick G. Vlahakis Graduate Fellowship, Google Anita Borg Scholarship, Best Paper Awards (IoTDI, ASME SHM/NDE, HASCA), Best Poster Awards (SenSys, IPSN), Best Demo Award (Ubicomp, BuildSys), Best Presentation Award (SenSys Doctoral Colloquium), and Audience Choice Award (BuildSys) from ACM/IEEE conferences.
ABSTRACT: The number of everyday smart devices is projected to grow to billions in the coming decade, which enables various smart building applications. These applications, especially in-home long-term occupant monitoring, rely on emerging device-free human sensing techniques. From the system perspective, we introduce an alternative non-intrusive sensing modality through ambient structural vibration to indirectly infer fine-grained occupant information. However, due to the complexity of the physical world, sensing data distributions face severe domain variances. Therefore, from the data perspective, accurate information learning through pure data-driven approaches requires a large amount of labeled data, which is costly and difficult to obtain in practice. We address these challenges by combining physical and data-driven knowledge in learning.
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