All Seminars
| Title: Scientific Machine Learning: Learning from Small Data |
|---|
| Seminar: Numerical Analysis and Scientific Computing |
| Speaker: Dr. Lu Lu of Brown University |
| Contact: Yuanzhe Xi, yxi26@emory.edu |
| Date: 2020-04-24 at 2:00PM |
| Venue: https://emory.zoom.us/j/313230176 |
| Download Flyer |
| Abstract: Deep learning has achieved remarkable success in diverse applications; however, its use in scientific applications has emerged only recently. I have developed multi-fidelity neural networks to extract mechanical properties of solid materials (including 3D printing materials) from instrumented indentation. I have improved the physics-informed neural networks (PINNs) and developed the library DeepXDE for solving forward and inverse problems for differential equations, including partial differential equations (PDEs), fractional PDEs, and stochastic PDEs. I have also developed the deep operator network (DeepONet) based on the universal approximation theorem of operators to learn nonlinear operators (e.g., dynamical systems) accurately and efficiently from a relatively small dataset. In addition, I will present my work on the deep learning theory of optimization and generalization. |
| Title: Recent Development of Multigrid Solvers in HYPRE on Modern Heterogeneous Computing Platforms |
|---|
| Seminar: Numerical Analysis and Scientific Computing |
| Speaker: Dr. Ruipeng Li of Lawrence Livermore National Lab |
| Contact: Yuanzhe Xi, yxi26@emory.edu |
| Date: 2020-04-17 at 2:00PM |
| Venue: https://emory.zoom.us/j/313230176 |
| Download Flyer |
| Abstract: Modern many-core processors such as the graphics processing units (GPUs) are becoming an integral part of many high performance computing systems nowadays. These processors yield enormous raw processing power in the form of massive SIMD parallelism. Accelerating multigrid methods on GPUs has drawn a lot of research attention in recent years. For instance, in recent releases of the HYPRE package, the structured multigrid solvers (SMG, PFMG) have full GPU-support for both the setup and the solve phases, whereas the algebraic multigrid (AMG) solver, namely BoomerAMG, has only its solve phase been ported and the setup can still be computed on CPUs only. In this talk, we will provide an overview of the available GPU-acceleration in HYPRE and present our current work on the algorithms in the AMG setup that are suitable for GPUs including the parallel coarsening algorithms, the interpolation methods and the triple-matrix multiplications. The recent results as well as the future work will also be included. |
| Title: A discussion on the Log-Brunn-Minkowski Conjecture and Related Questions |
|---|
| Seminar: Analysis and Differential Geometry |
| Speaker: Professor Galyna Livshytz of Georgia Institute of Technology |
| Contact: Vladimir Oliker, oliker@emory.edu |
| Date: 2020-04-07 at 4:00PM |
| Venue: https://emory.zoom.us/j/352530072 |
| Download Flyer |
| Abstract: We shall discuss the Log-Brunn-Minkowski conjecture, a conjectured strengthening of the Brunn-Minkowski inequality proposed by Boroczky, Lutwak, Yang and Zhang. The discussion will involve introduction and explanation of how the local version of the conjecture arises naturally, a collection of ‘’hands on’’ examples and elementary geometric tricks leading to various related partial results, statements of related questions as well as a discussion of more technically involved approaches and results. Based on work with Johannes Hosle and Alexander Kolesnikov, as well as on previous joint results with Colesanti, Marsiglietti, Nayar, Zvavitch. |
| Title: Applied differential geometry and harmonic analysis in deep learning regularization |
|---|
| Seminar: Numerical Analysis and Scientific Computing |
| Speaker: Dr. Wei Zhu of Duke University |
| Contact: Yuanzhe Xi, yxi26@emory.edu |
| Date: 2020-04-03 at 2:00PM |
| Venue: https://emory.zoom.us/j/313230176 |
| Download Flyer |
| Abstract: With the explosive production of digital data and information, data-driven methods, deep neural networks (DNNs) in particular, have revolutionized machine learning and scientific computing by gradually outperforming traditional hand-craft model-based algorithms. While DNNs have proved very successful when large training sets are available, they typically have two shortcomings: First, when the training data are scarce, DNNs tend to suffer from overfitting. Second, the generalization ability of overparameterized DNNs still remains a mystery despite many recent efforts. In this talk, I will discuss two recent works to “inject” the “modeling” flavor back into deep learning to improve the generalization performance and interpretability of DNNs. This is accomplished by deep learning regularization through applied differential geometry and harmonic analysis. In the first part of the talk, I will explain how to improve the regularity of the DNN representation by imposing a “smoothness” inductive bias over the DNN model. This is achieved by solving a variational problem with a low-dimensionality constraint on the data-feature concatenation manifold. In the second part, I will discuss how to impose scale-equivariance in network representation by conducting joint convolutions across the space and the scaling group. The stability of the equivariant representation to nuisance input deformation is also proved under mild assumptions on the Fourier-Bessel norm of filter expansion coefficients |
| Title: A GAN-based Approach for High-Dimensional Stochastic Mean Field Games |
|---|
| Seminar: Numerical Analysis and Scientific Computing |
| Speaker: Samy Wu Fung of UCLA |
| Contact: Lars Ruthotto, lruthotto@emory.edu |
| Date: 2020-03-27 at 2:00PM |
| Venue: https://emory.zoom.us/j/313230176 |
| Download Flyer |
| Abstract: We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean field games (MFGs). Our algorithm is geared toward high-dimensional instances of MFGs that are beyond reach with existing methods. We achieve this in two steps. First, we take advantage of the underlying variational primal-dual structure that MFGs exhibit and phrase it as a convex-concave saddle point problem. Second, we parameterize the value and density functions by two neural networks, respectively. By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial generative network (GAN). We show the potential of our method on up to 50-dimensional MFG problems. |
| Title: Increasing paths in edge-ordered hypergraphs |
|---|
| Defense: Dissertation |
| Speaker: Bradley Elliott of Emory University |
| Contact: Bradley Elliott, bradley.elliott@emory.edu |
| Date: 2020-03-27 at 3:30PM |
| Venue: https://emory.zoom.us/j/345080312 |
| Download Flyer |
| Abstract: Abstract: In this defense, we will see many variations on a classic problem of ordering the vertices or edges of a graph and determining the length of "increasing" paths in the graph. For finite graphs, the vertex-ordering problem is completely solved, and there has been recent progress on the edge-ordering problem. Here, we also provide upper bounds for the edge-ordering problem with respect to complete hypergraphs and Steiner triple systems. We also prove the hypergraph version of the vertex-ordering problem: every vertex-ordered hypergraph H has an increasing path of at least $chi(H)-1$ edges, where $chi(H)$ is the chromatic number of $H$.\\ \\ For countably infinite graphs, a similar problem is studied, asking which graphs contain an infinite increasing path regardless of how their vertices or edges are ordered. Here we answer such questions for hypergraphs. For example, we show that the condition that a hypergraph contains a subhypergraph with all infinite degrees is equivalent to the condition that any vertex-ordering permits an infinite increasing path. We prove a similar result for edge-orderings. In addition, we find an equivalent condition for a graph to have the property that any vertex-ordering permits a path of arbitrary finite length. Finally, we study related problems for orderings by all integers $Z$ (instead of just positive integers $N$). For example, we show that for every countable graph, there is an ordering of its edges by $Z$ that forbids infinite increasing paths. |
| Title: Local-global principles for norm one tori over semi-global fields. |
|---|
| Defense: Dissertation |
| Speaker: Sumit Chandra Mishra of Emory University |
| Contact: David Zureick-Brown, DAVID.M.BROWN.JR@GMAIL.COM |
| Date: 2020-03-24 at 4:00PM |
| Venue: https://emory.zoom.us/j/382949597 |
| Download Flyer |
| Abstract: Let K be a complete discretely valued field with residue field k (e.g. k((t)) ). Let n be an integer coprime to char(k). Let F = K(x) be the rational function field in one variable over F and L/F be any Galois extension of degree n. Suppose that either k is algebraically closed or k is finite field containing a primitive nth root of unity. Then we show that an element in F? is a norm from the extension L/F if and only if it is a norm from the corresponding extensions over the completions of F at all discrete valuations of F. We also prove that such a local-global principle holds for product of norms from cyclic extensions of prime degree if k is algebraically closed. |
| Title: Deep Learning with Graph Structured Data: Methods, Theory, and Applications |
|---|
| Seminar: Numerical Analysis and Scientific Computing |
| Speaker: Jie Chen of MIT-IBM Watson AI Lab, IBM Research |
| Contact: Yuanzhe Xi, yxi26@emory.edu |
| Date: 2020-03-06 at 2:00PM |
| Venue: MSC W201 |
| Download Flyer |
| Abstract: Graphs are universal representations of pairwise relationship. With the rise of deep learning that demonstrates promising parameterizations of functions on Euclidean and regularly structured data (e.g., images and sequences), natural interests seek extensions of neural networks for irregularly structured data, including notably, graphs. This talk aims at painting a global picture of the emerging research on graph deep learning and inspiring novel directions. The speaker will share his recent research on modeling, computation, and applications of graph neural networks. Whereas modeling network architectures under different learning settings draws major interests in the field, understanding the capacity and limits of these networks attracts increasing attention. Moreover, efficient training and inference with large graphs or large collections of graphs need to address challenges beyond those of usual neural networks with regularly structured data. Of separate interest is the learning of a hidden graph structure if objects or variables interact, the subject of which interfaces with causality in machine learning. Last but not least, graphs admit numerous interesting applications, among which the speaker touches drug design, cryptocurrency forensics, cybersecurity, and power systems. |
| Title: Harry Potter's Cloak via Transformation Optics |
|---|
| Colloquium: Combinatorics |
| Speaker: Gunther Ulhmann of University of Washington |
| Contact: David Borthwick, dborthw@emory.edu |
| Date: 2020-03-05 at 5:00PM |
| Venue: Oxford Road Building Presentation Room (311) |
| Download Flyer |
| Abstract: Can we make objects invisible? This has been a subject of human fascination for millennia in Greek mythology, movies, science fiction, etc., including the legend of Perseus versus Medusa and the more recent Star Trek and Harry Potter. In the last two decades or so there have been several scientific proposals to achieve invisibility. We will introduce in a non-technical fashion one of them, the so-called "transformation optics," that has received the most attention in the scientific literature. |
| Title: Connections between mock modular forms and vertex operator algebras |
|---|
| Defense: Number Theory |
| Speaker: Lea Beneish of Emory University |
| Contact: David Zureick-Brown, DAVID.M.BROWN.JR@GMAIL.COM |
| Date: 2020-03-03 at 3:00PM |
| Venue: MSC W303 |
| Download Flyer |
| Abstract: The results in this dissertation come in two flavors, first we aim to strengthen the analogy between monstrous and umbral moonshine using vertex operator algebras, and second we derive structural results on vertex operator algebras using mock modular forms. \\ Towards strengthening the analogy between umbral and monstrous moonshine, we reframe Mathieu moonshine by repackaging the Mathieu moonshine mock modular forms in a few different ways, verifying the existence of corresponding modules, and giving various applications including connections with arithmetic. We produce vertex operator algebra constructions of some of these modules. \\ Using results from orbifold theory and from the theory of mock modular forms, we derive new structural results on vertex operator algebras. In joint work with Victor Manuel Aricheta, we study the asymptotic structure sequences of $G$-modules where $G$ are finite automorphism groups of certain vertex operator algebras (in particular this holds for umbral moonshine modules). And in joint work with Michael Mertens, we use Weierstrass mock modular forms to relate a dimension formula for certain vertex operator algebras to the arithmetic of modular curves. |