Jianxin Xie

Assistant professor at University of Virginia, School of Data Science

Contact:

Our lab welcomes motivated volunteers and visiting scholars to join our research team. Prospective students interested in PhD opportunities are also welcome to contact me.

Education

Ph.D., Industrial & System Engineering, The University of Tennesse at Knoxville, 2023

M.S., Industrial Engineering and Manufacturing, Florida State University, 2020

B.S., Physics, Southeast University, 2016

Research Interest

My research centers on designing innovative machine learning models tailored to application- and problem-specific scenarios. I develop methodologies that can learn from diverse data characteristics, reveal meaningful patterns in unobserved regions, and integrate domain knowledge to enhance predictive performance, interpretability, and robustness. These approaches are driven by real-world challenges in healthcare and materials science, where AI holds the potential to generate new insights, improve diagnostics, and accelerate discovery. My research encompasses the following areas:

Representation learning for medical data
Physics-aware machine learning
Generative model for imaging tasks
Prediction and decision-making
Data-driven modeling and optimization

Publications

Journal Articles

L. Zhu., K. Bilchick., & J. Xie. (2025). “Physics-informed residual learning with spatiotemporal local support for inverse ECG reconstruction.” Scientific Reports, 15, no. 1 (2025): 31747. https://doi.org/10.1038/s41598-025-15687-1
J. Xie, W. Ko, R. X. Zhang, & B. Yao. (2024). Physics-augmented Deep Learning with Adversarial Domain Adaptation: Applications to STM Image Denoising. arXiv preprint arXiv:2409.05118.
J. Xie., S. Stavrakis, & B. Yao. “Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet”, Frontiers in Physiology, 15, 1362185, 2024. https://doi.org/10.3389/fphys.2024.1362185
J. Xie., B. Yao., & Z. Jiang. “Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement”, 2024, arXiv preprint arXiv:2403.07228.
J. Xie., B. Yao., & Z. Jiang. “The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture Estimation”, 2024, arXiv preprint arXiv:2403.08154.
J. Xie and B. Yao, “Hierarchical Active Learning for Defect Localization in 3D Simulation”, IISE Transaction on Healthcare Systems Engineering, (Accepted). https://doi.org/10.1080/24725579.2023.2233992
J. Xie and B. Yao, “Physics-constrained Deep Active Learning for Spatiotemporal modeling of Cardiac Electrodynamics”, Computers in Biology and Medicine, 2022. https://doi.org/10.1016/j.compbiomed.2022.105586
J. Xie and B. Yao, “Physics-constrained Deep Learning for Robust Inverse ECG Modeling”, Early Access, IEEE Transactions on Automation Science and Engineering, 2022. https://doi.org/10.1109/TASE.2022.3144347 (Best poster award in QCRE & DAIS Tracks best student poster competition, Runner Up Best Paper Award in the Operations Research Track, 2021 IISE Annual Conference)
C. Shen, J. Xie, M. Zhang, P. Andrei, M. A. Hendrickson, E. J. Plichta, and J. P. Zheng, "Self-Discharge Behavior of Lithium-Sulfur Batteries at Different Electrolyte/Sulfur Ratios," Journal of The Electrochemical Society, vol. 166 (3), pp. A5287-A5294, 2019. https://doi.org/10.1149/2.0461903jes
C. Shen, J. Xie, M. Zhang, P. Andrei, J. P. Zheng, M. A. Hendrickson, and E. J. Plichta, "A Li-Li2S4 battery with improved discharge capacity and cycle life at low electrolyte/sulfur ratios," Journal of Power Sources 414, pp. 412-419, 2019. https://doi.org/10.1016/j.jpowsour.2019.01.029
C. Shen, J. Xie, T. Liu, M. Zhang, P. Andrei, L. Dong, M. Hendrickson, E. J. Plichta, and J. P. Zheng, "Influence of pore size on discharge capacity in Li-air batteries with hierarchically macroporous carbon nanotube foams as cathodes," Journal of The Electrochemical Society, vol. 165 (11), pp. A2833-A2839, 2018. https://doi.org/10.1149/2.1141811jes
C. Shen, J. Xie, M. Zhang, M. A. Hendrickson, E. J. Plichta, and J. P. Zheng. "Carbon nanotube (CNT) foams as sulfur hosts for high-performance lithium-sulfur battery." ECS Transactions, vol. 77(11), pp.457, 2017. https://doi.org/10.1149/07711.0457ecst
C. Shen, J, Xie, M. Zhang, J. P. Zheng, M. A. Hendrickson, and E. J. Plichta. "Communication—Effect of lithium polysulfide solubility on capacity of lithium-sulfur cells." Journal of The Electrochemical Society, vol. 164 (6), pp. A1220-A1222, 2017. https://doi.org/10.1149/2.1381706jes
C. Shen, J. Xie, M. Zhang, P. Andrei, M. A. Hendrickson, E. J. Plichta, and J. P. Zheng. "Understanding the role of lithium polysulfide solubility in limiting lithium-sulfur cell capacity." Electrochimica Acta, vol. 248, pp. 90-97, 2017. https://doi.org/10.1016/j.electacta.2017.07.123

Proceedings:

J. Xie, Z, Jiang, and B. Yao, “The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture Estimation”, 2023 IISE Annual Conference Proceeding, Submitted, 2023.
J. Xie, & M. Zhang (2018, October). Laser Processing Technology for PAN Fiber Carbonization. In The Composites and Advanced Materials Expo. CAMX Conference Proceedings.
J. P. Zheng, C. Shen, J. Xie , M. Zhang, P. Andrei, M. A. Hendrickson, & E. J. Plichta (2017, September). Energy Density Limitation of Lithium-Sulfur Battery by Lithium Polysulfide Solubility in Electrolyte. In Meeting Abstracts (No. 5, pp. 473-473). The Electrochemical Society.
J. Li, J. Xie, & M. Zhang (2017). The Fabrication and Characterization of Nanocarbon Foam as Novel Wick Material for Thermal Management of Electronics. In TechConnect World Innovation Conference (Washington, DC (pp. 52-55). TechConnect.

Referred Presentations

J. Xie, “Physics-constrained Modeling and Optimization of Complex Systems: Healthcare Application”, The 13th Quality & Reliability Science and Technology Symposium, Tsinghua University, Beijing, China, Jul 7-9, 2025.
J. Xie, “Physics-constrained Modeling and Optimization of Complex Systems: Healthcare Application”, 3rd Data Science for Smart Manufacturing and Healthcare Workshop, SIAM International Conference on Data Mining, Alexandria, VA, May 1-3, 2025.
J. Xie, “Physics-constrained Modeling and Optimization of Complex Systems: Healthcare Applications”, Department of Systems and Information Engineering, University of Virginia, Apr, 25th, 2025.
J. Xie, “Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling”, NSF Workshop on Data-driven Modeling and Prediction of Rare and Extreme Events, Chicago, IL, Nov 22nd, 2024
J. Xie, “Physics-constrained Modeling and Optimization of Complex Systems: Healthcare Applications”, Department of Statistics Colloquium, University of Virginia, Nov, 10th, 2023.
J. Xie, B. Yao, “Hierarchical Active Learning for Defect Localization in 3D Systems” INFOMRS Annual Conference, Phoenix, AZ, Oct, 13-17, 2023.
J. Xie, S. Stavrakis, B. Yao, “Automated Identification of Atrial Fibrillation from Single-lead ECGs Using Multi-branching ResNet.” INFOMRS Annual Conference, Phoenix, AZ, Oct, 13-17, 2023.
J. Xie, Z, Jiang, B. Yao, “The Effect of Different Optimization Strategies to Physics-Augmented Deep Learning for Soil Moisture Estimation.” IISE Annual Conference, New Orleans, LA, May, 20-23, 2022.
J. Xie, S, Stavrakis, B. Yao, “Automated Identification of Atrial Fibrillation from Single-lead ECGs Using Multi-branching ResNet.” IISE Annual Conference, New Orleans, LA, May, 20-23, 2022.
J. Xie, B. Yao, “Physics-constrained Deep Active Learning for Spatiotemporal modeling of Cardiac Electrodynamics.” INFOMRS Annual Conference, Indianapolis, IN, Oct 14-19, 2022.
J. Xie, Yao, B. “Physics-constrained Deep Active Learning for Spatiotemporal modeling of Cardiac Electrodynamics.” IISE Annual Conference, Seattle, WA, May 21-24, 2022.
J. Xie, Yao, B. “Physics-constrained Deep Learning for High Dimensional Predictive Modeling.” OSU Cowboy Innovations Health and Life Science Technology showcase, Oklahoma State University, Apr 1st, 2022.
J. Xie, Yao, B. “Physics-constrained Deep Learning for Robust Inverse ECG Modeling.” INFORMS Annual Conference, Oct 24-27, 2021.
J. Xie, Yao, B. “Physics-constrained Deep Learning for High Dimensional Predictive Modeling.” IISE Annual Conference, May 22-25, 2021. (Best poster award in QCRE & DAIS Tracks best student poster competition, Runner Up Best Paper Award in the Operations Research Track)
J. Xie, Carles, M., Zhang, M. “Laser Processing Technology for PAN Fiber Carbonization and Graphitization.” The Composites and Advanced Materials Expo (CAMX), Dallas, TX, Oct 2018.
Van, H., J. Xie, Zhang, M. “Laser-Induced Graphitic Structure Change in Carbon Materials.” The 3rd Global Nanotechnology Congress and Expo, Scientific Federation, Aug 2017.
Shen, C., J. Xie, Zhang, M., Zheng, J. P. “Energy density limitation of lithium-sulfur battery.” The 48th Power Sources Conference, Electrochemical Society, Denver, CO, Jun 2018.
Zheng, J. P., Shen, C., J. Xie, Zhang, M., Andrei, P., Hendrickson, M. A., Plichta, E. J. “Energy Density Limitation of Lithium-Sulfur Battery by Lithium Polysulfide Solubility in Electrolyte.” The 232nd ECS (The Electrochemical Society) Meeting, Electrochemical Society, National Harbor, MD, Oct 2017.
Shen, C., J. Xie, Zhang, M., Zheng, J. P., Hendrickson, M. A., Plichta, E. J. “Carbon Nanotube (CNT) Foams as Sulfur Hosts for High Performance Lithium Sulfur Battery.” The 231st ECS (The Electrochemical Society) Meeting, Electrochemical Society, New Orleans, LA, May 2017.
Shen, C., J. Xie, Zhang, M., Zheng, J. P., Hendrickson, M. A., Plichta, E. J. “Macroporous Carbon Nanotube (CNT) Foams as Lithium Air Battery Cathodes.” The 231st ECS (The Electrochemical Society) Meeting, Electrochemical Society, New Orleans, LA, May 2017.

Honors & Awards

Featured Article in IISE Transactions on Healthcare Systems Engineering, May 2024
Best Poster Award, QCRE Best Student Poster Competition, IISE Annual Conference, 2023
Travel Award, Graduate Student Senate, University of Tennessee, 2023
Volunteer of Distinction, Office of Provost, University of Tennessee, 2023
Outstanding Graduate Student Award, ISE, University of Tennessee, 2023
Gilbreth Memorial Fellowship, IISE, 2022
Best Poster Award in QCRE & DAIS Tracks Best Student Poster Competition, IISE Annual Conference, 2021
Runner Up Best Paper Award in the Operations Research Track, IISE Annual Conference, 2021
Second Prize for the 11th Innovative Competition for Scientific and Technological Works of Physics and Experiment among University Students in Jiangsu Province, 2014
Second Prize (for the team) in English Drama Competition, Southeast University, 2014
Excellent Academic Research Report for the 5th Academic Conference of Southeast University, 2015
Outstanding Youth League Member, Southeast University, 2014
“Hongguang” Scholarship (endowed by Mr. CAO Hongguang, an alumnus of Southeast University, to students with excellent academic performance), 2015
“Optics Course” Award, Southeast University, 2013
Grand Prize (for the team) for Social Practice on “Research for the New Rural Social Security in Huaibei, Anhui Province”, 2014
Outstanding Individual Prize for Social Practice on “Research for the New Rural Social Security in Huaibei, Anhui Province”, 2014
Merit Student for the academic year 2012-2013, Department of Physics, Southeast University, 2013

Service Activities
Reviewer

Computers in Biology and Medicine

INFORMS QSR Best Paper Competition

INFORMS DEI Best Student Paper Competition

INFORMS DMDA Best Paper Competition

IISE Annual conference, DAIS Track

IISE DAIS Best Student Paper Competition

IEEE Transaction on Automation Science and Engineering

IEEE Transactions on Neural Networks and Learning Systems

IEEE Transactions on Cognitive and Developmental Systems

IEEE Computational Intelligence Magazine

Frontier in Physiology

Computers in Biology and Medicine

INFORMS Data Mining Best Paper Competition

Journal of Electrocardiology

Expert Systems with Applications

Session Chair

“Advanced Data Analytics for Smart Health”, IISE Annual Conference, New Orleans, LA, USA, May 2023

Students
PhD students:

Chuankai Xu

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