Jingxian Wang

I am a final-year Ph.D. candidate at Carnegie Mellon University’s ECE Department, advised by Swarun Kumar. My research is in the area of wireless systems with an emphasis on building next-generation battery-free IoT technologies for long-range communication, novel sensing, and efficient energy delivery. My work has also been highly inter-disciplinary, exploring connections between soft materials and wireless systems.

I publish in wireless networks, mobile computing, and hci conferences. My work has been recognized as the best long wearables paper at UbiComp 2020, the best paper at IPSN 2021, the best demo candidate at UbiComp 2018, and research highlights in SIGMOBILE and Communications of ACM. I am a recipient of Microsoft Research Fellowship.

I am excited to announce that I'm on the academic job market this year! Stay tuned.

Research Directions

Recent News



Selected Research Topics

  • Materials Science meets Wireless Systems

    Selected project: Smart Tattoos. Three million people in the US are suffering voice impairments. Current solutions, like keyboard and electrolarynx, require extra effort for the patients to compensate for their voice disorders. We present RFTattoo, an elegant speech recognition system for voice impaired using batteryless and flexible RFID tattoos.

  • Extending the Range of Commercial Passive IoT Devices

    Selected project: PushID. Today’s commercial passive RFIDs report ranges of 5-10 meters at best. This reveals blind zones for RFIDs to be detected in warehouses, stores and factories today. We present PushID which achieves an 8x improvement in range over commercial RFID systems.

  • Towards a Shape-aware Metaverse

    Selected project: WiSh. WiSh gives ordinary objects shape-awareness, relaying their geometry by combining wireless sensing with graphics. It opens up novel applications: clothing that can detect a user’s posture, or even bridges that report their structural health.

  • Monitoring Heart Rate and Respiratory Rate

    Selected project: Heartbeat and respiration signals from multiple people are captured by low-cost geophones. We build 3D characteristic models for each person based on their spatial signature.

  • Next-generation Smartwatches

    Selected project: A tactile feedback system to enhance the user experience with a method unique to the smartwatch form factor, allowing the visual scene on screen to extend into 2.5D physical space.


  • CMU Collaborative Innovation Center
    4720 Forbes Avenue, Pittsburgh, PA 15213