Past Events

A recent line of work asks: given a clustering optimization problem (like k-means or k-medians), how does the cost of the best explainable clustering compare to that of the best

Link diagrams are combinatorial objects used to represent and study classical links in 3-space. Take your favorite 4-valent planar graph and decorate each vertex with crossing data and you get

Generalized Ray-Knight theorems for edge local times have proved to be a very useful tool for studying the limiting behavior of a number of models of self-interacting random walks (SIRWs)

We will look at two ways we can use tools from machine learning to help us with research in combinatorics. First we discuss reinforcement learning, a method that gives us

The hereditary discrepancy of a set system is a quantitative measure of the pseudorandom properties of the system. Roughly speaking, hereditary discrepancy measures how well one can 2-color the elements

The method of hypergraph containers is a general technique for estimating the number of independent sets in hypergraphs. In this talk we focus our attention on how this method works

In this talk we shall address anticoncentration inequalities for sums of random vectors. In particular, we shall discuss how to asymptotically establish two conjectures: one by Lee Jones (1978) and

**Light lunch served at 12 noon, talk at 12:15 pm. Please RSVP to Nicole Clark-Johnson < nicolec@dimacs.rutgers.edu > if you will be attending lunch.** I will talk about with our

Given a collection of n points and m lines in the plane, we say a point x is incident to a line L if the point x lies on the

Randomness extractors provide a generic way of converting sources of randomness that are merely unpredictable into almost uniformly random bits. While in general, deterministic randomness extraction is impossible, it is

An edge-coloured graph is said to be rainbow if it uses no colour more than once. Extremal problems involving rainbow objects have been a focus of much research as they

I will be presenting my work (along with some joint work with Doron Zeilberger) on how to use symbolic computation to study the area under generalized Dyck paths (i.e. paths

We settle the complexity of dynamic least-squares regression (LSR), where rows and labels (A^(t),b^(t)) can be adaptively inserted and/or deleted, and the goal is to efficiently maintain an ε-approximate solution

Let G be an unweighted directed graph. You're permitted to add no more than a nearly linear number of extra “shortcut” edges to G, subject to their not violating the

We study the weak recovery problem on the r-uniform hypergraph stochastic block model (r-HSBM) with two balanced communities. In this model, n vertices are randomly divided into two communities, and

We conjecture p-adic identities associated to bilateral rational Ramanujan-like series. Then, we show how to recover the rational Ramanujan series from their p-adic mates. Link to video: https://vimeo.com/918969735?share=copy

Given two univariate polynomials, how does one compute their greatest common divisor (GCD)? This problem can be solved in polynomial time using the Euclidean algorithm, and even in quasi-linear time

There are 32 golfers who play golf in groups of 4 once a week. We want to create a schedule for these golfers such that no two golfers play in

Agreement testing (aka direct product testing), checks if consistent local information reveals global structure. Beyond its theoretical connections to probabilistic checkable proofs (PCPs), constructing agreement testers is a fundamental combinatorial

**Light lunch served at 12 noon, talk at 12:15 pm. Please RSVP to Nicole Clark-Johnson < nicolec@dimacs.rutgers.edu > if you will be attending lunch.** The rising U.S. offshore wind sector