Yufei Shen

Yufei Shen

PhD Student

Human Signals Lab
Wireless Networking and Communications Group (WNCG)
Department of Electrical and Computer Engineering
The University of Texas at Austin

shenyufei@utexas.edu
shenyufei2000@hotmail.com

About Me

I’m a PhD student in the Human Signals Lab at UT Austin ECE, supervised by Prof. Edison Thomaz. My research focuses on the interdisciplinary topics of ubiquitous computing, human-centered AI, and digital health. Specifically, I develop systems and models that leverage sensor data from smartphones and wearable devices to gain insights into human behaviors and enhance people’s health and quality of life.

I received B.S. Electrical Engineering and B.A. Statistics degrees from Rice University. During my undergraduate study, I had research experience in the Computational Wellbeing Group under the supervision of Prof. Akane Sano, where I worked on machine learning models for wellbeing predictions with mobile and wearable sensing.

Education

The University of Texas at Austin Expected May 2027

Ph.D. Electrical Engineering
Supervisor: Prof. Edison Thomaz

Rice University May 2022

B.S. Electrical Engineering, B.A. Statistics
Magna Cum Laude

Current Research Projects

TechSANS - Technology for Smartphone Assessment of Neurocognitive Symptoms

In this NIH-funded study, we explore digital biomarkers of cognitive functioning based on data collected from sensors in smartphones and wearable devices. We aim to develop systems and models to assess and characterize cognitive functioning in a more naturalistic, unobtrusive, efficient, and continuous manner. Leading this project, my role includes:

  • App Development
    • Developed an iOS mobile application that continuously collects and uploads longitudinal behavioral and physiological data from iPhone and Apple Watch sensors.
    • Utilized Apple’s SensorKit framework to collect additional data modalities from iPhones that are only accessible to approved research studies, including device usage, keyboard typing, light intensity, text message logs, and call logs.
    • Benchmarked the battery consumption of different app components to determine the optimal design for balancing data sampling frequency and energy efficiency.
  • Backend Infrastructure
    • Built a server backend to store collected data, and created scripts and web dashboards to monitor data collection from over 30 study participants for up to a year.
  • Data Analysis
    • Constructed pipelines to process the collected multimodal sensor data and trained machine learning models to assess cognitive functioning naturalistically and unobtrusively.