Dr. Mohinder Sharma is a dedicated researcher in forensic radiology, forensic anthropology, craniofacial morphometry, and medical imaging, with professional experience in the Department of Radiodiagnosis & Imaging at PGIMER, Chandigarh, since 2015. His work integrates advanced imaging modalities, quantitative analysis, statistical modelling, and machine learning to address challenges in forensic identification.

Research Expertise

His expertise encompasses multidetector computed tomography (MDCT), MRI, Zero Echo Time MRI, 3D image reconstruction, DICOM analysis, craniofacial landmark identification, and quantitative morphometry. He is proficient in statistical approaches including discriminant function analysis, principal component analysis, MANOVA, and multiple regression, along with machine-learning-based classification techniques.

Research Contributions

Dr. Sharma's research primarily focuses on developing population-specific forensic identification standards for the Northwest Indian population. His investigations cover craniofacial morphometry, sex estimation using craniofacial bones and the patella, external occipital protuberance morphology, nasal profile and width estimation, craniofacial soft-tissue thickness, mandibular morphometry, and forensic facial approximation.

His PhD research, “Personal Identification by Craniometry with the Help of MDCT: A Forensic Radiological Approach,” involved a substantial dataset of 525 individuals aged 18–80 years, employing advanced statistical modelling and machine-learning approaches for sex estimation and personal identification.

Publications & Scholarly Output

Dr. Sharma has contributed to peer-reviewed international publications in forensic imaging, legal medicine, anatomy, and related disciplines. His recent work includes studies published in Legal Medicine, Forensic Imaging, International Journal of Legal Medicine, and the Journal of the Anatomical Society of India. His research has also resulted in accepted and under-review manuscripts addressing CT-based craniometry, machine learning, nasal dimensions, and Zero Echo Time MRI.

Innovation & Research Achievements

A notable aspect of his work is the development of population-specific craniofacial standards and CT-based forensic identification models. He has established regression equations for craniofacial prediction, applied machine learning to forensic sex estimation, and explored advanced MRI techniques for forensic craniofacial measurements. He is also associated with a patent, “Forensic Trace Material Detection and Analysis Device” (Design No. 471014-001, dated 25 August 2025).

Peer Review & Academic Service

Dr. Sharma actively contributes to scholarly communication as a peer reviewer for national and international journals in forensic medicine, forensic anthropology, radiology, medical imaging, anatomy, and biomedical sciences. His reviewing activities involve assessing original research, systematic reviews, and case reports and providing methodological and scientific recommendations.

Awards & Professional Recognition

His professional accomplishments include a Best Paper Award, Academic Excellence Award, and Travel Grant. He has also been recognized as a Young Scientist for his contributions to forensic radiology, craniofacial morphometry, and medical imaging. He has presented his research at national and international scientific conferences.

Research Impact

Dr. Sharma's scholarly profile includes 42 Google Scholar citations, an h-index of 3, and an i10-index of 1, along with a ResearchGate score of 21.7. His research demonstrates a strong emphasis on translating medical imaging technologies into practical forensic identification applications.

Eligibility for Young Researcher Award 

Dr. Mohinder Sharma is eligible for the Young Researcher Award in Scientific Laurels based on his focused and emerging research career in forensic radiology and forensic anthropology. His substantial MDCT-based research, population-specific forensic identification models, application of machine learning, peer-reviewed publications, ongoing manuscripts, patent, peer-review contributions, conference presentations, and recognition as a Young Scientist collectively demonstrate research innovation, scientific productivity, and promising academic impact. His interdisciplinary approach combining radiology, forensic anthropology, quantitative morphometry, statistics, and artificial intelligence further strengthens his candidature for the Young Researcher Award.