Yale University's Yihong Wu is part of a wave of new faculty participating in a university-wide initiative to integrate data science and mathematical modeling research across campus.
Wu's work focuses on the intersection of high-dimensional statistics, information theory, and computer science, and he has made fundamental contributions to the problem of estimating the number of unseen symbols in a population. Specifically, Wu plans to create algorithms that give users provable, guaranteed results that they can access quickly.
Wu and his collaborators sometimes use optimization techniques called convex relaxations to solve a relaxed version of the original problems.
He also uses "belief propagation," an iterative algorithm normally used in statistical physics that passes messages back and forth to fill in gaps in information.
Wu employed these techniques to revisit the classic problem of predicting the number of unseen species based on a collection of samples.
From Yale University News
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