Spring 2019 Seminars

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Jan. 30, 2:00 pm
Dr. Jan Peszek, CSCAMM, University of Maryland
Multi-D macroscopic description of flocking
I will present certain developments related to the macroscopic limit of the Cucker-Smale flocking model. The main focus will be on the multi-D euclidean space results from the recent joint work with Raphael Danchin, Piotr Mucha and Bartosz Wróblewski.
Feb. 6, 2:00 pm
Dr. Dong Dong, CSCAMM, University of Maryland
Multilinear operators: classic, curved, and discrete
Several important integral operators in harmonic analysis will be presented in an organized way. The fundamental question about these operators is the boundedness on Lebesgue spaces. We will show that the difficulty to establish the boundedness will change dramatically once we move from the classic type of operators to their curved and discrete analogs. It is very interesting to see that harmonic analysis can interact with many other fields of mathematics such as PDE, ergodic theory, number theory, combinatorics, and even algebraic geometry.
Feb. 13, 2:00 pm
Prof. Fei Lu, Department of Mathematics, Johns Hopkins University
Nonparametric inference of interaction laws in particles/agent systems
Self-interacting systems of particles/agents arise in many areas of science, such as particle systems in physics, flocking and swarming models in biology, and opinion dynamics in social science. A natural question is to learn the laws of interaction between the particles/agents from data consisting of trajectories. In the case of distance-based interaction laws, we present efficient regression algorithms to estimate the interaction kernels, and we develop a nonparametric statistical learning theory addressing identifiability, consistency and optimal rate of convergence of the estimators. Especially, we show that despite the high-dimensionality of the systems, optimal learning rates can still be achieved. (Joint work with Mauro Maggioni, Sui Tang and Ming Zhong).
Feb. 20, 2:00 pm
Dr. Steven Damelin, Department of Mathematics, The University of Michigan
Shape Space, Recognition, Minimal Distortion, Vision Groups and Applications
Visual objects are often known up to some ambiguity, depending on the methods used to acquire them. The first-order approximation to any transformation is, by definition, affine, and the affine approximation to changes between images has been used often in computer vision. Thus it is beneficial to deal with objects known only up to an affine transformation. For example, feature points on a planar transform projectively between different views, and the projective transformation can in many cases be approximated by an affine transformation. More generally, given two visual objects in a containing Euclidean space R^k, one may study vision group actions between these two objects often with an underlying signature which are equivalent under some symmetry or minimal distortion action with respect to a suitable metric inherited by this action. For example, Euclidean groups, similarity, Equi-Affine, projections, camera rotations and video groups. The study of the space of ordered configurations of n distinct points in R^k up to similarity transformations was pioneered by Kendall who coined the name shape space. For different groups of transformations (rigid, similarity, linear, affine, projective for example) one obtains different shape spaces. Moreover, while these formulations allow often global optimal optimization, e.g. using convex objectives , many of the problems above require efficient approximation methods which work locally. This framework has applications to biological structural molecule reconstruction problems, to recognition tasks and to matching features across images with minimal distortion” This talk will discuss various work with collaborators around this circle of ideas.
Feb. 27, 2:00 pm
Speaker TBA,
Title TBA
Mar. 5-6
Mar. 6, 2:00 pm
Dr. Bin Cheng, Department of Mathematics, University of Surrey
Analysis of nonlinear dynamics with three time scales
A PDE/ODE system can evolve in 3 time scales when the fast scales are associated with 2 small parameters that tend to zero at different rates. We investigate the limiting dynamics when the fast dynamics is generated by 2 skew-seft-adjoint operators and the initial time derivative is uniformly bounded regardless of the small parameters. To find the subspace that the limiting dynamics resides, we rely on matrix perturbation theory.
Mar. 13, 2:00 pm
Prof. John Bush, Department of Mathematics, MIT
Title TBA
Mar. 20
No seminar. UMD Spring Break.
Mar. 27, 2:00 pm
Prof. Alex Hening, Department of Mathematics, Tufts University
Title TBA
Apr. 3, 2:00 pm
Speaker TBA,
Title TBA
Apr. 10, 2:00 pm
Prof. Daniel Han-Kwan, Center of Mathematics Laurent-Schwartz, École Polytechnique
Title TBA
Apr. 17, 2:00 pm
Dr. Didier Bresch, LAMA UMR 5127 CNRS, Université Savoie Mont-Blanc
Title TBA
Apr. 24, 2:00 pm
Prof. Anita Layton, Department of Applied Mathematics, University of Waterloo
Title TBA
May. 1, 2:00 pm
Dr. Mark Cerenzia, Operations Research, Princeton University
Title TBA
May. 8, 2:00 pm
Prof. Yao Yao, School of Mathematics, Georgia Tech
Title TBA

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