I am a PhD candidate at the University of Michigan pursuing a joint doctorate in Mechanical Engineering and Scientific Computing.
My research focuses on quantifying uncertainty in machine learning / deep learning models and utilizing these models in variety of exercise, health, and safety applications.
Projects and Publications
Balance Training Assessment
Projected Variational Bayesian Inference for Convolutional Neural Networks in Precision Health Balance Training
Jeremiah Hauth, Jamie Ferris, Steven Teguhlaksana, Christopher DiCesare, Kathleen H. Sienko, and Xun Huan
USNCCM conference, July 2021, Presentation
Variational Bayesian Inference for Convolutional Neural Networks in Precision Health Balance Training
Jeremiah Hauth, Steven Teguhlaksana, Jamie Ferris, Christopher DiCesare, Kathleen H. Sienko, and Xun Huan
SIAM CSE conference, March 2021, Presentation
Loss of Balance
Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units
Jeremiah Hauth, Safa Jabri, Fahad Kamran, Eyoel W. Feleke, Kaleab Nigusie, Lauro V. Ojeda, Shirley Handelzalts, Linda Nyquist, Neil B. Alexander, Xun Huan, Jenna Wiens, Kathleen H. Sienko
Sensors 2021, July 2021
Bayesian Tumor Segmentation
Low-cost Bayesian Uncertainty Quantification for Deep Learning in Medical Image Segmentation
Snehal Prabhudesai, Jeremiah Hauth, Nikola Banovic, and Xun Huan
Machine Learning for Health Care. In Review.
Helicopter Icing
Real-Time In-Flight Ice Detection System via Computational Aeroacoustics and Bayesian Neural Networks
Beckett Y. Zhou, Nicolas R. Gauger, Myles Morelli, Alberto Guardone, Jeremiah Hauth, and Xun Huan
AIAA Scitech. January 2020. Presentation.
Correlation Effects in Bayesian Neural Networks for Computational Aeroacoustics Ice Detection
Jeremiah Hauth, Xun Huan, Beckett Y. Zhou, Nicolas R. Gauger, Myles Morelli, and Alberto Guardone
AIAA Scitech. January 2020. Presentation.