Special Lecture Archives for Academic Year 2017


DST Lecture Series: Can We Model Uncertainty?

When: Thu, September 29, 2016 - 4:00pm
Where: Toll Physics Bldg 1412
Speaker: C. David Levermore (UMd) - http://math.umd.edu/~lvrmr


WIM Lecture: Surviving Graduate School and Beyond

When: Thu, October 13, 2016 - 4:00pm
Where: Kirwan Hall 1310
Speaker: Dianne P. O'Leary () - http://www.cs.umd.edu/~oleary/


MathJax Test

When: Wed, December 7, 2016 - 9:00am
Where: Kirwan Hall 1311
Speaker: Test (UMD) - http://www.umd.edu
Abstract: $\sum_{i=0}^n i^2 = \frac{(n^2+n)(2n+1)}{6}$

TBA - By invitation of the hiring committee

When: Tue, December 20, 2016 - 2:00pm
Where: Kirwan Hall 1308
Speaker: Tamas Darvas (UMCP) -


Title: Recent developments on deterministic and probabilistic well-posedness for nonlinear Schrödinger and wave equations.

When: Wed, January 18, 2017 - 2:00pm
Where: Kirwan Hall 1308
Speaker: Aynur Bulut (Princeton University) -
Abstract: Dispersive equations such as nonlinear Schrödinger and wave equations
arise as mathematical models in a variety of physical settings,
including models of plasma physics, the propagation of laser beams,
water waves, and the study of many-body quantum mechanics. They also
serve as model equations for studying fundamental issues in many
aspects of nonlinear partial differential equations. Key questions in
the analysis of these equations include issues of well-posedness (for
instance, existence of solutions, uniqueness of these solutions, and
their continuous dependence on initial data in appropriate topologies)
locally in time, long-time existence and behavior of solutions, and,
conversely, the possible existence of solutions which blow-up in
finite time.

In this talk, we will give an overview of several recent results
concerning the local and global (long-time) theory, including some
results where probabilistic tools are used to obtain estimates for
randomly chosen initial data which are not available in deterministic
settings. A recurring theme (and oftentimes obstacle) is the notion
of supercriticality arising from the natural scaling of the equation —
seeking to characterize long-time behavior of solutions when the
relevant scale-invariant norms are not controlled by the conserved
energy, or for initial data of very low regularity. The techniques
involved include input from several areas of mathematics, including
ideas arising in many areas of PDE, harmonic analysis, and
probability.
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Rethinking algorithms in Data Science: Scaling up optimization using non-convexity, provably

When: Thu, February 9, 2017 - 2:00pm
Where: Kirwan Hall 3206
Speaker: Dr. Anastasios Kyrillidis (UT Austin) -
Abstract: With the quantity of generated data ever-increasing in most research areas, conventional data analytics run into solid computational, storage, and communication bottlenecks. These obstacles force practitioners to often use algorithmic heuristics, in an attempt to convert data into useful information, fast. It is necessary to rethink the algorithmic design, and devise smarter and provable methods in order to flexibly balance the trade-offs between solution
accuracy, efficiency, and data interpretability.

In this talk, I will focus on the problem of low rank matrix inference in large-scale settings. Such problems appear in fundamental applications such as structured inference, recommendation systems and multi-label classification problems. I will introduce a novel theoretical framework for analyzing the performance
of non-convex first-order methods, often used as heuristics in practice. These methods lead to computational gains over classic convex approaches, but their analysis is unknown for most problems. This talk will provide precise theoretical guarantees, answering the long-standing question why such non-convex techniques behave well in practice for a wide class of problems. I will discuss implementation details of these ideas and, if time permits, show the superior
performance we can obtain in applications found in physical sciences and machine learning.

WIM Lecture: Career Opportunities for Mathematicians at NSA

When: Fri, February 24, 2017 - 3:00pm
Where: Kirwan Hall 1310
Speaker: Susan Carter and Sara Taylor (NSA) -


Mathematics for Art Investigation

When: Thu, April 27, 2017 - 4:00pm
Where: Kirwan Hall 3206
Speaker: Ingrid Daubechies (Duke University) - https://math.duke.edu/people/ingrid-daubechies
Abstract: Mathematical tools for image analysis increasingly play a role in helping art historians and art conservators assess the state of conversation of paintings, and probe into the secrets of their history. the talk will review several case studies, Van Gogh, Gauguin, Van Eyck among others.