## Seminars and Colloquia by Series

Monday, December 4, 2017 - 14:00 , Location: Skiles 005 , , Department of Mathematics, North Carolina State University , Organizer: Luca Dieci
In the real world, the historical performance of a stock may have impacts on its dynamics and this suggests us to consider models with delays. We consider a portfolio optimization problem of Merton’s type in which the risky asset is described by a stochastic delay model. We derive the Hamilton-Jacobi-Bellman (HJB) equation, which turns out to be a nonlinear degenerate partial differential equation of the elliptic type. Despite the challenge caused by the nonlinearity and the degeneration, we establish the existence result and the verification results.
Monday, November 27, 2017 - 14:00 , Location: Skiles 005 , , Applied and Computational Mathematics and Statistics Dept, U of Notre Dame , , Organizer: Yingjie Liu
In this talk, we will present new central and central DG schemes for solving ideal magnetohydrodynamic (MHD) equations while preserving globally divergence-free magnetic field on triangular grids. These schemes incorporate the constrained transport (CT) scheme of Evans and Hawley with central schemes and central DG methods on overlapping cells which have no need for solving Riemann problems across cell edges where there are discontinuities of the numerical solution. The  schemes are formally second-order accurate with major development on the reconstruction of globally divergence-free magnetic field on polygonal dual mesh. Moreover, the computational cost is reduced by solving the complete set of governing equations on the primal grid while only solving the magnetic induction equation on the polygonal dual mesh.
Monday, November 20, 2017 - 14:00 , Location: Skiles 005 , Yat Tin Chow , Mathematics, UCLA , , Organizer: Prasad Tetali
In this talk, we will introduce a family of stochastic processes on the Wasserstein space, together with their infinitesimal generators.  One of these processes is modeled after Brownian motion and plays a central role in our work.  Its infinitesimal generator defines a partial Laplacian on the space of Borel probability measures, taken as  a partial trace of a Hessian.  We study the eigenfunction of this partial Laplacian and develop a theory of Fourier analysis.  We also consider the heat flow generated by this partial Laplacian on the Wasserstein space, and discuss smoothing effect of this flow for a particular class of initial conditions.  Integration by parts formula, Ito formula and an analogous Feynman-Kac formula will be discussed. We note the use of the infinitesimal generators in the theory of Mean Field Games, and we expect they will play an important role in future studies of viscosity solutions of PDEs in the Wasserstein space.
Monday, November 6, 2017 - 13:55 , Location: Skiles 005 , Prof. Kevin Lin , University of Arizona , , Organizer: Molei Tao
Weighted direct samplers, sometimes also called importance samplers, are Monte Carlo algorithms for generating independent, weighted samples from a given target probability distribution. They are used in, e.g., data assimilation, state estimation for dynamical systems, and computational statistical mechanics. One challenge in designing weighted samplers is to ensure the variance of the weights, and that of the resulting estimator, are well-behaved. Recently, Chorin, Tu, Morzfeld, and coworkers have introduced a class of novel weighted samplers called implicit samplers, which possess a number of nice empirical properties. In this talk, I will summarize an asymptotic analysis of implicit samplers in the small-noise limit and describe a simple method to obtain a higher-order accuracy. I will also discuss extensions to stochastic differential equatons. This is joint work with Jonathan Goodman, Andrew Leach, and Matthias Morzfeld.
Monday, October 16, 2017 - 14:00 , Location: Skiles 005 , Dr. Barak Sober , Tel Aviv University , , Organizer: Doron Lubinsky
We approximate a function defined over a $d$-dimensional manifold $M ⊂R^n$ utilizing only noisy function values at noisy locations on the manifold. To produce the approximation we do not require any knowledge regarding the manifold other than its dimension $d$. The approximation scheme is based upon the Manifold Moving Least-Squares (MMLS) and is therefore resistant to noise in the domain $M$ as well. Furthermore, the approximant is shown to be smooth and of approximation order of $O(h^{m+1})$ for non-noisy data, where $h$ is the mesh size w.r.t $M,$ and $m$ is the degree of the local polynomial approximation. In addition, the proposed algorithm is linear in time with respect to the ambient space dimension $n$, making it useful for cases where d is much less than n. This assumption, that the high dimensional data is situated on (or near) a significantly lower dimensional manifold, is prevalent in many high dimensional problems. Thus, we put our algorithm to numerical tests against state-of-the-art algorithms for regression over manifolds and show its dominance and potential.
Monday, October 2, 2017 - 13:55 , Location: Skiles 005 , , University of Maryland, College Park , , Organizer: Wenjing Liao
We formulate super-resolution as an inverse problem in the space of measures, and introduce a discrete and a continuous model. For the discrete model, the problem is to accurately recover a sparse high dimensional vector from its noisy low frequency Fourier coefficients. We determine a sharp bound on the min-max recovery error, and this is an immediate consequence of a sharp bound on the smallest singular value of restricted Fourier matrices. For the continuous model, we study the total variation minimization method. We borrow ideas from Beurling in order to determine general conditions for the recovery of singular measures, even those that do not satisfy a minimum separation condition. This presentation includes joint work with John Benedetto and Wenjing Liao.
Monday, September 25, 2017 - 13:55 , Location: Skiles 005 , Professor Alessandro Veneziani , Emory Department of Mathematics and Computer Science , Organizer: Martin Short
When we get to the point of including the huge and relevant experience of finite element fluid modeling collected in over 25 years of experience in the treatment of cardiovascular diseases, the risk of getting “lost in translation” is real. The most important issues are the reliability that we need to guarantee to provide a trustworthy decision support to clinicians; the efficiency we need to guarantee to fit into the demand coming from a large volume of patients in Computer Aided Clinical Trials as well as short timelines required by special circumstances (emergency) in Surgical Planning. In this talk, we will report on some recent activities taken at Emory to make this transition possible. Reliability requirements call for an appropriate integration of measurements and numerical models, as well as for uncertainty quantification. In particular, image and data processing are critical to feeding mathematical models. However, there are several challenges still open, e.g. in simulating blood flow in patient-specific arteries after stent deployment; or in assessing the correct boundary data set to be prescribed in complex vascular districts. The gap between theory, in this case, is apparent and good simulation and assimilation practices in finite elements for clinical hemodynamics need to be drawn. The talk will cover these topics. For computational efficiency, we will cover some numerical techniques currently in use for coronary blood flow, like the Hierarchical Model Reduction or efficient methods for coping with turbulence in aortic flows. As Clinical Trials are currently one of the most important sources of information for medical research and practice, we envision that the suitable achievement of reliability and efficiency requirements will make Computer Aided Clinical Trials (specifically with a strong Finite-Elements-in-Fluids component) an important source of information with a significant impact on the quality of healthcare. This is a joint work with the scholars and students of the Emory Center for Mathematics and Computing in Medicine (E(CM)2), the Emory Biomech Core Lab (Don Giddens and Habib Samady), the Beta-Lab at the University of Pavia (F. Auricchio ). This work is supported by the US National Science Foundation, Projects DMS 1419060, 1412963 1620406, Fondazione Cariplo, Abbott Vascular Inc., and the XSEDE Consortium.
Monday, September 18, 2017 - 13:55 , Location: Skiles 005 , Prof. Nathan Kutz , University of Washington, Applied Mathematics , Organizer: Martin Short
The emergence of data methods for the sciences in the last decade has been enabled by the plummeting costs of sensors, computational power, and data storage. Such vast quantities of data afford us new opportunities for data-driven discovery, which has been referred to as the 4th paradigm of scientific discovery. We demonstrate that we can use emerging, large-scale time-series data from modern sensors to directly construct, in an adaptive manner, governing equations, even nonlinear dynamics, that best model the system measured using modern regression techniques. Recent innovations also allow for handling multi-scale physics phenomenon and control protocols in an adaptive and robust way. The overall architecture is equation-free in that the dynamics and control protocols are discovered directly from data acquired from sensors. The theory developed is demonstrated on a number of canonical example problems from physics, biology and engineering.
Friday, August 25, 2017 - 13:55 , Location: Skiles 005 , Prof. Song Li , Zhejiang University , Organizer: Haomin Zhou
In this talk, i shall provide some optimal PIR bounds, which confirmed a conjecture on optimal RIP bound. Furtheremore, i shall also investigate some results on signals recovery with redundant dictionaries, which are also related to statistics and sparse representation.
Monday, April 24, 2017 - 14:05 , Location: Skiles 005 , Prof. George Mohler , IUPUI Computer Science , Organizer: Martin Short
In this talk we focus on classification problems where noisy sensor measurements collected over a time window must be classified into one or more categories.  For example, mobile phone health and insurance apps take as input time series from the accelerometer, gyroscope and GPS radio of the phone and output predictions as to whether the user is still, walking, running, biking, driving etc.  Standard approaches to this problem consist of first engineering features from statistics of the data (or a transform) over a window and then training a discriminative classifier.  For two applications we show how these features can instead be learned in an end-to-end modeling framework with the advantages of increased accuracy and decreased modeling and training time.  The first application is reconstructing unobserved neural connections from Calcium fluorescence time series and we introduce a novel convolutional neural network architecture with an inverse covariance layer to solve the problem.  The second application is driving detection on mobile phones with applications to car telematics and insurance.