Lin ching

Department

  • Electrical Engineering and Computer Science
  • School

  • School of Engineering
  • Expertise

  • Machine Learning
  • Computational Informatics
  • Medical Image Processing and Analysis
  • Data Analytics
  • Algorithm Design
  • Education

  • D.Sc., Computer Science, George Washington University, Washington DC, 1998
  • M.Sc., Computer Science, George Washington University, Washington DC, 1993
  • B.Sc., Information and Computer Engineering, Chung-Yuan Christian University, Taiwan, 1991
  • Bio

    Lin-Ching Chang's research interests include medical informatics, modeling and simulation, machine learning, pattern recognition, parallel processing, and telecommunication applications. During her career at the National Institutes of Health (NIH), she worked on several computational neuroscience projects focusing on algorithm design and software development for medical image processing and quantitative diffusion tensor magnetic resonance imaging (DTI) analysis. She was associated with the NIH pediatric neuroimaging project, a study to learn the brain development in normal healthy children and adolescents using MRI techniques. Prior to NIH, she was a senior software engineer at 3Com Corporation and worked on several commercial telecommunication projects including 3Com's unified messaging system, data encryption, and database systems migration. She also led several wireless applications projects including an interactive voice response system and short message service.

    Selected Publications

    *PhD students at CUA

    1. I Almubark*, L-C Chang, K Shattuck, T Nguyen, R Turner, X Jiang (2020), A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease, Frontiers in Aging Neuroscience, Volume 12, DOI: 10.3389/fnagi.2020.603179. 
    2. V Bui*, L Hsu, L Tran, S Shanbhag, L-C Chang, W Zhou, N Mehta, M Chen (2021), DeepHeartCT: A Fully Automatic Artificial Intelligence System For Cardiac Computed Tomography Angiography Multi-Structure Image Segmentation, Journal of Cardiovascular Computed Tomography 15 (4), S4.
    3. V Bui*, L-Y Hsu, SM Shanbhag, L Tran, WP Bandettini, L-C Chang, Marcus Y Chen (2020), 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.
    4. V Bui*, SM Shanbhag, O Levine, M Jacobs, WP Bandettini, L-C Chang, MY Chen, L-Y Hsu (2020), Simultaneous multi-structure segmentation of the heart and peripheral tissues in contrast enhanced cardiac computed tomography angiography, IEEE Access 8, 16187-16202.
    5. M Jacobs*, M Benovoy, L-C Chang, D Corcoran, C Berry, AE Arai, L-Y Hsu (2021), Automated segmental analysis of fully quantitative myocardial blood flow maps by first-pass perfusion cardiovascular magnetic resonance, IEEE Access, Volume 9, pp. 52796-52811.
    6. M Jacobs*, M Benovoy, L-C Chang, AE Arai, L-Y Hsu (2016), Evaluation of an automated method for arterial input function detection for first-pass myocardial perfusion cardiovascular magnetic resonance
      Journal of Cardiovascular Magnetic Resonance 18 (1), 1-11.
    7. M Jacobs*, L-C Chang, A Pulkkinen, M Romano (2015), Automatic analysis of double coronal mass ejections from coronagraph images, Space Weather 13 (11), 761-777.
    8. E Makki*, L-C Chang (2017), Integration of Social Media, Regional and Service Factors in Quantitative E-commerce Websites Evaluation, International Journal of Current Research 9 (2), 46608–46613.
    9. E Makki*, L-C Chang (2015), Understanding the effects of social media and mobile usage on e-commerce: an exploratory study in Saudi Arabia, International management review 11 (2), 98-109.
    10. E Makki*, L-C Chang (2015), E-commerce acceptance and implementation in saudi arabia: previous, current and future factors, The International Journal of Management Research and Business Strategy 4 (3).
    11. V Kirnosov*, L-C Chang (2016), A Pulkkinen Combining STEREO SECCHI COR2 and HI1 images for automatic CME front edge tracking, Journal of Space Weather and Space Climate 6, A41.
    12. V Kirnosov*, L-C Chang, A Pulkkinen (2015), Automatic CME front edge detection from STEREO white‐light coronagraph images, Space Weather 13 (8), 469-483.
    13. L-C Chang, E El-Araby, VQ Dang, LH Dao (2014), GPU acceleration of nonlinear diffusion tensor estimation using CUDA and MPI, Neurocomputing 135, 328-338. (Citation: 22) 
    14. L-C Chang, L Walker L, C Pierpaoli (2012), 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, pp.1654-1663. (Citation: 117)
    15. L-C Chang, CG Koay, PJ Basser, C Pierpaoli (2008), Linear least‐squares method for unbiased estimation of T1 from SPGR signals, Magnetic Resonance in Medicine 60:2, pp 496-501. (Citation:83)
    16. L-C Chang, CG Koay, C Pierpaoli, PJ Basser (2007), Variance of estimated DTI‐derived parameters via first‐order perturbation methods, Magnetic Resonance in Medicine 57:1, pp 141-149. (Citation: 57)
    17. L-C Chang, DK Jones, C Pierpaoli C (2005), RESTORE: robust estimation of tensors by outlier rejection. Magnetic Resonance in Medicine 53, pp.1088-95. (Citation: 690)

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