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What do you do with Data Science?

I am broadly interested in the theoretical and algorithmic aspects of high-dimensional statistics, information theory, and optimization. My current research interests are in understanding the statistical and computational limits of statistical problems arising in large-scale inference and combinatorial settings. Some representative work: Zhou Fan, Cheng Mao, Yihong Wu, and Jiaming Xu, "Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model, II: Erdős-Rényi Graphs and Universality", Foundations of Computational Mathematics, Jun 2022. Yihong Wu, Jiaming Xu and Sophie H. Yu, "Testing correlation of unlabeled random graphs", The Annals of Applied Probability, 2022+. Cheng Mao and Yihong Wu, "Learning Mixtures of Permutations: Groups of Pairwise Comparisons and Combinatorial Method of Moments", The Annals of Statistics, 2022+. Yihong Wu and Harrison H. Zhou, "Randomly initialized EM algorithm for two-component Gaussian mixture achieves near optimality in O(sqrt{n}) iterations", Mathematical Statistics and Learning, 4 (2021), 143–220. Yanjun Han, Soham Jana, and Yihong Wu, "Optimal prediction of Markov chains with and without spectral gap", Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021. Vivek Bagaria, Jian Ding, David Tse, Yihong Wu, and Jiaming Xu, "Hidden Hamiltonian Cycle Recovery via Linear Programming", Operations Research, volume 68, number 1, Jan 2020. Yihong Wu and Pengkun Yang, "Optimal estimation of Gaussian mixtures via denoised method of moments", The Annals of Statistics, vol. 48, no. 4, pp. 1981-2007, 2020. Alon Orlitsky, Ananda Theertha Suresh, and Yihong Wu, "Optimal prediction of the number of unseen species", Proceedings of the National Academy of Sciences, vol. 113, no. 47, pp. 13283–13288, Nov 2016.

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