Conferences  >  Mathematics  >  Graph Theory and Combinatorics  >  United States

Simons Laufer Mathematical Sciences Institute (SLMath)

This workshop will feature leading experts in several major areas of graph theory, including extremal, probabilistic and structural aspects of the field. Introductory lectures will form an important part of the program, providing background and motivation, and aimed at a general mathematical audience. Complementing these, research talks will share exciting recent developments in graph theory.

extremal graph theory, random graphs, probabilistic methods, structural graph theory, Ramsey theory

Institute for Pure & Applied Mathematics (IPAM)

Over the last half a century, Algebra, Combinatorics, and Discrete Geometry have undergone transformations due, in part, to the connections each of these areas have to other fields and the growth of computational approaches used in the study of theoretical mathematics. These areas are closely intertwined, with various algebraic, combinatorial, and geometric objects playing pivotal roles. These objects include monomial ideals, affine semigroup rings, Stanley-Reisner rings, Ehrhart rings, toric rings, Cox Rings, Chow Rings, Gröbner bases on the algebraic side; graphs, matroids, simplicial complexes, polytopes and polyhedral complexes, convex bodies, posets, lattices, arrangements of hyperplanes on the combinatorial and discrete geometry side.

Algebra,    Numerical Analysis and Computational Mathematics

Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University

Researchers working on graph algorithms use a broad range of different criteria for deciding what makes an algorithm efficient. While in theory the dominant benchmark is the asymptotic running time, in practice the story is more nuanced: an algorithm needs to be simple enough to be implementable, fast on graphs of bounded size, space efficient, cache-friendly, and easy to test. While many of these requirements motivate interesting algorithmic questions that are highly relevant in practice, they are often overlooked by the theory community. The goal of the workshop is to foster the exchange of ideas between researchers working on graph algorithms, which have high practical relevance. The workshop will include overview talks on the various perspectives, research talks, an open problem session, and structured time for collaboration.

ICERM Program Staff;     Phone: [+1 (401) 863-5030];     Email: info@icerm.brown.edu

graph algorithms, data science, networks

Algorithms and Data Structures,    Scientific Computing and Data Science

Simons Laufer Mathematical Sciences Institute (SLMath)

Many exciting breakthroughs in combinatorics involve innovative applications of techniques from a wide range of areas such as harmonic analysis, polynomial and linear algebraic methods, spectral graph theory, and representation theory. This workshop will present recent developments in this area and facilitate discussions of research problems.

extremal combinatorics, extremal graph theory, probabilistic combinatorics, discrete geometry, additive combinatorics, combinatorial geometry, incidence geometry, arithmetic progressions, Discrete analysis

Algebra,    Analysis

Institute for Computational and Experimental Research in Mathematics (ICERM)

This workshop will be centered on recent advances in graph rigidity and interactions between rigidity, algebraic statistics, and matroid theory. Three major advances are the recent resolution of the matroid maximality conjecture, the newly developed link to maximum likelihood estimation in Gaussian graphical models, and the recent positive resolution of Lovasz and Yemini's connectivity conjecture for generic rigidity. The workshop will showcase a diverse sample of current work addressing fundamental problems in graph rigidity, algebraic matroids, and algebraic statistics.

ICERM;     Email: info@icerm.brown.edu

Institute for Computational and Experimental Research in Mathematics (ICERM)

The geometric arrangement of a material’s constituents plays an important role in governing its behavior. Concepts from discrete mathematics to describe these geometric arrangements, including notions of rigidity and flexibility, can bring fundamental insight into how a material might respond to stress, be designed, be reconfigured, etc. Beyond materials, the notions of network rigidity can be applied to more abstract networks and geometries, such as those found in data science. This workshop aims to build connections between the field of mathematical rigidity theory, other topics in applied mathematics, and related areas of science and engineering.

ICERM;     Email: info@icerm.brown.edu

Geometry and Topology,    Chemicals and Materials Science

Simons Laufer Mathematical Sciences Institute (SLMath)

In a growing number of applications, one needs to analyze and interpret data coming from massive networks. The statistical problems arising from such applications lead to important mathematical challenges: building novel probabilistic models, understanding the possibilities and limitations for statistical detection and inference, designing efficient algorithms, and understanding the inherent limitations of fast algorithms. The workshop will bring together leading researchers in combinatorial statistics, machine learning, and random graphs in the hope of cross-fertilization of ideas.

combinatorial statistics, random graphs, network inference, network reconstruction, detection, estimation

Complex Networks,    Scientific Computing and Data Science