PERSONNEL

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Ghada Zamzmi Alzamzmi, PhD

Computational Health Research Branch

Contact Information
Building 38A - Lister Hill Center, 10N1003L
301.827.7573
ghadazamzmi.alzamzmi@nih.gov


Expertise and Research Interests:

Ghada Zamzmi joined the Communication Engineering Branch at Lister Hill National Center for Biomedical Communications (LHNCBC) in February 20179. She received her PhD degree from University of South Florida in 2018. She focuses on using computational sciences and engineering techniques toward advancing healthcare of vulnerable populations (e.g., infants, minority groups). Ghada research interests include medical imaging, affective and cognitive computing, human behavior analysis, and healthcare application. She has >12 journal papers published in top tier journals (e.g., Transactions on Affective Computing), >15 conference publications and two patents. She served in the program committee in several top conferences in Computer Vision including CVPR, NeurIPS, and MICCAI. She chaired several academic workshops and events in her area of interests, led and participated in several mentoring programs. Ghada received different prestigious awards such as MIT Innovator under 35 and IEEE Computational Life Sciences best PhD Thesis Award (2019).

Publications:

Alzamzmi GA, Rajaraman S, Sachdev V, Antani S. Trilateral Attention Network for Real-Time Cardiac Region Segmentation. IEEE Access, vol. 9, pp. 118205-118214, 2021, doi: 10.1109/ACCESS.2021.3107303.

Rajaraman S, Alzamzmi GA, Folio L, Alderson P, Antani S. Chest X-ray bone suppression for improving classification of tuberculosis-consistent findings. Diagnostics. 2021; 11(5):840. https://doi.org/10.3390/diagnostics11050840

Alzamzmi GA, Rajaraman S, Antani S. UMS-Rep: Unified Modality-Specific Representation for Efficient Medical Image Analysis. (2021) Informatics in Medicine Unlocked, 24, art. no. 100571. http://www.journals.elsevier.com/informatics-in-medicine-unlocked doi: 10.1016/j.imu.2021.100571

Alzamzmi GA, Hsu L, Li W, Sachdev V, Antani SK. Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions. IEEE Reviews in Biomedical Engineering, doi: 10.1109/RBME.2020.2988295

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