Geometric apparatuses are frequently inundated with too much structural detail to be computationally tractable, while traditional topological tools often incur too much simplification of the original data to be practically useful. Persistent homology, a new branch of algebraic topology, is able to bridge the gap between geometry and topology. In this talk, I will discuss a few new developments in persistent homology. First, we introduce multiscale-multiresolution persistent homology to describe the topological fingerprints and topological transitions of nano-bio materials. Additionally, multidimensional persistence is developed for topological denoising and revealing the topology-function relationship in biomolecular data. Moreover, molecular topological fingerprints are utilized to resolve ill-posed inverse problems in cryo-EM structure determination. Finally, objective-oriented persistent homology is constructed via the variational principle and differential geometry for proactive feature extracting from big data sets, which leads to topological partial differential equations (TPDEs). 

This talk is about a curious phenomenon, which concerns the reliable estimation of principal components in the face of severe corruptions. Here, the scientist is given a data matrix which is the sum of an approximately low-rank matrix and a sparse matrix modeling corrupted entries. In addition, many entries may be missing. Hence, we have a blind de-mixing problem in which the goal is to recover the low-rank structure and find out which entries have been corrupted. We present a novel approach to this problem with very surprising performance guarantees as well as a few applications in computer vision and biomedical imaging, where this technique opens new perspectives. 

In many imaging problems such as X-ray crystallography, detectors can only record the intensity or magnitude of a diffracted wave as opposed to measuring its phase.  Phase retrieval concerns the recovery of an image from such phaseless information.  Although this problem is in general combinatorially hard, it is of great importance because it arises in many applications ranging from astronomical imaging to speech analysis. This talk discusses novel acquisition strategies and novel convex and non-convex algorithms which are provably exact, thereby allowing perfect phase recovery from a minimal number of noiseless and intensity-only measurements. More importantly, we also demonstrate that our noise-aware algorithms are stable in the sense that the reconstruction degrades gracefully as the signal-to-noise ratio decreases. This may be of special contemporary interest because phase retrieval is at the center of spectacular current research efforts collectively known under the name of coherent diffraction imaging aimed, among other things, at determining the 3D structure of large protein complexes.  

We complete the discussion of Baumgartner's axiom and related topics.

One of the main tenets in the Kolmogorov theory of 3D turbulence is the direct cascade of energy. This means that the rate of transfer of energy from one length scale to the next smallest is roughly constant over the so-called inertial range of scales. This can be indicated by a large quotient of the averages of enstrophy over energy. Similarly, the Batchelor, Kraichnan, Leith theory of 2D turbulence features an additional direct cascade, that of enstrophy, which in turn is indicated by a large quotient of averaged palinstrophy over enstrophy.  In the case of the 2D NSE we have derived bounding curves for these pairwise quantities by combining estimates for their time derivatives.  To do so for the 3D NSE, however, is to confront its outstanding global regularity problem.

Beirao da Veiga, following work of Constantin and Fefferman, showed that solutions to the 3D NSE are regular if one assumes that the direction of vortex filaments is Holder continuous with exponent 1/2. Under this assumption we construct in a single bounding curve whose maximal enstrophy is shown to scale as an exponential of the Grashof number.  This suggests that even under this smoothness assumption solutions can display extraordinary bursts in enstrophy.