Shailja – MICCAI Doctoral Consortium "Overall Winner Award"

Shailja received the award for her thesis, “Reeb Graphs for Topological Connectomics”. Her dissertation develops a principled framework to model the connections in the human brain. Our brain consists of billions of neurons that are densely interconnected. Shailja’s doctoral dissertation, advised by B. S. Manjunath, Distinguished Professor of Electrical and Computer Engineering, proposes a unique solution to model the brain’s structural network. Her work encodes sparse yet topologically meaningful representations in a computationally scalable and interpretable form. This enables unprecedented quantitative insights into how neurodiseases alter brain structure and advances our understanding of how the brain's structure is related to its function.

The multi-format MICCAI Doctoral Consortium is led by the MICCAI Student Board (MSB) in collaboration with the MICCAI Career Advancement Working Group. This consortium is designed to highlight the work of outstanding PhD graduates globally who have led groundbreaking research in the thematic areas of the MICCAI technical program. Shailja’s thesis stood out as the overall winner at the consortium, in addition to being selected for a long oral talk and a poster at the main conference. The judges noted the uniqueness of Shailja’s thesis as it connects two previously distinct fields: topological information from white-matter fibers in the brain and classical computer vision in neuroimaging. Shailja was given this award at Daejeon, Republic of Korea, during the conference award ceremony — the capstone of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) held from September 23-27, 2025.

Shailja is continuing her research journey at Stanford University, towards understanding the structure-function coupling in the human brains by building on the core idea of her thesis at UCSB. At Stanford, she is leading an effort that would map the structural connectivity in the human brain with electrophysiology, using stereo-EEG recordings to map and quantify structure-function relationships.

Shailja’s Bio

Shailja is a postdoctoral scholar at Stanford University working with Prof. Jennifer McNab in the Radiological Sciences Laboratory. Her research is focused on computational geometry algorithms for diffusion MRI and tractography-based targeting for functional neurosurgeries. Shailja received her PhD in Electrical and Computer Engineering from the University of California, Santa Barbara (UCSB) under the guidance of Prof. B. S. Manjunath and her bachelor's degree from the Electrical Engineering department at the Indian Institute of Technology (IIT), Kharagpur. Shailja’s PhD dissertation proposes a novel Reeb graph-based algorithm to model the neuronal networks in the human brain to study topological connectomics. For this research, she was awarded the Lancaster Best Doctoral Dissertation award at UC Santa Barbara. Shailja’s research vision is to model multi-modal healthcare data for precise diagnostics using AI and integration of domain knowledge to “close-the-loop” between surgeons, research scientists, and engineers.

Relevant Journal Publications

• "ReeBundle: A Method for Topological Modeling of White Matter Pathways Using Diffusion MRI," EEE Transactions on Medical Imaging, vol. 42, no. 12, pp. 3725-3737, Dec. 2023
• "Artificial Intelligence for Automatic Analysis of Shunt Treatment in Presurgery and Postsurgery Computed Tomography Brain Scans of Patients With Idiopathic Normal Pressure Hydrocephalus," Neurosurgery 95(6):p 1329-1337, December 2024.