EU

Meet the Team

Jadie Adams, Ph.D.

Senior R&D Engineer

Computer Vision

Kitware Remote

Ph.D. in Computing
The University of Utah, Scientific Computing and Imaging Institute

B.S. in Mathematics
Westminster University

Jadie Adams

Jadie Adams, Ph.D. is a senior R&D engineer on Kitware’s Computer Vision Team. She contributes to projects related to 3D reconstruction from imagery and video, natural language processing, and media forensics.

Prior to working at Kitware, Jadie interned at NASA’s Jet Propulsion Lab (JPL) on the Machine Learning and Instrument Autonomy (MLIA) team. She collaborated with a team of data scientists and cosmologists to develop a probabilistic machine learning model for cosmic microwave background recovery from full sky maps. She also worked at Sorenson on the Speech Sciences and Machine Learning Team as an AI/Speech Scientist II, where she designed and implemented ML/NLP algorithms to enhance automatic speech recognition for telephone captioning.

Jadie earned her Ph.D. in computing (image analysis track) from the University of Utah’s Scientific Computing and Imaging Institute. Her dissertation focused on probabilistic deep learning for medical image analysis and anatomical shape modeling. She did her undergraduate at Westminster University in Salt Lake City where she earned a bachelor’s degree, with honors, in mathematics with a minor in physics and computer science.

Awards

  • Best Oral Presentation in the STACOM Workshop at the MICCAI 2022 conference

  • Best Paper Runner-Up in the ShapeMI Workshop at the MICCAI 2020 conference

Invited Talks & Media

  • ARCS Forward, National Scholars Speaker Series “Machine Learning and Algorithms” February 2022

Publications

  1. R. Bhalodia, S. Elhabian, J. Adams, W. Tao, L. Kavan, and R. Whitaker, "DeepSSM: A blueprint for image-to-shape deep learning models," Medical Image Analysis, vol. 91, pp. 103034, Jan. 2024. [URL]
  2. J. Adams and S. Elhabian, "Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud," in The Twelfth International Conference on Learning Representations (ICLR), 2024. [URL]
  3. R. Nihalaani, T. Kataria, J. Adams, and S. Elhabian, "Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. Springer Nature Switzerland, 2024, pp. 273-285. [URL]
  4. K. Iyer, J. Adams, and S. Elhabian, "SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images," in Medical Image Understanding and Analysis. Springer Nature Switzerland, 2024, pp. 142-157. [URL]
  5. J. Adams, S. Lu, K. Gorski, G. Rocha, and K. Wagstaff, "Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 13, pp. 15640-15646, Jun. 2023. [URL]
  6. J. Adams, N. Khan, A. Morris, and S. Elhabian, "Learning spatiotemporal statistical shape models for non-linear dynamic anatomies," Frontiers in Bioengineering and Biotechnology, vol. 11, pp. 1086234, Jan. 2023. [URL]
  7. J. Adams and S. Elhabian, "Can Point Cloud Networks Learn Statistical Shape Models of Anatomies?," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. Springer Nature Switzerland, 2023, pp. 486-496. [URL]
  8. J. Adams and S. Elhabian, "Fully Bayesian VIB-DeepSSM," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. Springer Nature Switzerland, 2023, pp. 346-356. [URL]
  9. A. Aziz, J. Adams, and S. Elhabian, "Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models," in Shape in Medical Imaging. Springer Nature Switzerland, 2023, pp. 157-172. [URL]
  10. J. Adams and S. Elhabian, "Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation," in Uncertainty for Safe Utilization of Machine Learning in Medical Imaging. Springer Nature Switzerland, 2023, pp. 53-63. [URL]
  11. J. Adams, N. Khan, A. Morris, and S. Elhabian, "Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach," in International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM MICCAI). Springer Nature Switzerland Cham, 2022, pp. 143–156. [URL]
  12. J. Adams and S. Elhabian, "From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Springer Nature Switzerland, 2022, pp. 474-484. [URL]
  13. J. Adams, G. Lopez, C. Mann, and N. Tran, "Your Friendly Neighborhood Voderberg Tile," Mathematics Magazine, vol. 93, no. 2, pp. 83-90, Mar. 2020. [URL]
  14. J. Adams, R. Bhalodia, and S. Elhabian, "Uncertain-DeepSSM: From Images to Probabilistic Shape Models," in Shape in Medical Imaging. Springer International Publishing, 2020, pp. 57-72. [URL]

Bibliography generated 2024-10-30-12:30:05 (7361)