I am a PhD student at Electrical and Computer Engineering, University of California, Los Angeles (UCLA), advised by Prof. Yang Zhang. Currently, my research is centered on harnessing the kinetic energy generated by people interactions with everyday objects to smarten up the environment by facilitating activity recognition and home automation. I am also broadly interested in the application of radar technologies in IoT, energy harvesting, ubiquitous sensing, wireless power transfer and wearable sensing. 

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Research & Projects

Headar: Sensing Head Gestures for Confirmation Dialogs on Smartwatches with Wearable Millimeter-Wave Radar (IMWUT 2023) 

Xiaoying Yang, Xue Wang, Gaofeng Dong, Zihan Yan, Mani Srivastava, Eiji Hayashi, Yang Zhang

Headar employs mmWave radar to detect head gestures, enabling fluent and natural interaction through nods and shakes between user and wearable devices.

LinkYouTube

CubeSense++: Smart Environment Sensing with Interaction-Powered Corner Reflector Mechanisms (UIST 2023)

Xiaoying Yang, Jacob Sayono, Yang Zhang

CubeSense++ are battery-free reflector mechanisms that can encode user interactions with everyday objects into structured responses to mmWave radar.

LinkYouTube

MiniKers: Interaction-Powered Smart Environment Automation (IMWUT 2022)

Xiaoying Yang, Jacob Sayono, Jess Xu, Jiahao "Nick" Li, Josiah Hester, Yang Zhang

MiniKers is a fleet of environment actuation devices that harness energy from user interactions with everyday objects. "Kers" comes from "Kinetic Energy Recovery System" that are widely used in automotive systems for recovering energy under braking.

LinkGitHub

ForceSight: Non-Contact Force Sensing with Laser Speckle Imaging (UIST 2022)

Siyou Pei, Pradyumna Chari, Xue Wang, Xiaoying Yang, Achuta Kadambi, Yang Zhang

ForceSight is a non-contact force sensing approach using laser speckle imaging. Surface deformations caused by force can be approximated by laser speckle shift.

LinkYouTube

CubeSense: Wireless, Battery-Free Interactivity through Low-Cost Corner Reflector Mechanisms (CHI 2021 LBW) 

Xiaoying Yang, Yang Zhang

CubeSense is a sensing approach that encodes user interactivity (e.g. button press, switch toggle, slider position, knob rotation) into Radar Cross Section (RCS) of corner reflectors.  

LinkYouTube