Geometry in Biology / Heart wall myofibers are arranged in minimal surfaces to optimize organ function / Project page
Geometry in Biology / Astrocyte geometry / Project page
Geometry in Biology / Moving frames / Project page
Human Vision / Scene symmetry / Project page
Computer Vision / 3D object recognition / Project page
Computer Graphics / Generalized helicoids for hair modeling / Project page
Medical Image Analysis / Diffusion MRI: Curve Inference and Streamline Flows / Project page
Medical Image Analysis / 3D Stochastic Completion Fields for Brain Connectivity Mapping / Project page
Computer Graphics / Generalized helicoids for hair modeling / Project page
Computational Fluid Dynamics / Vortex Flows / Project page
Computational Vision / Blur Modeling by Reverse Projection / Project page
Computational Vision / Medial Surfaces of Polyhedra / Project page


I am a Professor of Computer Science at McGill University, a member of McGill's Centre for Intelligent Machines, and an associate member of the Dept. of Mathematics and Statistics, the Goodman Cancer Research Centre, and MILA. I work in shape analysis, with a particular interest in areas at the interface between disciplines - computer vision, neuroscience, bio-medicine, machine learning and perception. My doctoral work at Brown University was on curve evolution based methods for the analysis of visual form. I graduated from Cathedral and John Connon High School in Bombay and obtained my undergraduate degree in Electrical Engineering from Lafayette College. I presently serve as the Field Chief Editor of Frontiers in Computer Science. In this role I am seeking to promote work that highlights the theoretical, algorithmic and applied aspects of our field, while also considering its impact on other disciplines.


The shape of an object lies at the interface between vision and cognition, yet general purpose theories of shape have been notoriously difficult to formulate. Drawing on techniques from singularity theory, partial differential equations, geometric flows and graph theory, our group is broadly concerned with the problem of shape analysis in computational vision, visual perception, bio-medicine, neuroscience and robotics. A key theme is the development of "generic" models, to support a notion of similarity between qualitatively similar shapes. Distinct from current trends in computer vision in representation learning from data using black box methods, we are interested in learning representations which are informed by regularities of the physical world and the objects within it. A key focus has been on the use of mathematical tools from differential geometry and group theory to allow for appropriate levels of abstraction of visual form.