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Spring Semester 2026

Date / Time Speaker Title Location
12 March 2026
14:15-15:15 		Robert Wang
University of Waterloo, CA
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DACO Seminar

Title Derandomizing Matrix Concentration Inequalities from Free Probability
Speaker, Affiliation Robert Wang, University of Waterloo, CA
Date, Time 12 March 2026, 14:15-15:15
Location HG G 19.1
Abstract Recently, sharp matrix concentration inequalities were developed using the theory of free probability. In this work, we design polynomial time deterministic algorithms to construct outcomes that satisfy the guarantees of these inequalities. As direct consequences, we obtain polynomial time deterministic algorithms for the matrix Spencer problem and for constructing near-Ramanujan graphs. Our proofs show that the concepts and techniques in free probability are useful not only for mathematical analyses but also for efficient computations.
Derandomizing Matrix Concentration Inequalities from Free Probabilityread_more
HG G 19.1
* 20 May 2026
14:15-15:30 		Prof. Dr. Shahar Mendelson
Texas A&M University, USA
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DACO Seminar

Title On the structure of marginals in high dimensions
Speaker, Affiliation Prof. Dr. Shahar Mendelson, Texas A&M University, USA
Date, Time 20 May 2026, 14:15-15:30
Location HG F 26.1
Abstract Let X be a centred random vector in \R^d and consider the random matrix \Gamma=\sum_{i=1}^N e_i, whose rows are independent copies of X. Given T \subset S^{d-1}, how much of T's structure is captured by \Gamma T? Our focus here is on fine notions of similarity - specifically, whether the (empirical) distributions of ()_{i=1}^N are close in an appropriate sense to the distributions of , uniformly in \theta \in T. The answer to this question was not known even when X=G, the standard gaussian. In this talk I will present the current state-of-the-art - which holds under minimal assumptions, and in particular leads to an (almost) optimal answer in the gaussian case. If time permits, I will present some of the questions that are still open. Joint work with D. Bartl.
On the structure of marginals in high dimensionsread_more
HG F 26.1

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