Integrated Calendar

Abstract: I will present a general solution to the problem of determining all S-dual descriptions for a specific (but very rich) class of N=1 SCFTs. These SCFTs are indexed by decorated toric diagrams, and can be engineered in string theory by probing orientifolds of isolated toric singularities with D3 branes. The S-dual phases are described by quiver gauge theories coupled to specific types of conformal matter which I will describe explicitly.

Epigenetics is the study of heritable changes in phenotype that are not encoded in an organism’s DNA. Epigenetic effects due to persistent changes in gene transcription have been linked to chemical modification of DNA and the proteins that package and regulate DNA in the nucleus, histones. One of the major post-translational modifications of histones is acetylation of lysine residues prevalent in histone tails. The principal readers of histone acetyl lysine marks are bromodomains (BRDs), which are a diverse family of over sixty evolutionary conserved protein-interaction modules. Proteins that contain BRDs have been implicated in the development of a large variety of diseases, including cancer and inflammation. In order to decipher the complex biology of bromodomains and provide evidence linking specific bromodomain targets to disease, we are discovering selective, cell active small molecule inhibitors of bromodomains.

Kernel methods constitute a mathematically elegant framework for general-purpose infinite-dimensional non-parametric statistical inference. By providing a principled framework to extend classical linear statistical techniques to non-parametric modeling, their applications span the entire spectrum of statistical learning. However, training procedures naturally derived via this framework scale poorly and with limited opportunities for parallelization. This poor scalability poses a significant barrier for the use of kernel methods in big data applications. As such, with the growth in data across a multitude of applications, scaling up kernel methods has acquired renewed and somewhat urgent significance.

Random feature maps, such as random Fourier features, have recently emerged as a powerful technique for speeding up and scaling the training of kernel-based methods. However, random feature maps only provide crude approximations to the kernel function, so delivering state-of-the-art results requires huge amount of random features. Nevertheless, in some cases, even when the number of random features is driven to be as large as the training size, full recovery of the generalization performance of the exact kernel method is not attained. In the talk I will show how random feature maps can be used to efficiently perform non-approximate kernel ridge regression, and thus there is no need to compromise between quality and running time. The core idea is to use random feature maps to form preconditioners to be used in solving kernel ridge regression to high accuracy. I will describe theoretical conditions on when this yields an effective preconditioner, and empirically evaluate the method and show it is highly effective for datasets of up to one million training examples.

The discovery of topological insulators, Weyl and Dirac materials, has been arguably the most exciting development in condensed matter physics in decades. It has lead to a resurgence of interest in the role of topological quantum numbers in not only understanding but also classifying certain kinds of solids, akin to how symmetry has been used to classify properties of solids. Weyl and Dirac materials have been predicted to have extraordinary properties, particularly in their transport. In this talk I discuss some transport and magnetic signatures revealed in quantum oscillatory phenomena that may provide a pathway to identify and apply these exotic materials.

Coherences and time-lags are commonly used to infer directionality of information flow in electrophysiology EEG, MEG and MRI. Current approaches, however, enable to calculate only pairwise (linear) coherences. I will describe a novel high-order statistical framework to calculate coherences among multiple coupled time-series. The full mathematical expressions for 4 time-series will be described and its validity will be demonstrated by computer simulations of the Kuramoto model. Quartets of time-series (i.e. brain regions) will be defined as linear, nonlinear or of higher (>4) order. By this, whole systems (e.g. motor, visual) will be categorized as linear or nonlinear. Based on the assumption that MRI phase delays are associated with time of information flow, the temporal hierarchy and directionality of several brain systems will be described. To fully categorize the information flow within 4th order networks, I will introduce the concept of Motifs that describes the pathway trajectories within networks. The advantages of motifs in brain research will be demonstrated by comparing motifs of the ventral versus the dorsal streams systems and in males versus females.