Jitendra Malik

Jitendra Malik has made many fundamental contributions in computer vision and image analysis including 1. Perona-Malik anisotropic diffusion equation, a non-linear variant of the heat equation, enables images to be smoothed while preserving edges, and is widely used for denoising medical and biological images. 2. Image based modeling and rendering, with the Facade system for building 3D architectural models from images (w/ Debevec & Taylor) and computational photography with an algorithm for capturing high dynamic range images using multiple exposures (w/ Debevec); these techniques are used for creating visual effects in Hollywood. 3. Normalized Cut algorithm for clustering using spectral graph theory (w/ Shi) and its application to contour detection and image segmentation, as well as general clustering problems. 4. Novel image descriptors such as shape contexts and poselets, and machine learning techniques, e.g., fast intersection kernel SVMs and R-CNN for visual object recognition. 5. Computational models of human texture perception - segmentation (w/ Perona) and shape from texture (w/ Rosenholtz). His work has resulted in many awards & honors including election to the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences.