Our Theory Seminar kicked off in Spring 2018.

## Upcoming talks

We are currently on break for the summer. Watch this space!

## Past talks

Abstract: In network routing users often tradeoff different objectives in selecting their best route. An example is transportation networks, where due to uncertainty of travel times, drivers may tradeoff the average travel time versus the variance of a route. Or they might tradeoff time and cost, such as the cost paid in tolls.

We wish to understand the effect of two conflicting criteria in route selection, by studying the resulting traffic assignment (equilibrium) in the network. We investigate two perspectives of this topic: (1) How does the equilibrium cost of a risk-averse population compare to that of a risk-neutral population? (i.e., how much longer do we spend in traffic due to being risk-averse) (2) How does the equilibrium cost of a heterogeneous population compare to that of a comparable homogeneous user population?

We provide characterizations to both questions above.

Based on joint work with Richard Cole, Thanasis Lianeas and Nicolas Stier-Moses.

At the end I will mention current work of my research group on algorithms and mechanism design for power systems.

This talk aims to describe my research, which seeks to develop the tools needed to characterize the power of quantum computation, both in the very near-term and the indefinite future. These tools will provide the foundation for building the next generation of useful quantum algorithms, but will also help guide the course of quantum experiment.

The talk will be accessible to a general computer science audience.

Some relevant papers are as follows.

(1) Generalized Wong sequences and their applications to Edmonds’ problems, by G. Ivanyos, M. Karpinski, Y Qiao, M. Santha. arXiv:1307.6429.

(2) Constructive non-commutative rank is in deterministic polynomial time, by G. Ivanyos, Y Qiao, K. V. Subrahmanyam. arXiv:1512.03531.

(3) Operator scaling: theory and applications, by A. Garg, L. Gurvits, R. Oliveira, A. Wigderson. arXiv:1511.03730.

(4) Linear algebraic analogues of the graph isomorphism problem and the Erdős-Rényi model, by Y. Li, Y. Qiao. arXiv:1708.04501.

(5) From independent sets and vertex colorings to isotropic spaces and isotropic decompositions, by X. Bei, S. Chen, J. Guan, Y. Qiao, X. Sun. In preparation; paper available soon.

^{1+o(1)}β

^{o(1)}-time algorithm for generating uniformly random spanning trees in weighted graphs with max-to-min weight ratio β. In the process, we illustrate how fundamental tradeoffs in graph partitioning can be overcome by eliminating vertices from a graph using Schur complements of the associated Laplacian matrix.

Our starting point is the Aldous-Broder algorithm, which samples a random spanning tree using a random walk. As in prior work, we use fast Laplacian linear system solvers to shortcut the random walk from a vertex v to the boundary of a set of vertices assigned to v called a "shortcutter." We depart from prior work by introducing a new way of employing Laplacian solvers to shortcut the walk. To bound the amount of shortcutting work, we show that most random walk steps occur far away from an unvisited vertex. We apply this observation by charging uses of a shortcutter S to random walk steps in the Schur complement obtained by eliminating all vertices in S that are not assigned to it.

In this talk, I introduce parametrized complexity: a branch of computational complexity that classifies problems based on their hardness with respect to several parameters of the input in addition to the input size; hence it gives a much more fine-grained classification compared to the traditional worst-case analysis that is based on only the input size. Then, I present several parametrized algorithms for computing low-cost maps between geometric objects. The running time of these algorithms are parametrized with respect to topological and geometric parameters of the input objects. For example, when the input is a graph, a topological parameter can be its treewidth that measures to what extent the graph looks like a tree, and a geometric parameter can be the intrinsic dimensionality of the metric space induced by shortest paths in the graph.

Such algorithms work so well that, in certain applications unrelated to network analysis, such as image segmentation, it is useful to associate a network to the data, and then apply spectral clustering to the network. In addition to its application to clustering, spectral embeddings are a valuable tool for dimension-reduction and data visualization.

The performance of spectral clustering algorithms has been justified rigorously when applied to networks coming from certain probabilistic generative models.

A more recent development, which is the focus of this lecture, is a worst-case analysis of spectral clustering, showing that, for every graph that exhibits a certain cluster structure, such structure can be found by geometric algorithms applied to a spectral embedding.

Such results generalize the graph Cheeger’s inequality (a classical result in spectral graph theory), and they have additional applications in computational complexity theory and in pure mathematics.

I will first describe negative results for private data analysis via a connection to cryptographic objects called fingerprinting codes. These results show that an (asymptotically) optimal way to solve natural high-dimensional tasks is to decompose them into many simpler tasks. In the second part of the talk, I will discuss concentrated differential privacy, a framework which enables more accurate analyses by precisely capturing how simpler tasks compose.

In this talk, we overview the algebraic structure of perfect matchings from the viewpoint of representation theory. We give perfect matching analogues of some familiar notions and objects in the field of Boolean functions and discuss a few fundamental differences that arise in the non-Abelian setting. The talk will conclude with a summary of some results in extremal combinatorics, optimization, and computational complexity that have been obtained from this algebraic point of view.

^{2}) bits of space in the worst case, improving the Ω(n/ϵ

^{2}) bound of Andoni et al. and matching the best known upper bound achieved by spectral sparsifiers. Our proof is based on a rigidity phenomenon for cut (and spectral) approximation which may be of independent interest: any two d−regular graphs which approximate each other's cuts significantly better than a random graph approximates the complete graph must overlap in a constant fraction of their edges.

In this talk, we will take a road trip together through a small part of the landscape. We will start with representations of planar curves and their deformations, together with their appearance and many uses in various disciplines of math and cs. Then we focus on a discrete representation of curve deformations called homotopy moves. We will sketch the ideas behind a few recent results on untangling planar curves using homotopy moves, its generalization on surfaces, and the implications on solving planar graph optimization problems using electrical transformations.

There are no assumptions on background knowledge in topology. Open questions (not restricted to planar curves) will be provided during the talk.