Jadie Adams
Salt Lake City, Utah
Welcome to my website!

About Me
Machine Learning Researcher
I'm a computing PhD candidate at the Scientific Computing and Imaging Institute (SCI) at the University of Utah under Dr. Shireen Elhabian's advisement. I joined SCI after working in machine learning and speech science for telephone transcription at CaptionCall. Before that, I earned my BS in Mathematics from Westminster College, minoring in Physics and Computer Science and earning an honors certificate.
My research involves quantifying uncertainty in deep learning models for medical image analysis. I am interested in Bayesian and statistical methods for shape modeling from volumetric images and 3D point clouds. My work has medical applications such as pathology detection and diagnosis in orthopedic, neuroscience, and cardiac research.
I recently completed an internship with the Machine Learning and Instrument Autonomy group at the NASA Jet Propulsion Lab. In this project, I applied Bayesian deep learning techniques for an application in cosmology - Cosmic Microwave Background (CMB) recovery.
I tend to my garden with my husband and dog in my free time. We also enjoy art, skiing, backpacking, and river rafting.
Recent News
My paper "Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud" has been accepted for a spotlight presentation at ICLR 2024. Of the 7,000 papers submitted, only 5% were selected for spotlight presentations this year.Â
I received the NIH STAR student award at MICCAI 2023.Â

I presented three papers at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023 conference in Vancouver, Canada:
 "Fully Bayesian VIB-DeepSSM" (Main track, early accept)
"Can point cloud networks learn statistical shape models of anatomies?" (Main track)
 "Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation" (UNSURE workshop)



My journal article "Learning Spatiotemporal Statistical Shape Models for Nonlinear Dynamic Anatomies" was accepted for publication in the Frontiers in Bioengineering and Biotechnology Biomechanics.
Accepted to participate in the AAAI 2023 Doctoral Consortium.
My paper "Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach" was accepted to the Conference on Innovative Applications of Artificial Intelligence (IAAI), part of AAAI 2023.
I won the "Best Oral Presentation" award for my paper "Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach" at the MICCAI Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop, 2022.
 My paper "From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach" was early accepted at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.