0000-0001-9785-8414 About
Ravi Ratnaik is a Project Research Scientist (PRS-I, Non-Medical) and Research Scholar at the Indian Institute of Technology (Banaras Hindu University), Varanasi, associated with an Indian Council of Medical Research (ICMR) funded project focused on Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), and ASD+ADHD neuroimaging analysis. His research interests include neuroimaging, machine learning, deep learning, graph theory, and brain connectivity analysis using advanced MRI modalities such as Diffusion Tensor Imaging (DTI), functional MRI (fMRI) and structural (MRI).
His current research focuses on developing automated diagnostic frameworks for ASD using structural and functional brain imaging biomarkers integrated with artificial intelligence techniques. He has contributed to studies involving graph theoretical analysis, diffusion metrics, spectrogram-based fMRI analysis, and multimodal neuroimaging approaches for understanding neurodevelopmental disorders.
Ravi has presented his research at several national and international conferences, including the Annual Meeting of the Indian Chapter of the ISMRM and the IEEE Engineering in Medicine and Biology Society (EMBC 2025), Copenhagen, Denmark. His work has received recognition through poster and oral presentations in the fields of neuroimaging and biomedical engineering.
His broader academic interests include computational neuroscience, medical image processing, explainable artificial intelligence in healthcare, and network neuroscience for brain disorder characterization.
Employment
Employment history is unavailable.
Education
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Projects & Funding
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Publications (4)
- Age-Stratified Differences in Morphological Connectivity Patterns in ASD: An sMRI and Machine Learning Approach Save
- Automated Autism Spectrum Disorder Diagnosis using Graph Metrics from Diffusion Tensor Imaging and Machine Learning Save
- Characterizing ASD Subtypes Using Morphological Features from sMRI with Unsupervised Learning Save
- Diagnostic Classification of ASD Improves with Structural Connectivity of DTI and Logistic Regression Save