Rate My Professor Shekhar Chandra

Chandra's research focuses on accelerating magnetic resonance imaging through advanced reconstruction, segmentation, and analysis methods, leveraging deep learning and mathematical structures including fractals, turbulence, group theory, and number theory. He has developed shape model-based algorithms for knee, hip, and shoulder joint segmentation, which are deployed as a product on the Siemens syngo.via platform. His contributions extend to quantitative imaging for osteoarthritis, traumatic brain injury prognosis, skin cancer detection, and nephron visualization. With 109 publications since 2006, including 53 journal articles, key works include Deep learning in magnetic resonance image reconstruction (2021), Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative (Medical Image Analysis, 2024), A scalable and efficient UAV-based pipeline and deep learning framework for phenotyping sorghum panicle morphology from point clouds (Plant Phenomics, 2025), SCGC: Self-supervised contrastive graph clustering (Neurocomputing, 2025), and Hybrid discrete and finite element analysis enables fast evaluation of hip joint cartilage mechanical response (Journal of Biomechanics, 2025). Chandra has secured major funding such as the ARC Future Fellowship for next generation magnetic resonance imaging through vision (2026-2029), Australia's Economic Accelerator Innovate Grant for low-field musculoskeletal MRI (2025-2027), ARC Discovery Project for nephron MRI (2022-2026), NHMRC MRFF Traumatic Brain Injury Mission grant (2020-2026), and past NHMRC Development Grant for MR Hip Intervention and Planning System (2018-2022).