The problem of spectral estimation, namely – recovering the frequency contents of a signal – arises in various fields of science and engineering, including speech recognition, array imaging and remote sensing. In this talk, I will introduce the MUltiple SIgnal Classification (MUSIC) algorithm for line spectral estimation and provide a stability analysis of the MUSIC algorithm. Numerical comparison of MUSIC with other algorithms, such as greedy algorithms and L1 minimization, shows that MUSIC combines the advantages of strong stability and low computational complexity for the detection of well-separated frequencies on a continuum. Moreover, MUSIC truly shines when the separation of frequencies drops to one Rayleigh length and below while all other methods fail. This is a joint work with Albert Fannjiang at UC Davis. The talk involves basic linear algebra and Fourier analysis and it will be accessible to all.

Data lying in a high-dimensional ambient space are commonly thought to have a much lower intrinsic dimension. In particular, the data may be concentrated near a lower-dimensional subspace or manifold. There is an immense literature focused on approximating the unknown subspace, and in exploiting such approximations in clustering, data compression, and building of predictive models. Most of the literature relies on approximating subspaces using a locally linear, and potentially multiscale, dictionary. In this talk, we propose a simple and general alternative, which instead uses pieces of spheres, or spherelets, to locally approximate the unknown subspace. Theory is developed showing that spherelets can produce dramatically lower covering numbers and MSEs for many manifolds. We develop spherical principal components analysis (SPCA) and spherical multiscale methods. Results relative to state-of-the-art competitors show dramatic gains in ability to accurately approximate the subspace with orders of magnitude fewer components. This leads to substantial gains in data compressibility, few clusters and hence better interpretability, and much lower MSE based on small to moderate sample sizes. A Bayesian nonparametric model based on spherelets will be introduced as an application.

In many problems in fluids and electromagnetics, we may formulate solutions to the Dirichlet and Neumann problems in terms of double and single layer potentials. Such boundary integral representations can result in computational difficulties at points on and near the boundary due to singularities and near singularities. The case of a smooth closed boundary has been well-studied, but I will focus on computational issues that arise from a boundary that is only piecewise smooth, consisting of connected curve segments. I will give an overview of my research in approximating singular and nearly singular integrals, as well as discuss an approach for the computation of double layer potentials at points on and near a curve segment.

I will motivate the need to analyze data sets in high dimensions, their geometrical properties and the properties of functions on them with several examples. I will focus on techniques based on random walks on data sets, and introduce a new nonlinear system of coordinates based on heat kernels, similar in spirit to the GPS system, for parametrizing data sets. If time allows, I will also discuss simple but surprisingly successful applications of the heat kernel to fit functions on data, that performs at the state-of-art or better as a classifier on a variety of benchmark data sets.

Knots, while combinatorial in flavor, play a key role in the topology of manifolds in three and four dimensions. I'll discuss this role and describe some classical problems about knots that were surprisingly solved only recently through high-powered techniques. Gauge theory, symplectic geometry, and the Poincare conjecture may make cameo appearances.

I will begin by recounting how I got involved in an industrial collaboration with a company that makes glass tempering furnaces and how younger grad students can seek such opportunities. I will then describe the mathematical model I developed for the company while highlighting challenges that arose due to differences in culture and priorities between academia and industry.

A polytope is a bounded subset of R^d which is the intersection of finitely many half-spaces. Given a polytope P, we can consider integer dilations of P, and ask how many integer points are contained in each dilation, as a function of the dilation factor. Under the right conditions, this counting function is a polynomial with some very interesting and unexpected properties. To demonstrate the usefulness of these results, we will give alternative proofs to some well known results from far outside the realm of geometry.

The nephron is the functional unit of the kidney. The flow rate in each nephron is regulated, in part, by tubuloglomerular feedback, a negative feedback loop. In some parameter regimes, this feedback system can exhibit oscillations that approximate limit-cycle oscillations. However, nephron flow in spontaneously hypertensive rats (SHR) can exhibit highly irregular oscillations similar to deterministic chaos. We used a mathematical model of tubuloglomerular feedback (TGF) to investigate potential sources of the irregular oscillations and the associated complex power spectra in SHR. A bifurcation analysis of the TGF model equation was performed by finding roots of the characteristic equation, and numerical simulations of model solutions were conducted to assist in the interpretation of the analysis. Four potential sources of spectral complexity in SHR were identified: (1) bifurcations that produce qualitative changes in solution type, leading to multiple spectrum peaks and their respective harmonic peaks; (2) continuous lability in delay parameters, leading to broadening of peaks and their harmonics; (3) episodic lability in delay parameters, leading to multiple peaks and their harmonics; and (4) coupling of small numbers of nephrons, leading to broadening of peaks, multiple peaks, and their harmonics. We conclude that the complex power spectra in SHR may be explained by the inherent complexity of TGF dynamics, which may include solution bifurcations, variation in TGF parameters, and coupling between small numbers of neighboring nephrons.

In a recent paper of Pierce, Turnage-Butterbaugh, and Wood, the authors proved an effective Chebotarev density theorem for families of number fields. Notably D_4-quartic fields have not been treated in their paper. In this talk, I will explain the importance of the case for D_4-quartic fields and will present my proof of a Chebotarev density theorem for certain families of D_4-quartic fields. The key tools are a lower bound for the number of fields in the families and a zero-free region for almost all fields in the families.

Classically, a modular form for a reductive group G is an automorphic form that gives rise to a holomorphic function on the symmetric space G/K, when this symmetric space has complex structure. However, there are very interesting groups G, such as those of type G_2 and E_8, for which G/K does not have complex structure. Nevertheless, there is a theory of modular forms on these exceptional groups, whose study was initiated by Gross-Wallach and Gan-Gross-Savin. I will define these objects and describe what is known about them.

We describe a geometric theory of dark matter called "wave dark matter" whose underlying equations are the Einstein-Klein-Gordon system of PDEs. In spherical symmetry this system has simple static state solutions which we use to model dark galactic halos. We outline some scaling properties of these states including two new boundary conditions which might account for the existence of an astrophysical scaling relation called the baryonic Tully-Fisher relation.

An old question about surfaces in 3-space is: When can a surface be written as a sum of two curves? For example, the elliptic paraboloid z = x^2 + y^2 can be thought of as the sum of the two space curves (x,0,x^2) and (0,y,y^2). However, a little thought shows that most surfaces in space should not be expressible parametrically as X(s) + Y(t) where X and Y are space curves. Surfaces for which this can be done are called `surfaces of translation'. This raises the question of determining whether or not this is possible for a given surface and in how many ways. This simple question leads to some surprisingly deep mathematics, involving complex analysis and overdetermined systems of PDE, and to other questions that are still open today. I will explain some of these developments (and what they have to do with my own work). There will even be a few pictures.

This talk will be concerned with two problems. The first is the zeros of the derivatives of. Kac random polynomials K_n, which is a random polynomial with i.i.d. coefficients. It has been shown that the empirical measure of zeros of K_n will converge to the uniform measure on the unit circle of complex plane. Same convergence holds true for nay fixed order of derivative of K_n. In a joint work with Renjie Feng, we show if we consider the N_n-th order of derivative of K_n, then asymptotic behavior of empirical measure of this derivative will depend on the limit of \frac{N_n}{n}. In particular, as long as this ratio is greater than 0, the phenomenon of ‘zeros clustering around unit circle’ breaks down. The second talk is about Average Nearest Neighbor Degree (ANND), which is a measure for the degree-degree correlation for complex network. We shall be concerned with the probabilistic properties of ANND in the configuration model. We prove if the variable X generating the network has order of moment larger than 2, then the ANND(k) will converge uniformly to μ2/μ1, where μ2 is the second moment of X, and μ1 is the first moment. For the case that X has infinite variance, we show the pointwise (i.e., for fixed k) scaled convergence of ANND(k) to a stable random variable. This is joint work with Nelly Litvak and Pim van der Hoorn. More recently, Clara Stegehuis showed that when X is sample from the Pareto distribution, then one can obtain a complete spectrum of ANND(k) for the erased configuration model.

After brief introductions to special relativity and the foundations of differential geometry, we will discuss the big ideas behind Einstein's theory of general relativity. Einstein's theory replaces Newtonian physics not only as the best description of gravity according to experiments, but also as a philosophically pleasing and very geometric idea, which Einstein called his "happiest thought." We will also discuss the predictions made by general relativity, including the big bang and black holes, both of which are strongly supported by observations. We will discuss these ideas from a geometric perspective, and discuss some of the open problems and future directions that are currently being studied.

Math Slam

Suppose you need to compute an integral over a general surface numerically. How would you do it? You could triangulate the surface, or you might use coordinate charts. Either way is a lot of work, maybe more than you want to do if you have a large number of surfaces. I will describe a fairly simple method, appropriate for smooth, closed surfaces, developed by a former grad student here, Jason Wilson, in his Ph.D. thesis, including proofs that his algorithm works. I will then discuss the extension to integrals for potentials defined by densities on surfaces, such as harmonic functions. In that case the integrand has a singularity; special treatment is needed, and some interesting math comes in. Another of our former Ph.D.'s, Wenjun Ying, has contributed to that work (among many projects of his). Such integrals occur in several scientific contexts; I will especially mention Stokes flow (fluid flow dominated by viscosity), appropriate for modeling some aspects of biology on small scales. For more information, see J. t. Beale, W. Ying, and J. R. Wilson, A simple method for computing singular or nearly singular integrals on closed surfaces at my web site or at http://arxiv.org/abs/1508.00265

The Conley Index is a topological tool in dynamical systems which makes heavy use of basic homological techniques, particularly exact sequence. I will present a short primer on relative homology, describe the homological version of the Conley Index, and then present a diagram chase that contains information about the unstable manifolds of invariant sets in a dynamical system. This talk is aimed at students in the Algebraic Topology I course as well as anyone interested in dynamical systems.

Many machine learning algorithms today rely on finding the nearest neighbors of all points in a data set. In data sets which are too large and too complex, we cannot ask for finding the true nearest neighbors without paying the price of a full pairwise search. Therefore, we look to using approximate nearest neighbor searches to quickly construct rough approximations of the nearest neighbor graph. I will talk about many of the methods used today in practice.

(This abstract is in TeX source code. Sorry!) Fix a small positive number $h$. Let $$G=h\mathbb{Z}^2=\{(ih,jh):i,j\in\mathbb{Z}\};$$ thus $G$ is a rectangular grid of points in $\mathbb{R}^2$. Let $\Omega$ be an bounded open subset of $\mathbb{R}^2$ with $C^1$ boundary and let $E=\{x\in G:x\in\Omega\}$. {\bf Question One.} Given $E$ can one determine the length of $\partial\Omega$ to within $O(h)$? The answer to this question is ``yes'', provided $\Omega$ satisfies a certain natural ``thickness'' condition; without this additional assumption the answer may be ``no''. {\bf Question Two.} Is there a fast algorithm for determining the length of $\partial\Omega$. The answer to this question also ``yes''. In this talk I will describe the proof that the answer to Question One is ``yes'' and I will describe the fast algorithm whose existence is implied in the answer to Question Two. If time permits, I will describe some applications.

Given a contact structure (a plane field) on R^3, one can define a Legendrian knot to be an embedding of the circle such that the embedding is everywhere tangent to the plane field. Surgery along such a knot gives a way to construct new manifolds and so there is interest in classifying Legendrian knots. This turns out to be a finer classification than that of topological knots -- there are many different Legendrian unknots. Given a Legendrian knot, one can associate the Chekanov-Eliashberg differential graded algebra (DGA) generated by the crossings and then find augmentations of this DGA much like those in your standard algebraic topology course. This talk will give an overview of the relationships Joshua Sabloff and Dmitry Fuchs gave between such rulings and augmentations and how it relates to my current work.

TBD

Khovanov homology is a special case of a process known as categorification. The idea of categorification is to lift a known polynomial invariant of links to a homology theory whose isomorphism type is an invariant of links and whose ¡°Euler characteristic¡± is the original polynomial. In the case of Khovanov homology, this Euler characteristic is the famous Jones polynomial. After reviewing some basic knot theory and the construction of the Jones polynomial, we discuss the construction of Khovanov homology. Finally, we will discuss some topological applications of Khovanov homology.

More than 95% of the present day curvature of the universe is not a result of regular baryonic matter represented by the periodic table of elements. About 73% is well described by a geometrically natural cosmological constant, also referred to as dark energy, which results in a very small amount of curvature uniformly spread throughout the universe. We will explore the possibility that the remaining 23%, commonly referred to as dark mater, could also be explained very naturally from a geometric point of view.

This talk will discuss the basics of time-reversal theory in the context of wave propagation in random media. It will outline several of the interesting applications in the field including detection and encryption.

At first glance, knot theory and representation theory seem to be unrelated fields of mathematics. In fact, this is not the case: in the early 90's, Reshetikhin and Turaev proved that knot invariants (and 3-manifold invariants) can be derived via the representation theory of quantum groups. The key link (no pun intended) between these areas is the observation that both the category of tangles and the category of representations share many similar structural features. In this talk we will explore these ideas, and if time permits, their categorified counterparts. If things like categories scare you, fear not; as the title suggests, all categories (and constructions on them) we encounter will have pictorial descriptions. In fact, no knowledge of category theory or representation theory is assumed. At the same time, if you have indeed taken Math 253, then this talk will provide context for the material in that course.