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My research revolves around sampling which is the basic building block in many domains including computer graphics, computer vision and quantitative finance. I strive to understand how different sample correlations affect specific applications and I enjoy developing models to characterize these correlations using various mathematical tools. For example, sample distributions directly affect the error during Monte Carlo and Quasi-Monte Carlo (MCQMC) based numerical estimations of global illumination integrals. Similar numerical approximations are extremely important in quantitative analysis in financial math. On the vision side, the data obtained from scanners in the form of noisy point clouds needs constant improvement for fast and better detection, segmentation and reconstruction of underlying objects/material properties. Similar problems are also encountered in computational geometry for remeshing. In all, I am interested in various aspects of sample correlations/distributions. If you have a specific question or if you wonder why some distributions in nature are the way they are, feel free to send me an email.
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