Haofan Cai (https://people.ucsc.edu/~hcai10/) is a Ph.D. student in the Department of Computer science and Engineering at University of California Santa Cruz (UCSC), where she works with Prof. Chen Qian. Her research spans the broad areas of wireless networking, Internet-of-Things (IoT), mobile computing, computer vision, and network security. She mainly focuses on integrating large-scale and low-cost passive RFID tags into existing pervasive-sensing applications to enable multi-functional and cost-efficient IoT systems. Her works have been published in several top-tier networking conferences (Mobicom, ICNP, SECON) and journals (TON, TOSN).
My research interests cover the broad area of Wireless Networking, Internet-of-Things (IoT), Mobile Computing, Computer Vision, and Network Security. With the advances of new technologies such as Radio-frequency identification (RFID) systems, pervasive sensing, and cyber-physical systems (CPS), the Internet-of-Things enables inter-connection of small objects (such as tagged items, embedded systems, and mobile devices) and data collection, delivery, as well as processing among them. My research focuses on integrating large-scale and low-cost passive RFID tags into existing pervasive-sensing applications to enable multi-functional and cost-efficient IoT systems. We believe the inherent advantages of RFID systems to identify, trace, and track information using easily deployable tags provide unique opportunities to enable many novel IoT applications in new areas of sensing, actuation, and user interaction, which is far beyond its traditional use in supply chain management. However, the design of these applications involves challenges, as the limited computation ability and simple functionality of passive tags may make them ill-fitted for meeting the diverse requirements. For example, transforming RFID system with identification capability into human sensing and tracking platforms is quite difficult, as the backscattered signal is sparse and coarse, where the useful feature can hardly be extracted. To tackle these issues, my Ph.D. research takes a deep look at exploring the potential of commercial off-the-shelf (COTS) passive tags, and proposes new applications in the following themes: (1) Enabling cyber-physical connection using RFID passive tags, where system can seamlessly detect user-item interaction and gather information from real-world subjects. (2) Fusing the information from computer vision and RFID sensing channel to actively find/track the mobile object/person with least training efforts. (3) Addressing scalability issue and domain shift challenge in human gesture recognition with RFID via employing domain-adaptive few-shot learning (DA-FSL).