Victor Fragoso
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Information: I design self assessment mechanisms that can reason about the correctness of a decision outcome given a noisy input, e.g., predicting the correctness of a nearest-neighbor classification outcome when matching features, computing non-uniform sampling strategies for speeding up sample-and-consensus model estimations, among others. For my dissertation I have created algorithms that use the statistical theory of extreme values to improve decision making processes in several computer vision and machine learning applications.

In general, I am interested in statistical modeling and scalable computational techniques for computer vision and machine learning. My aim is to create theoretically sound, robust, and scalable computer vision and machine learning algorithms.