Rate My Professor Shekhar Chandra

Dr Shekhar Chandra, also known as Shakes Chandra, serves as a Senior Lecturer and ARC Future Fellow in the School of Electrical Engineering and Computer Science at the University of Queensland. He leads a team of more than 20 researchers specializing in deep learning and artificial intelligence applications for medical image analysis and signal and image processing across various scientific and medical domains. Chandra obtained his PhD in Theoretical Physics from Monash University in Melbourne in 2010, with a thesis titled Circulant theory of the Radon transform. His professional trajectory includes a postdoctoral position at the Australian e-Health Research Centre from 2011 to 2014, a Lecturer role at the University of Queensland from 2017 to 2020, and promotion to Senior Lecturer in 2021. He currently teaches computer science courses such as Theory of Computation and Pattern Recognition and Analysis.

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).