Sivarama Krishnan Rajaraman photo

Sivarama Krishnan Rajaraman, PhD

Computational Health Research Branch
Research Scientist

Contact InformationNihbc 38A - Lister Hill 1003k 301.827.2383

Expertise and Research Interests:

Dr. Sivaramakrishnan Rajaraman is an accomplished Research Scientist contributing to medical image processing ML/AI at the National Library of Medicine (NLM), National Institutes of Health (NIH), USA. His work revolves around harnessing computational sciences and engineering techniques to revolutionize automated medical decision-making. Dr. Rajaraman’s diverse research portfolio spans machine learning, data science, biomedical image analysis, and computer vision. Before joining NLM, he had 15 years of academic experience where he taught core and allied subjects in electronics, communication, and biomedical engineering while publishing extensively in national and international journals and conferences with a cumulative h-index of 24 (to date). Dr. Rajaraman serves on the Editorial Boards of premier journals like PeerJ Computer Science, PLOS ONE, PLOS Digital Health, and MDPI. He is actively involved in organizing special issues and conference workshops. He is reviewing manuscripts for over 100 prestigious journals and conferences and is holding memberships in SPIE, IEEE, and BMES, demonstrating his commitment to excellence in his field.

Honors and Awards:

Dr. Rajaraman received the NLM Special Acts/Services Group Award in 2018. He is placed in the top 1% of reviewers consecutively on Publons’ global reviewer database for the award years 2017-18 and 2018-19. This award is determined by the number of peer review reports performed during the given award year.


Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani SK. Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images. PLOS Digital Health 3(1): e0000286.

Liang Z, Xue Z, Feng Y, Rajaraman S, Huang JX, Antani SK. Emergency Department Wait Time Forecast based on Semantic and Time Series Patterns in COVID-19 Pandemic. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 3067-3072, doi: 10.1109/BIBM58861.2023.10385758.

Mahmoodi E, Xue Z, Rajaraman S, Antani SK. A Study on Reducing Big Data Image Annotation Burden Through Iterative Expert-In-The-Loop Strategy. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 3097-3102, doi: 10.1109/BIBM58861.2023.10385356.

Rajaraman S, Yang F, Zamzmi G, Xue Z, Antani S. Can deep adult lung segmentation models generalize to the pediatric population? Expert Systems with Applications, Volume 229, Part A, 2023, 120531, ISSN 0957-4174,