Xuewen Yao

PhD student

The University of Texas at Austin

I am a PhD student at Daily Activity Lab at the University of Texas at Austin. I am interested in leveraging wearable/mobile sensors and machine learning algorithms to analyze and model human signals. In particular, I study the (stress) signals from mother-infant pairs collected longitudinally through their daily interactions. I am supervised by Prof. Kaya de Barbaro and Prof. Edison Thomaz.

“In-the-wild” dataset and individual differences are two characteristics in my research. “In-the-wild” datasets are much noisier and harder to work with comparing to lab data, but they reflect and better model our real world. I intend to build models that can detail individual differences as in health-related applications, it is not enough to have a satisfactory mean accuracy. The model needs to be good with each data point.

I have worked with motion and audio sensors extensively and developed models for parent holding behaviors, parent affect classification and infant crying/fussing classification.


  • Wearable Computing
  • Deep Learning
  • Activity Recognition
  • Affect Detection
  • Natural Language Processing


  • PhD in Electrical and Computer Engineering, 2023 (Expected)

    The University of Texas at Austin

  • MSc in Computer Science, 2018

    Gerogia Institute of Technology

  • BEng in Information Engineering, 2016

    City Univeristy of Hong Kong