Grant: Discovery Grants Program - Individual

The overarching goal of my research program is to solve a number of long standing open problems in the intersection of theoretical computer science, graph theory, and probability theory. A commonality between the proposed topics is the use of probabilistic techniques. More precisely, my proposed research program focuses on the following areas. (Objective 1) Randomized algorithms. Although counting the number of proper k-colourings of a graph is a computationally hard problem, Jerrum, Valiant, and Vazirani showed that a nearly uniform sampler gives rise to an approximate enumeration, motivating the question of finding an algorithm to efficiently generate uniformly random proper colourings of a graph; this is a central topic in both computer science and statistical physics.  My colleagues and I made a recent breakthrough, the first progress on the most important question in this area in 19 years. My students and I will push this approach further, both working on the fundamental problem for Glauber dynamics and using similar techniques to bound the mixing time of other Markov chains as well. (Objective 2) Random regular graphs. A question that has attracted much interest in graph theory is: under what conditions can we partition the edge set of a graph into edge disjoint copies of a subgraph? This is fundamentally related to some of the most notorious open areas of research such as finding orientations of certain types, nowhere-zero flows, and colourings of planar graphs. A key new insight is that moving long standing problems from structural graph theory to the random regular setting can provide additional machinery and help to shed light on classical, longstanding problems.  For instance using probabilistic techniques, a coauthor and I recently showed that a random 4-regular graph has a decomposition into 3-stars asymptotically almost surely. An important line of research with far reaching applications is generalizing this result in various ways.  My students and I will pursue developing methods for k-stars in d-regular random graphs as well as decompositions into other trees. (Objective 3) Computational geometry. An active line of inquiry in combinatorics in recent years has been extending classical results to the so-called sparse random setting, where the goal is to show that certain known properties of "dense" combinatorial structures are inherited by their randomly chosen "sparse" substructures. In this spirit my team and I will develop an algorithmic approach that shows if a given algebraic hypergraph is "dense" in a certain sense, then a generic low-dimensional subset of the vertices induces a subhypergraph that is also "dense." Such results have applications in computational geometry and matroid theory. My team and I will also establish a natural generalization of the classical dimension of fibers theorem in algebraic geometry, a result interesting in its own right.