Jiarui (Jerry) Xing

Jiarui (Jerry) Xing

PhD Candidate in Computer Engineering @ University of Virginia

About

Hi, I’m Jiarui (Jerry) Xing, currently a PhD candidate at University of Virginia. My research advances medical image analysis through innovative deep learning techniques, focusing on multimodal learning to integrate diverse imaging modalities and motion-based analysis to leverage crucial motion information beyond static images. These approaches are applied to tasks such as multimodal cardiac motion video generation with latent diffusion models, multimodal cardiac dyssynchrony estimation, and multitask myocardial scar segmentation. My work primarily centers on cardiac imaging applications, with potential extensions to other areas.

Research Interests

Medical Image Analysis, Multimodal Learning, Motion-based Analysis, Latent Diffusion Models, Cardiac Imaging Analysis

Education

University of Virginia, Charlottesville, VA
Ph.D. in Computer Engineering, 09/2019 - present

Washington University in St. Louis, St. Louis, MO
M.S. in Computer Science, 09/2017 - 05/2019

Beijing Normal University, Beijing, China
B.S. in Electronic Engineering, 09/2013 - 05/2017

University of California, Santa Barbara, Santa Barbara, CA
School exchange Program, 09/2016 - 12/2016

Research Experience

University of Virginia, 03/2020 - present
Graduate Research Associate
Project: Detecting abnormal mechanical activation in heart failure patients from cardiac magnetic resonance imaging (MRI)
Advisor: Dr. Miaomiao Zhang

Washington University in St. Louis, 02/2019 - 03/2020
Independent Research
Project: Plug-and-play priors for joint image registration and reconstruction and its application in placental health monitoring
Advisor: Dr. Miaomiao Zhang, Dr. Ulugbek Kamilov

Washington University in St. Louis, 09/2018 - 01/2019
Independent Research
Project: Deep learning for liver MRI artifacts removing
Advisor: Dr. Ulugbek Kamilov

Invited Talks and Representations

  • Multimodal Learning to Improve Cardiac Late Mechanical Activation Detection From Cine MR Images, 2024 IEEE International Symposium on Biomedical Imaging (ISBI), May 2024
  • Multi-task Deep Learning for Late-activation Detection of Left Ventricular Myocardium, International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, May 2021
  • Plug-and-Play Priors for Reconstruction-based Placental Image Registration, Perinatal, Preterm and Paediatric Image Analysis workshop, MICCAI satellite event, October 2020, Shenzhen, China

Awards

  • Honorable Mentions in Mathematical Contest in Modeling/Interdisciplinary Contest in Modeling, 01/2016
  • The 1st Prize in Contemporary Undergraduate Mathematical Contest in Modeling (CUMCM), Beijing, 10/2015
  • The 1st Prize in Beijing Normal University Mathematical Modeling Contest, 05/2015
  • The 2nd Prize in “Jingshi Cup” Contest for Extracurricular Academic Technology Works, Beijing Normal University, 05/2015
  • The 3rd prize the National Youth China Adolescents Science & Technology Innovation Contest, 05/2015
  • The 3rd Prize in Beijing Normal University International Collegiate Programming Contest (ACM) Freshmen Contest, 12/2013
  • Title of Excellent Intern in Chinasoft International Limited, 07/2016

Publications

  1. Jiarui Xing, Nivetha Jayakumar, Nian Wu, Yu Wang, Frederick H. Epstein, and Miaomiao Zhang. “LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation.” Paper accepted for publication in Proceedings of the Workshop on Shape in Medical Imaging (ShapeMI), in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2024). In press.

  2. Nian Wu, Jiarui Xing, and Miaomiao Zhang. “TLRN: Temporal Latent Residual Networks For Large Deformation Image Registration.” Paper accepted for publication in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2024). In press.

  3. Jiarui Xing, Shuo Wang, Amit R. Patel, Kennth Bilchick, Frederick H. Epstein, Miaomiao Zhang “Scar-Aware Late Mechanical Activation Detection Network For Cardiac Resynchronization Therapy.” Abstract submitted to the SCMR 28th annual scientific sessions. Washington, DC: SCMR (2025).

  4. Jiarui Xing, Nian Wu, Kenneth Bilchick, Frederick Epstein, Miaomiao Zhang. “Multimodal Learning To Improve Cardiac Late Mechanical Activation Detection From Cine MR Images.” In 2024 IEEE 20th International Symposium on Biomedical Imaging (ISBI).

  5. Nivetha Jayakumar, Jiarui Xing, Tonmoy Hossain, Fred Epstein, Miaomiao Zhang. “Diffusion Models To Predict 3D Late Mechanical Activation From Sparse 2D Cardiac MRIs.” Machine Learning for Health (ML4H), 190-200 PMLR

  6. Jiarui Xing, Shuo Wang, Kenneth C. Bilchick, Amit R. Patel, and Miaomiao Zhang. “Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI.” In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023.

  7. Jiarui Xing, Shuo Wang, Kenneth C. Bilchick, Frederick H. Epstein, Amit R. Patel, and Miaomiao Zhang. “Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine Dense MRI.” In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023.

  8. Jian Wang, Jiarui Xing, Jason Druzgal, William M. Wells III, and Miaomiao Zhang. “MetaMorph: Learning Metamorphic Image Transformation with Appearance Changes.” In International Conference on Information Processing in Medical Imaging (IPMI), pp. 576-587. Cham: Springer Nature Switzerland, 2023.

  9. Jiarui Xing, Sona Ghadimi, Mohammad Abdishektaei, Kenneth C. Bilchick, Frederick H. Epstein and Miaomiao Zhang. Multi-task Deep Learning for Late-activation Detection of Left Ventricular Myocardium, International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2021

  10. Jiarui Xing, Sona Ghadimi, Mohammad Abdishektaei, Kenneth C. Bilchick, Frederick H. Epstein and Miaomiao Zhang. “Deep Networks To Automatically Detect Late-Activating Regions Of The Heart.” 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021.

  11. Jiarui Xing, Ulugbek Kamilov, Wenjie Wu, Yong Wang and Miaomiao Zhang. “Plug-and-play priors for reconstruction-based placental image registration.” Smart Ultrasound Imaging and Perinatal, Preterm and Pediatric Image Analysis. Springer, Cham, 2019. 133-142.

  12. Youshan Zhang, Jiarui Xing, and Miaomiao Zhang. “Mixture probabilistic principal geodesic analysis.” In Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy: 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 4, pp. 196-208. Springer International Publishing, 2019.

Academic Services

Co-chair, IEEE Signal Processing Society Student Chapter at University of Virginia, 2021-2023

Teaching Assistant Experience

  • ECE/CS 4501/6501: Machine Learning in Image Analysis, University of Virginia, Fall 2022
  • ECE 4501/6782 - CS 4501/6501: Digital Image Processing, University of Virginia, Fall 2020
  • ECE/CS 3502/3501: Foundation of Data Analysis, University of Virginia, Spring 2020
  • CSE517a, Machine Learning, Washington University in St. Louis, Spring 2018

Software Releases

Strain Matrix Labeler
This is a free source code of a software that can visualize and add label to strain matrix data. It is developed in Python using PyQt, Numpy and Matplotlib.
Distributed at: https://github.com/jr-xing/strainmatLabeler
Role: Developer

FLASHC Docker Image
This is a docker image that has FLASH (a C++ implementation for fast diffeomorphic image registration algorithm) installed. Instead of configuring a series of C++ libraries and facing the risk of messing up the system, with the docker image user can simply download and use in a plug-and-play manner.
Distributed at: https://hub.docker.com/repository/docker/jrxing/flashc
Role: Developer