Lin Ching Chang

Department

  • Computer Science

    • School

  • College of Engineering, Physics, and Computing

    • Expertise

  • Machine Learning
  • Data Science
  • Bio/Medical Data Analytics
  • Algorithm Design
  • Software Engineering

    • Bio

    Dr. Lin-Ching Chang is a Full Professor of Computer Science, Chair of the Department of Computer Science, and Director of the Data Analytics program at The Catholic University of America. Her research lies at the intersection of artificial intelligence, machine learning, and data science, with applications in biomedical data analytics, medical imaging, pattern recognition, and high-performance computing. Her work emphasizes the development of computational methods that translate complex data into actionable insights, particularly in healthcare and scientific domains.

    Prior to joining Catholic University, Dr. Chang was an Intramural Research Fellow at the National Institutes of Health (NIH), where she made significant contributions to computational neuroscience. Her research focused on algorithm design and software development for quantitative diffusion tensor magnetic resonance imaging (DT-MRI), advancing the understanding of brain development in children and adolescents. She continues to maintain active collaborations with NIH researchers and other interdisciplinary teams, extending her work into broader areas of biomedical image analysis and AI-driven healthcare solutions.

    Dr. Chang has led and collaborated on a wide range of interdisciplinary research initiatives with institutions such as Georgetown University Medical Center, the Rehabilitation Engineering Research Center, and the Broadband Wireless Access and Applications Center. Her current work includes AI applications in medical imaging, assistive technologies, and data-driven modeling, reflecting a strong commitment to impactful, real-world problem solving.

    Before her academic career, Dr. Chang worked as a Senior Software Engineer at 3Com Corporation, where she led the development of several large-scale telecommunications systems, including unified messaging platforms, data encryption solutions, database migrations, and wireless communication applications such as interactive voice response and SMS systems. This industry experience continues to inform her approach to applied research and system design.

    Across her career, Dr. Chang has demonstrated leadership in research, education, and program development, contributing to the growth of AI and data science initiatives while mentoring students and fostering interdisciplinary collaboration.

    Journal Publications

    2007-2025

    1. T Tran, L-C Chang, P Lum, A Deep Learning Approach to Access Upper Extremity Movement Using Accelerometry Data, Frontiers in Artificial Intelligence, doi: 10.3389/frai.2025.1547127, November 2025.
    2. A Thai, L-C Chang, C Pierpaoli, MO Irfanoglu, Exploiting Four-way Phase-encoding Benefits for Robust Detection and Correction of EPI Artifacts: Application to Residual Ghosts in Diffusion MRI, Journal of Medical Imaging, doi: 10.1016/j.mri.2025.110454, October 2025.
    3. EM Nieto, E Lujan, CA Mendoza, Y Arriaga, C Fierro, T Tran, L-C Chang, AN Gurovich, PS Lum, S Geed, Accelerometry and the Capacity–Performance Gap: Case Series Report in Upper-Extremity Motor Impairment Assessment Post-Stroke, Bioengineering 12 (6), 615; doi: 10.3390/bioengineering12060615, June 2025.
    4. A Abdaltawab, L-C Chang, M Mansour, M Koubeissi. How accurate are machine learning models in predicting anti-seizure medication responses: A systematic review, Epilepsy & Behavior, doi: 10.1016/j.yebeh.2024.110212, February 2025.
    5. L-C Chang, A Angelopoulou. “Editorial: Women in AI medicine and public health 2022”, Frontiers in Big Data, Volume 6, doi: 10.3389/fdata.2023.1303367, October 2023.
    6. V Bui, L-Y Hsu, L-C Chang, A-Y Sun, L Tran, SM Shanbha, W Zhou, NN Mehta, and MY Chen. “DeepHeartCT: A Fully Automatic Artificial Intelligence Hybrid Framework Based on Convolutional Neural Network and Multi-Atlas Segmentation for Multi-Structure Cardiac Computed Tomography Angiography Image Segmentation”, Frontiers in Artificial Intelligence: Medicine and Public Health, Volume 5, doi: 10.3389/frai.2022.1059007, November 2022.
    7. M Jacobs, M Benovoy, L-C Chang, D Corcoran, C Berry, AE Arai, L-Y Hsu. “Automated Segmental Analysis of Fully Quantitative Myocardial Blood Flow Maps by First-Pass Perfusion Cardiovascular Magnetic Resonance”, IEEE Access, 9: 52796-52811, doi: 10.1109/access.2021.3070320, April 2021.
    8. J Jenkins, L-C Chang, BQ Tran, H Szu. “Exploring Hormone Communication and Perception of Emotion”, MOJ Applied Bionics and Biomechanics, 5 (1): 8-17, doi: 10.15406/mojabb.2021.05.00150, March 2021.
    9. PS Lum, L Shu, EM Bochniewicz, T Tran, L-C Chang, J Barth, AW Dromerick. “Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method”, Neurorehabilitation and Neural Repair, 34(12): 1078–1087, doi: 10.1177/1545968320962483, December 2020.
    10. I Almubark, L-C Chang, K Shattuck, T Nguyen, R Turner, X Jiang. “A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease”, Frontiers in Aging Neuroscience, 12: 603179, doi: 10.3389/fnagi.2020.603179, December 2020.
    11. V Bui, L-Y Hsu, SM Shanbhag, L Tran, WP Bandettini, L-C Chang, MY Chen. “Improving Multi-Atlas Cardiac Structure Segmentation of Computed Tomography Angiography: A Performance Evaluation Based on a Heterogeneous Dataset”, Computers in Biology and Medicine, 125: 104019, doi: 10.1016/j.compbiomed.2020.104019, October 2020.
    12. T Nguyen, V Bui, A Thai, V Lam, CB Raub, L-C Chang, G Nehmetallah, “Virtual Organelle Self-Coding for Fluorescence Imaging Via Adversarial Learning”, Journal of Biomedical Optics, 25(9): 096009, doi: 10.1117/1.JBO.25.9.096009, September 2020.
    13. VK Lam, TC Nguyen, V Bui, BM Chung, L-C Chang, G Nehmetallah, CB Raub. “Quantitative Scoring of Epithelial and Mesenchymal Qualities of Cancer Cells Using Machine Learning and Quantitative Phase Imaging”, Journal of Biomedical Optics, 25(2): 026002, doi: 10.1117/1.JBO.25.2.026002, February 2020.
    14. V Bui, SM Shanbhag, O Levine, M Jacobs, WP Bandettini, L-C Chang, MY Chen, L-Y Hsu. “Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography”, IEEE Access, 8: 16187–16202, doi: 10.1109/ACCESS.2020.2966985, January 2020.
    15. HH Szu, L-C Chang, H Chu, R Kolluru, S Foo, J Wu. “The 3rd Wave AI Requirements”, MOJ Applied Bionics and Biomechanics, 3(1):18-22, doi: 10.15406/mojabb.2019.03.00094, February 2019.
    16. X Liang, N Lu, L-C Chang, TH Nguyen, A Massoudieh. “Evaluation of Bacterial Run and Tumble Motility Parameters Through Trajectory Analysis”, Journal of Contaminant Hydrology, 211:26-38, doi: 10.1016/j.jconhyd.2018.03.002, April 2018.
    17. T Nguyen, V Bui, V Lam, CB Raub, L-C Chang, G Nehmetallah. “Automatic Phase Aberration Compensation for Digital Holographic Microscopy Based on Deep Learning Background Detection”, Optics Express, 25 (13): 15043-15057, doi: 10.1364/OE.25.015043, June 2017.
    18. E Makki, L-C Chang. “Integration of Social Media, Regional and Service Factors in Quantitative E-commerce Websites Evaluation”. International Journal of Current Research, 9(2): 46608–46613, February 2017.
    19. V Kirnosov, L-C Chang, A Pulkkinen. “Combining STEREO SECCHI COR-2 and HI-1 Images for Automatic CME Front Edge Tracking”, Journal of Space Weather and Space Climate, 6: A41, doi: 10.1051/swsc/2016037, December 2016.
    20. M Jacobs, M Benovoy, L-C Chang, A E Arai, L-Y Hsu. “Evaluation of an Automated Method for Arterial Input Function Detection in First-Pass Myocardial Perfusion Magnetic Resonance Images and Its Impact on Quantitative Perfusion”, Journal of Cardiovascular Magnetic Resonance, 18:17, doi: 10.1186/s12968-016-0239-0, April 2016.
    21. L Walker, L-C Chang, A Nayak, M O Irfanoglu, K N Botteron, J McCracken, R C McKinstry, M J Rivkin, D-J Wang, J Rumsey, C Pierpaoli, the Brain Development Cooperative Group. “The Diffusion Tensor Imaging (DTI) Component of The NIH MRI Study of Normal Brain Development (PedsDTI)”, NeuroImage, 124(Pt-B): 1125-1130, doi: 10.1016/j.neuroimage.2015.05.083, January 2016.
    22. M Jacobs, L-C Chang, A Pulkinnen, M Romano. “Automatic Analysis of Double Coronal Mass Ejections From Coronagraph Images”, Space Weather, 13(11): 761-777, doi: 1002/2015SW001260, November 2015. (Selected as the journal cover article)
    23. L Dao, B Glancy, B Lucotte, L-C Chang, R S Balaban, L-Y Hsu. “A Model-Based Approach for Microvasculature Structure Distortion Correction in Two-Photon Fluorescence Microscopy Images”, Journal of Microscopy, 260(2):180-193, doi: 10.1111/jmi.12281, November 2015.
    24. V Kirnosov, L-C Chang, A Pulkkinen. “Automatic CME Front Edge Detection from STEREO White‐light Coronagraph Images”, Space Weather, 13(8): 469–483, doi: 10.1002/2015SW001190, July 2015.
    25. E Makki, L-C Chang. “E-commerce Acceptance and Implementation is Saudi Arabia: Previous, Current and Future Factors”, International Journal of Management Research and Business Strategy, 4(3): 29-44, July 2015.
    26. E Makki, L-C Chang. “Understanding the Effects of Social Media and Mobile Usage on E-Commerce: An Exploratory Study in Saudi Arabia”, International Management Review, 11(8): 98-109, January 2015.
    27. L Dao, B Lucotte, B Glancy, L-C Chang, L-Y Hsu, R S Balaban. “Use of Independent Component Analysis to Improve Signal-to-noise Ratio in Multi-probe Fluorescence Microscopy”, Journal of Microscopy, 256(2): 133-44, doi: 10.1111/jmi.12167, November 2014.
    28. L-C Chang, E El-Araby, V Dang, L Dao. “GPU Acceleration of Nonlinear Diffusion Tensor Estimation Using CUDA and MPI”, Neurocomputing, 135: 328–338, doi:10.1016/j.neucom.2013.12.035, July 2014.
    29. L-C Chang, L Walker L, C Pierpaoli. “Informed RESTORE: A Method for Robust Estimation of Diffusion Tensor from Low Redundancy Datasets in the Presence of Physiological Noise Artifacts”, Magnetic Resonance in Medicine, 68(5): 1654-1663, doi: 10.1002/mrm.24173, November 2012.
    30. S Rashidian, L-C Chang, P Asadollahi. “Application of Statistical Pattern Recognition in a Solid-Fluid Interaction Problem”, Electronic Journal of Geotechnical Engineering, 16(N): 1629-1637, January 2011.
    31. L Walker, L-C Chang, N Sharma, L Cohen, R Verma, C Pierpaoli. “Effects of Physiological Noise in Population Analysis of Diffusion Tensor MRI Data”, NeuroImage, 54(2):1168-1177, doi: 10.1016/j.neuroimage.2010.08.048, January 2011.
    32. L-C Chang, CG Koay, PJ Basser, C Pierpaoli. “A Linear Least Squares Method for Unbiased Estimation of T1 from SPGR Signals”, Magnetic Resonance in Medicine, 60(2): 496–501, doi: 10.1002/mrm.21669, August 2008.
    33. CG Koay, U Nevo, L-C Chang, C Pierpaoli, PJ Basser. “The Elliptical Cone of Uncertainty and Its Normalized Measures in Diffusion Tensor Imaging”. IEEE Transaction on Medical Imaging 27(6): 834-846, doi: 10.1109/TMI.2008.915663, June 2008.
    34. RZ Freidlin RZ, E Özarslan, ME Komlosh, L-C Chang, CG Koay, DK Jones, PJ Basser. “Parsimonious Model Selection for Tissue Segmentation and Classification Applications: A Study Using Simulated and Experimental DTI data”, IEEE Transaction on Medical Imaging 26(11): 1576-1584, doi: 10.1109/TMI.2007.907294, November 2007.
    35. CG Koay, L-C Chang, C Pierpaoli, PJ Basser. “Error Propagation Framework for Diffusion Tensor Imaging via Diffusion Tensor Representations”, IEEE Transaction on Medical Imaging, 26(8): 1017-1034, doi: 10.1109/TMI.2007.897415, August 2007.
    36. L-C Chang, CG Koay, C Pierpaoli, PJ Basser. “The Variance of DTI-derived Parameters via First-Order Perturbation Methods”, Magnetic Resonance in Medicine, 57(1):141-149, doi: 10.1002/mrm.21111, January 2007.

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