Sivarama Krishnan Rajaraman, PhD
Computational Health Research Branch
Building 38A - Lister Hill Center, 10N1003K
Expertise and Research Interests:
Dr. Sivarama Krishnan Rajaraman is working as a research scientist at the National Library of Medicine (NLM), National Institutes of Health (NIH). Dr. Rajaraman received his Ph.D. in Information and Communication Engineering from Anna University, India. He is involved in projects that aim to apply computational sciences and engineering techniques toward advancing life science applications. These projects involve the use of medical images for aiding healthcare professionals in low-cost decision-making at the POC screening/diagnostics. He is a versatile researcher with expertise in machine learning, data science, biomedical image analysis, and computer vision. He has more than 15 years of experience in academia where he taught core and allied subjects in biomedical engineering. He has authored several national and international journal and conference publications in his area of expertise. Dr. Rajaraman is an Editorial Board member of the journals PLOS ONE and MDPI Electronics. He is reviewing manuscripts for more than 75 journals including those published by Nature, Lancet, IEEE, MDPI, Elsevier, and other leading conferences including CVPR, EMBS, CBMS, and MICCAI. Dr. Rajaraman is a Life Member of the Society of Photo-optical Instrumentation Engineers (SPIE), a regular member of the Institute of Electrical and Electronics Engineers (IEEE), IEEE Engineering in Medicine & Biology Society (EMBS), and the Biomedical Engineering Society (BMES).
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.
Publications:Rajaraman S, Zamzmi G, Folio L, Alderson P, Antani S Improved TB classification using bone-suppressed chest radiographs. arXiv preprint arXiv:2104.04518 [eess.IV].
Rajaraman S, Folio LR, Dimperio J, Alderson PO, Antani SK Improved Semantic Segmentation of Tuberculosis-Consistent Findings in Chest X-rays Using Augmented Training of Modality-Specific U-Net Models with Weak Localizations. Diagnostics (Basel). 2021 Mar 30;11(4):616. doi: 10.3390/diagnostics11040616.
Rajaraman S, Folio L, Dimperio J, Alderson P, Antani S Training custom modality-specific U-Net models with weak localizations for improved Tuberculosis segmentation and localization. arXiv preprint arXiv:2102.10607 [cs.CV].
Yu H, Yang F, Rajaraman S, Ersoy I, Moallem G, Poostchi M, Palaniappan K, Antani S, Maude RJ, Jaeger S Malaria Screener: a smartphone application for automated malaria screening. BMC Infect Dis. 2020 Nov 11;20(1):825. doi: 10.1186/s12879-020-05453-1.