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.