About Me

I’m a first year PhD student in the Human Signals Lab at UT Austin ECE, supervised by Prof. Edison Thomaz. I received B.S. Electrical Engineering and B.A. Statistics degrees from Rice University. My research interests include Ubiquitous and Mobile Computing, Mobile Health, Wearable Computing, Activity Recognition, Machine Learning and Signal Processing. I want to build systems and models to understand people’s behaviors and improve their health and quality of life. 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. I also worked with Prof. Behnaam Aazhang on Alzheimer’s Disease diagnosis with MRI and machine learning. Here is my CV for more information.


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

Research Experience

Sleep Advice Prediction with Multimodal Data

Supervisor: Prof. Akane Sano, Rice University

  • Developed user-dependent and user-independent Random Forest models to predict three doctors’ sleep advice to shift workers in two Japanese hospitals using daily survey and wearable device data
  • Output advice probabilitites to indicate models’ confidence in advice selections
  • Compared the performance among individual models built for each doctor and a one-size-fits-all model
  • Delivered the models to collaborators in Japan for a clinical study to evaluate the effectiveness of sleep advice to shift workers

Schizophrenia Patients’ Symptom Prediction with Mobile Phone Sensing

Supervisor: Prof. Akane Sano, Rice University

  • Developed user-dependent and user-independent Gaussian Process and Long Short-Term Memory regression models to predict Schizophrenia patients’ self-assessment scores of ten symptoms with behavioral and environmental data collected from mobile phone sensing
  • Personalized the independent models by fine-tuning transfer learning and clustering participants’ daily behavior patterns and demographic information
  • Extended the system from predicting symptoms for only one day to thirty days with a Long Short-Term Memory classification model which would give doctors more time to prepare interventions for patients in practical use

Alzheimer’s Disease Diagnosis with Self-supervised Learning and MRI

Supervisor: Prof. Behnaam Aazhang, Rice University

  • Developing classification models for subjects’ cognitive conditions and Alzheimer’s Disease diagnosis with Magnetic Resonance Imaging
  • Working on Convolutional Neural Network models with Self-supervised Learning to demonstrate the possibility of accurate disease diagnosis with only a limited amount of labeled data
  • Gathered benchmark MRI datasets for pre-training and classification tasks