Lin ching


  • Electrical Engineering and Computer Science
  • School

  • School of Engineering
  • 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 and serves as the director of the Data Analytics program at CUA. Her research interests span across a wide range of areas, including machine learning, data science, biomedical data analytics, pattern recognition, parallel processing, algorithm design, and telecommunication applications.

    Prior to joining the CUA, Dr. Chang was an intramural research fellow at the National Institutes of Health (NIH). She has made significant contributions to computational neuroscience, particularly in the areas of algorithm design and software development for quantitative diffusion tensor magnetic resonance imaging (DT-MRI) analysis. Her expertise has been instrumental on improving the understanding of brain development in healthy children and adolescents through the application of advanced MRI techniques. After joining CUA, Dr. Chang maintains collaborations with various intramural research laboratories at NIH to explore diverse computational approaches for medical and biomedical image analysis. She also engages in a diverse range of collaborative projects with other research centers, such as the Georgetown University Medical Center, Rehabilitation Engineering Research Center, and Broadband Wireless Access and Applications Center.

    Before her NIH career, Dr. Chang held the position of senior software engineer at 3Com Corporation. She led several high-profile telecommunication projects, such as the development of 3Com's unified messaging system, implementation of data encryption, migration of database systems, and various wireless applications initiatives including interactive voice response system and short message service.

    Journal Publications


    1. 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, 5:1059007, doi: 10.3389/frai.2022.1059007, November 2022.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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)
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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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