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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
10 June 2026
13:15-14:15 		Anouk Brose
University of California, Davis, US
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DACO Seminar

Title Lattice Diameters of Polytopes
Speaker, Affiliation Anouk Brose, University of California, Davis, US
Date, Time 10 June 2026, 13:15-14:15
Location HG G 19.1
Abstract The lattice diameter of a polytope measures the maximum number of collinear lattice points in a polytope, equivalently, the maximum number of lattice points in a one-dimensional section of a polytope. We study structural properties of the lattice diameter and show connections to the shortest vector problem and the lattice width of a polytope. Algorithmically, we give a fixed-dimension polynomial time algorithm that computes the lattice diameter of a rational polytope, and show that this problem is NP-hard when the dimension is non-fixed. Furthermore, we study how segments attaining the lattice diameter behave under dilation of the polytope and prove an Ehrhart-theory type result. This is joint work with G. Averkov, J.A. De Loera, G. Lopez-Campos, and A. Torres.
Lattice Diameters of Polytopesread_more
HG G 19.1
* 23 June 2026
15:00-16:00 		Chris Camaño
Caltech, USA
Details

DACO Seminar

Title Efficient moment calculations for random tensor networks using Penrose diagrams
Speaker, Affiliation Chris Camaño, Caltech, USA
Date, Time 23 June 2026, 15:00-16:00
Location HG G 19.2
Abstract The statistical behavior of a random tensor is controlled by its moments. Unfortunately, these moments are notoriously challenging to describe analytically. In this talk, I will present the Penrose diagrammatic notation as a visual calculus that organizes these calculations into a series of diagrams. As an example, we will investigate the fourth-moment behavior of a particular random tensor called a random matrix product state (rMPS), also known as a random tensor train. Using a percolation argument, we will characterize the regime where an rMPS agrees with a standard Gaussian random tensor up to the first four moments. No prior background in tensors is assumed. Joint work with Joel A. Tropp, Ethan Epperly, and Raphael Meyer.
Efficient moment calculations for random tensor networks using Penrose diagramsread_more
HG G 19.2
* 15 July 2026
13:30-14:30 		Prof. Dr. Hyung-Chan An
Yonsei University, South Korea
Details

DACO Seminar

Title Handling LP-Rounding for Hierarchical Clustering and Fitting Distances by Ultrametrics
Speaker, Affiliation Prof. Dr. Hyung-Chan An, Yonsei University, South Korea
Date, Time 15 July 2026, 13:30-14:30
Location HG G 19.1
Abstract In this talk, we present an improved approximation algorithm for hierarchical correlation clustering. Given L layers of complete graphs on a common vertex set V, where each edge is labeled either + or -, the problem is to compute a clustering of V for each layer so that lower-layer clusterings refine upper-layer ones and the total weighted number of disagreements over all layers is minimized. Here, a + edge is counted as a disagreement if its endpoints are separated, and a - edge if its endpoints are placed in the same cluster. This problem is both a natural generalization of correlation clustering (the case L=1) and a formulation of the L1 ultrametric fitting problem arising in numerical taxonomy. Unlike the single-layer setting, for which substantial algorithmic progress has been made in recent years, the hierarchical case has seen little progress since the first constant-factor approximation algorithm of Cohen-Addad, Das, Kipouridis, Parotsidis, and Thorup. We give a new LP-rounding algorithm achieving an approximation ratio of 25.8, significantly improving the previous guarantee. Joint work with Mong-Jen Kao, Changyeol Lee, and Mu-Ting Lee (FOCS '25).
Handling LP-Rounding for Hierarchical Clustering and Fitting Distances by Ultrametricsread_more
HG G 19.1

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