Seminars > Fall 2016

Fall 2016 Seminars

Click here to subscribe to the CSCAMM Seminar mailing list
  • All talks are in the CSIC Bldg (#406) Room 4122 at 2.00pm (unless otherwise stated)
  • Directions can be found at:
  • Refreshments will be served after the talk
  • Contact Email:

August 31

4122 CSIC Bldg
Prof. Leonid Koralov, Department of Mathematics, University of Maryland

Metastable distributions of Markov chains with rare transitions and related problems that result in differential equations with nonstandard boundary conditions

We consider Markov chains with parameter-dependent transition rates. The asymptotic behavior of the Markov chains is established at various time scales related to value of the parameter. This result can be viewed as a generalization of the ergodic theorem to the case of parameter-dependent Markov chains. One of the interesting applications is to the study of randomly perturbed dynamical systems (i.e., diffusion processes with a small diffusion coefficient). In this case, each asymptotically stable equilibrium of the dynamical system can be associated with a state of a Markov chain. We describe the asymptotic behavior of a diffusion process with multiple trapping regions (with the vector field equal to zero outside the regions) in terms of a PDE with nonstandard boundary conditions.  The talk is based on joint work with M. Freidlin and A. Wentzell.
September 7

September 14

4122 CSIC Bldg
Prof. Kevin Galloway, Department of Electrical & Computer Engineering, U.S. Naval Academy

Analysis and Simulation of Cyclic Pursuit

In this talk we will demonstrate that dyadic pursuit interactions employed between self-steering agents within a collective (e.g. a flock of birds or a group of mobile robots) can be used as building blocks for generating coordinated motion of the collective. Inspired by biology and motivated by potential technological applications such as team-based autonomous search-and-rescue, we will develop a mathematical framework to model the motion of self-steering particles under a variety of feedback control strategies. We focus on one particular realization based on the constant bearing (CB) pursuit strategy with the cycle graph (i.e. “cyclic pursuit”), and demonstrate how certain invariance properties result in a reduced system and interesting behaviors. This work relies on a combination of mathematical analysis tools and computational simulation to uncover the structure and wide range of behaviors exhibited by these cyclic pursuit systems. In the course of the discussion a GUI-based MATLAB program is described and demonstrated, for integrating the underlying collective dynamics (with nonholonomic constraints), and for experimenting with various combinations of pursuit laws and pursuit graph topologies.

September 21

4122 CSIC Bldg
Prof. Mitchell Luskin, Department of Mathematics, University of Minnesota

Mathematical Modeling of Incommensurate Materials

Incommensurate materials are found in crystals, liquid crystals, and quasi-crystals. Stacking a few layers of 2D materials such as graphene and molybdenum disulfide, for example, opens the possibility to tune the elastic, electronic, and optical properties of these materials. One of the main issues encountered in the mathematical modeling of layered 2D materials is that lattice mismatch and rotations between the layers destroys the periodic character of the system. This leads to complex commensurate-incommensurate transitions and pattern formation.
Even basic concepts like the Cauchy-Born strain energy density, the electronic density of states, and the Kubo-Greenwood formulas for transport properties have not been given a rigorous analysis in the incommensurate setting. New approximate approaches will be discussed and the validity and efficiency of these approximations will be examined from mathematical and numerical analysis perspectives.

September 28

4122 CSIC Bldg
Dr. Emanuel Lazar, Department of Materials Science and Engineering, University of Pennsylvania

Local Structure Analysis in Atomic Systems

Many physical systems are modeled as large sets of atom-like particles. Understanding how such particles are arranged is thus a very natural problem, though describing this ``structure'' in an insightful yet tractable manner can be tricky. We consider several conventional methods for describing local structure and their limitations, theoretical and practical. We then introduce a topological approach more naturally suited for structure analysis and highlight its versatility and robustness. In particular, the proposed method can aid in analyzing high-temperature materials without uncontrolled modification of raw data. Several short applications to materials science are considered.

October 5

4122 CSIC Bldg
Joint CSCAMM/Numerical Analysis Seminar

Prof. Tom Goldstein, Department of Computer Science, University of Maryland

Scalable methods for machine learning and sparse signal recovery

The abundance of large, distributed web-based data sets and the recent popularity of cloud computing platforms has opened many doors in machine learning and statistical modeling. However, these resources present a host of new algorithmic challenges. Practical algorithms for large-scale data analysis must scale well across many machines, have low communication requirements, and have low (nearly linear) runtime complexity to handle extremely large problems.

In this talk, we discuss alternating direction methods as a practical and general tool for solving a wide range of model-fitting problems in a distributed framework. We then focus on new "transpose reduction" strategies that allow extremely large regression problems to be solved quickly on a single node. We will study the performance of these algorithms for fitting linear classifiers and sparse regression models on tera-scale datasets using thousands of cores.

October 12

4122 CSIC Bldg
Prof. Jacob Bedrossian, Department of Mathematics, University of Maryland

Dynamics near the subcritical transition of the 3D Couette flow

Since the 1800s it has been observed that 3D stationary states of the incompressible Navier-Stokes equations display nonlinear instabilities at lower Reynolds than what can be predicted by linear theory alone. This phenomenon is now referred to as subcritical transition. We make a detailed study of this behavior near the plane, periodic Couette flow. For sufficiently regular perturbations, we determine the nonlinear stability threshold at high Reynolds number and characterize the long time dynamics of solutions near this threshold. For rougher data, we obtain an estimate of the stability threshold which agrees well with numerical experiments. The primary stabilizing mechanism is an anisotropic enhanced dissipation resulting from the mixing caused by the large mean shear; the main linear instability is a non-normal instability known as the lift-up effect. Understanding the variety of nonlinear resonances and devising the correct norms to estimate them form the core of the analysis we undertake. Joint work with Pierre Germain and Nader Masmoudi. Connections with related results on Landau damping in kinetic theory and inviscid damping in 2D fluid mechanics may also be discussed if time permits.

October 19

4122 CSIC Bldg
Prof. Alexei Novikov, Department of Mathematics, Penn State University

Imaging with intensity-only measurements

Imaging requires the solution of complicated inverse problems where we aim to determine the medium parameters from the measurements of the reflections of probing signals. In optics and X-ray imaging it is often difficult, or impossible, to measure the phases received at the detectors, only the intensities are available for imaging. I will introduce this problem mathematically, and explain some approaches that arise in attempting to image with intensities. I will then show results from extensive numerical simulations.
October 26 NO SEMINAR
**November 2

4122 CSIC Bldg.

Note Special Time

The Avron Douglis Memorial Lecture

Prof. Sylvia Serfaty, Courant Institute and Pierre and Marie Curie University

Effective models for Ginzburg-Landau vortices

Ginzburg-Landau type equations are models for superconductivity, superfluidity, Bose-Einstein condensation. A crucial feature is the presence of quantized vortices, which are topological zeroes of the complex-valued solutions. This talk will review some results on the derivation of effective models to describe the statics and dynamics of these vortices, with particular attention to the situation where the number of vortices blows up with the parameters of the problem. In particular we will present new results on the derivation of mean field limits for the dynamics of many vortices starting from the parabolic Ginzburg-Landau equation or the Gross-Pitaevskii (Schrödinger-Ginzburg-Landau) equation.

November 9

4122 CSIC Bldg
Dr. Isabel Beichl,Mathematical and Computational Science Division, National Institute of Standards and Technology (NIST)

Sequential Importance Sampling for Counting Linear Extensions

A linear extension of a partially ordered set is a linear ordering of the vertices that respects the poset ordering. Any directed acyclic graph (DAG) defines a poset where vertex v precedes vertex w in the poset if w is reachable from v via a directed path. A linear ordering for the vertices of a DAG , called a topological sort, is equivalent to a linear extension for the associated DAG. Counting the number of topological sorts of a DAG is a well-known NP hard problem, important in in scheduling, and computational linear algebra. Because the problem is hard, approximations must be used. Monte Carlo methods for approximating using MCMC are known but they are not practical for real-world computation as they have complexity O(n^6). We will describe an alternate practical method based on sequential importance sampling. Success using SIS depends on designing an importance function that "knows" the search tree. We describe a robust importance function related to Moebius inversion. One interesting property is that our approximation is exact in case the DAG is a forest.

November 16
4122 CSIC Bldg
Prof. Tingni Sun, Department of Mathematics, University of Maryland

Adaptive Estimation in Two-way Sparse Reduced-rank Regression

This talk considers the problem of estimating a large coefficient matrix in a multiple response linear regression model in the high-dimensional setting, where the numbers of predictors and response variables can be much larger than the number of observations. The coefficient matrix is assumed to be not only of low rank, but also has a small number of nonzero rows and nonzero columns. We propose a new estimation scheme and provide its nearly optimal non-asymptotic minimax rates of estimation error under a collection of squared Schatten norm losses simultaneously. Some numerical studies will also be discussed.

November 23 NO SEMINAR, Thanksgiving

November 30

4122 CSIC Bldg
Prof. Mauro Maggioni, Department of Mathematics, Johns Hopkins University

Geometric Methods for the Approximation of High-dimensional Data sets and High-dimensional Dynamical Systems

We discuss a geometry-based statistical learning framework for performing model reduction and modeling of data sets as well as of certain classes of stochastic high-dimensional dynamical systems.

We start by discussing the problem of dictionary learning for data, and introduce a new setting for the problem and a solution based on hierarchical low-rank representation of the data, together with the corresponding statistical guarantees. We then discuss how to perform other statistical learning tasks, such as regression and estimation of distributions of the data, using the learned dictionaries.

We will then discuss the approximation of certain classes of stochastic dynamical systems: we assume only have access to a (large number of expensive) simulators that can return short simulations of high-dimensional stochastic system, and introduce a novel statistical learning framework for learning automatically a family of local approximations to the system, that can be (automatically) pieced together to form a fast global reduced model for the system, called ATLAS. ATLAS is guaranteed to be accurate (in the sense of producing stochastic paths whose distribution is close to that of paths generated by the original system) not only at small time scales, but also at large time scales, under suitable assumptions on the dynamics. We discuss applications to homogenization of rough diffusions in low and high dimensions, as well as relatively simple systems with separations of time scales, and deterministic chaotic systems in high-dimensions, that are well-approximated by stochastic differential equations.

December 7

4122 CSIC Bldg
Prof. Chris Jarzynski, Institute for Physical Science and Technology, University of Maryland

Irreversibility, information and the second law of thermodynamics at the nanoscale

What do the laws of thermodynamics look like, when applied to microscopic systems such as optically trapped colloidal particles, single molecules manipulated with laser tweezers, and biomolecular machines? In recent years it has become apparent that the fluctuations of small systems far from thermal equilibrium satisfy strong and unexpected laws, which allow us to rewrite familiar inequalities of macroscopic thermodynamics as equalities. These results in turn have spurred a renewed interest in the feedback control of small systems and the closely related Maxwell’s demon paradox. I will describe some of this progress, and will argue that it has refined our understanding of irreversibility, the second law, and the thermodynamic arrow of time.
University of Maryland    

UM Home | Directories | Calendar
Maintained by CSCAMM
Direct questions and comments to

CSCAMM is part of the
College of Computer, Mathematical & Natural Sciences (CMNS)