Human AI

Multimodal sensing for wearable AI

At Meta I work within the Human AI team, building the future of human-centric computing. My work centers on interaction — making a device feel natural to use — and understanding — giving it a real sense of the person using it. I focus on the technical direction for sensing across the wearables portfolio: which sensors are needed to deliver those interaction and understanding features.

My fundamental research is on radar. It works without contact or line of sight, is unaffected by lighting, and draws little power, which suits sensing that runs continuously in the background. We have demonstrated face, eye, and body tracking, health monitoring, and speech recognition.

Beyond radar, that spans ultrasound, MEMS, cameras, and lasers. Choosing among them is rarely about which measures best in isolation — it is about what a modality costs in power, space, and compute once it has to share a device with everything else, and still fit something people are willing to wear.

Selected publications: (Ma et al., 2025; Huang et al., 2024; Nguyen et al., 2025)

References

2025

  1. SenSys
    mmET: mmWave Radar-Based Eye Tracking on Smart Glasses
    Ruichun Ma, Yasuo Morimoto, John S Ho, and 2 more authors
    In Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems, 2025
  2. BioSensors
    Wireless Respiration Tracking Using Smart Glasses
    Dat T Nguyen, John S Ho, Daniel Heywood, and 1 more author
    In 2025 IEEE BioSensors Conference (BioSensors), 2025

2024

  1. IEEE MWTL
    Radar-based heart rate sensing on the smart glasses
    Irene Wei Huang, Paurakh Rajbhandary, Sam Shiu, and 4 more authors
    IEEE Microwave and Wireless Technology Letters, 2024