Georgia Institute of Technology College of Sciences

By the 4-color theorem, every planar graph on n vertices has an independent set of size at least n/4. Finding a simple proof of this fact is a long-standing open problem. Furthermore, no polynomial-time algorithm to decide whether a planar graph has an independent set of size at least (n+1)/4 is known. We study the analogous problem for triangle-free planar graphs. By Grotzsch' theorem, each such graph on n vertices has an independent set of size at least n/3, and this can be easily improved to a tight bound of (n+1)/3. We show that for every k, a triangle-free planar graph of sufficiently large tree-width has an independent set of size at least (n+k)/3, thus giving a polynomial-time algorithm to decide the existence of such a set. Furthermore, we show that there exists a constant c < 3 such that every planar graph of girth at least five has an independent set of size at least n/c.Joint work with Matthias Mnich.

Allowing formal desuspensions of maps and objects takes the category of topological spaces to the category of spectra, where cohomology is naturally represented. The EHP spectral sequence encodes how far one can desuspend maps between spheres. It's among the most useful tools for computing homotopy groups of spheres. RP^infty has a cell structure with a cell in each dimension and with attaching maps of degrees ...020202... Note that this sequence is periodic. In fact, it is more than the degrees of these maps which are periodic and a map of Snaith relates this periodicity to the EHP sequence.We will develop the EHP sequence, James periodicity and the relationship between the two.

We consider two approaches to address angles between random subspaces: classical random matrix theory and free probability. In the former, one constructs random subspaces from vectors with independent random entries. In the latter, one has historically started with the uniform distribution on subspaces of appropriate dimension. We point out when these two approaches coincide and present new results for both. In particular, we present the first universality result for the random matrix theory approach and present the first result beyond uniform distribution for the free probability approach. We further show that, unexpectedly, discrete uncertainty principles play a natural role in this setting. This work is partially with L. Erdos and G. Anderson.

Probabilistic methods in dynamical systems is a popular area of research. The talk will present the origin of the interplay between both subjects with Poincar\'e's unpredictability and Kolmogorov's axiomatic treatment of probability, followed by two main breakthroughs in the 60es by Ornstein and Gordin. Present studies are concerned with two main problems: transferring probabilistic laws and laws for 'smooth' functions. Recent results for both type of questions are explained at the end.