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.