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		<channel><title>Colloquium</title><link>http://www-math.umd.edu/research/seminars.html</link><description></description><item>
	<title>Predictive Science and Deep Learning - A Bright Future or an Odd Couple?</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Wed, 20 Sep 2023 15:15:00 EDT</pubDate>
	<description><![CDATA[When: Wed, September 20, 2023 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Wolfgang Dahmen (Aachen, University of South Carolina) - https://sc.edu/study/colleges_schools/artsandsciences/mathematics/our_people/directory/dahmen_wolfgang.php<br />
Abstract: Modern machine learning methodologies appear to exert a<br />
transformative impact on society and science, especially in “Big Data”<br />
application scenarios. While these applications are typically error-tolerant<br />
this talk focuses on a rigorous accuracy quantification when trying to to use<br />
machine learning tools to recover “physical states of interest” from several<br />
sources of incomplete information, e.g. in terms of observational data and<br />
governing possibly deficient physical laws. The latter “background model” is<br />
typically given as a parameter dependent family of PDEs. Efficient “forward<br />
exploration” of corresponding solution manifolds as well as related inverse<br />
tasks, like state or parameter estimation, hinge on “learning” efficient and<br />
certifiable surrogates for the parameter-to-solution map. We highlight<br />
intrinsic challenges and discuss conceptual pathways exploiting the role of<br />
stable variational formulations of the governing PDEs as well as tailored<br />
optimization strategies for training on variationally correct residual loss<br />
functions.<br />]]></description>
</item>

<item>
	<title>The optimal paper Moebius band</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Fri, 29 Sep 2023 15:15:00 EDT</pubDate>
	<description><![CDATA[When: Fri, September 29, 2023 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Richard  Schwartz (Brown University) - https://www.math.brown.edu/reschwar/<br />
<br />
<br />
Title: The optimal paper Moebius band<br />
<br />
Abstract: Ever since the last ice age, when children<br />
wandered out of their frozen caves and made Moebius<br />
bands from strips of paper, humans have wondered<br />
how short a strip of paper they could use to make such<br />
Moebius bands. In this talk I will give a hands-on and<br />
elementary account of my recent solution of the<br />
optimal paper Moebius band conjecture of B. Halpern and<br />
C. Weaver from 1977.   My result is that a unit width strip<br />
of paper needs to be more than sqrt(3) units long in order<br />
for you to be able to twist it up into a paper Moebius band,<br />
and the bound is sharp.<br />]]></description>
</item>

<item>
	<title>Riehl (TBA)</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Fri, 06 Oct 2023 15:15:00 EDT</pubDate>
	<description><![CDATA[When: Fri, October 6, 2023 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Emily Riehl (Johns Hopkins University) - https://math.jhu.edu/~eriehl/<br />
Abstract: TBA<br />]]></description>
</item>

<item>
	<title>Categorification and geometry</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Fri, 13 Oct 2023 15:15:00 EDT</pubDate>
	<description><![CDATA[When: Fri, October 13, 2023 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Lars Hesselholt (Nagoya University) - https://www.math.nagoya-u.ac.jp/~larsh/<br />
Abstract: The key principle in Grothendieck&#039;s algebraic geometry is that every commutative ring be considered as the ring of functions on some geometric object. Clausen and Scholze have introduced a categorification of algebraic and analytic geometry, where the key principle is that every stable dualizably symmetric monoidal infinity-category be considered as the infinity-category of quasi-coherent modules on some geometric object. In this talk, I will explain this shift in paradigm as well as Clausen&#039;s philosophy that *every* cohomology theory should arise from this picture, complete with a six-functor formalism of categories of coefficients. The Hahn-Raksit-Wilson even filtration and Efimov continuity are key players in this picture.<br />]]></description>
</item>

<item>
	<title>Mathematics Around the Heisenberg Group</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Thu, 26 Oct 2023 15:45:00 EDT</pubDate>
	<description><![CDATA[When: Thu, October 26, 2023 - 3:45pm<br />Where: Kirwan Hall 3206<br />Speaker: Roger Howe (Yale University) - https://www.norbertwiener.umd.edu/fft/2023/Speakers/Roger_Howe.html<br />
Abstract: The Heisenberg group is the group-theoretic embodiment of the Canonical Commutation Relations (CCR) of quantum mechanics, formulated by Werner Heisenberg just under a century ago. In the intervening years, the CCR and the Heisenberg group have come to be seen as a central nexus in mathematics, connecting and unifying many seemingly disparate phenomena, in harmonic analysis, partial differential equations, operator theory, mathematical physics, Lie theory, representation theory, classical invariant theory, number theory, and more. This talk will survey topics where the Heisenberg group plays an important role, with an emphasis on the connections. This talk is part of the FFT 2023 conference, and it is given by the Norbert Wiener Center Distinguished Lecturer.<br />]]></description>
</item>

<item>
	<title>Decoding Time&#039;s Mysteries for Better Predictions</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Thu, 26 Oct 2023 18:45:00 EDT</pubDate>
	<description><![CDATA[When: Thu, October 26, 2023 - 6:45pm<br />Where: Kirwan Hall 3206<br />Speaker: James Howard (Johns Hopkins University) - https://www.norbertwiener.umd.edu/fft/2023/Speakers/James_Howard.html<br />
Abstract: In our data-driven world, making sense of complex information is paramount. Data comes in various shapes and forms, from healthcare to finance, but perhaps none as intricate as time-series data. How can we unravel the underlying stories that this sort of data tells us? In this talk, we will journey through the cross-disciplinary avenues of harmonic analysis, survival models, and machine learning to answer this question. By taking a closer look at signature methods and rough paths, we will explore how mathematics not only dissects the intricacies of time-series data but also enhances our understanding of stochastic processes. These advancements have unprecedented applications in predicting vital healthcare outcomes and beyond. Drawing upon the latest research, including my own, I will illustrate how this mathematical framework provides a robust and efficient way to revolutionize predictive models. The aim is to bring together insights from various fields to show how an enriched mathematical understanding can lead to practical, real-world applications that can potentially save lives. This Keynote talk is part of the FFT 2023 conference.<br />]]></description>
</item>

<item>
	<title>A tale of two invariants</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Wed, 15 Nov 2023 15:15:00 EST</pubDate>
	<description><![CDATA[When: Wed, November 15, 2023 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Paul Feehan (Rutgers) - https://sites.math.rutgers.edu/~feehan/<br />
Abstract: <br />
<br />
In 1994, Edward Witten published his celebrated formula that expressed the countably many Donaldson invariants of a smooth 4-manifold in terms of its finitely many integer Seiberg-Witten invariants. Witten used ideas from theoretical physics to predict his formula, subsequently generalized by him and Gregory Moore (1997). Later in 1994, Victor Pidstrigatch and Andrei Tyurin proposed a mathematical program to give a rigorous proof of Witten’s formula, entirely within the realm of classical Yang-Mills gauge theory. This program was developed by Thomas Leness and the speaker, leading to a complete proof of Witten’s formula based on moduli spaces of non-Abelian monopoles to connect moduli spaces of anti-self-dual Yang-Mills connections, which define Donaldson invariants, and moduli spaces of Seiberg-Witten monopoles, which define Seiberg-Witten invariants.<br />
<br />
In this talk, we will review this paradigm and also explain how an analogous idea may provide a similar geometric explanation of the mysterious Gopakumar-Vafa formula for Gromov-Witten invariants of symplectic 6-manifolds and the integrality and finiteness of the BPS states, results that were recently proved by Ionel &amp;amp; Parker (2018) and by Doan, Ionel, &amp;amp; Walpuski (2021).<br />]]></description>
</item>

<item>
	<title>Using logic to study homeomorphism groups</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Wed, 29 Nov 2023 15:15:00 EST</pubDate>
	<description><![CDATA[When: Wed, November 29, 2023 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Thomas Koberda (University of Virginia) - https://sites.google.com/view/koberdat<br />
Abstract:  I will describe some recent results on the first order rigidity of homeomorphism groups of compact manifolds, and their applications to dynamics of group actions on manifolds. I will also describe how to find &quot;syntactic&quot; invariants of manifolds, and how these can be used to give a conjectural model-theoretic characterization of the genus of a surface.<br />]]></description>
</item>

<item>
	<title>Generative Models for Implicit Distribution Estimation: a Statistical Perspective</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Thu, 25 Jan 2024 15:30:00 EST</pubDate>
	<description><![CDATA[When: Thu, January 25, 2024 - 3:30pm<br />Where: Kirwan Hall 3206<br />Speaker: Yun Yang  (University of Illinois Urbana-Champaign) - https://sites.google.com/site/yunyangstat/<br />
Abstract: The estimation of distributions of complex objects from high-dimensional data with low-dimensional structures is an important topic in statistics and machine learning. Modern generative modeling techniques accomplish this by encoding and decoding data to generate new, realistic synthetic data objects, including images and texts. A key aspect of these models is the extraction of low-dimensional latent features, assuming the data lies on a low-dimensional manifold. Our study develops a minimax framework for distribution estimation on unknown submanifolds, incorporating smoothness assumptions on both the target distribution and the manifold. Through the perspective of minimax rates, we examine some existing popular generative models, such as variational autoencoders, generative adversarial networks, and score-based generative models. By analyzing their theoretical properties, we characterize their statistical capabilities in implicit distribution estimation and identify certain limitations that could lead to potential improvements.<br />]]></description>
</item>

<item>
	<title>Video Imputation and Prediction Methods with Applications in Space Weather</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Tue, 30 Jan 2024 16:00:00 EST</pubDate>
	<description><![CDATA[When: Tue, January 30, 2024 - 4:00pm<br />Where: Kirwan Hall 3206<br />Speaker: Yang Chen (University of Michigan) - https://yangchenfunstatistics.github.io/yangchen.github.io/<br />
Abstract: <br />
The total electron content (TEC) maps can be used to estimate the signal delay of GPS due to the ionospheric electron content between a receiver and a satellite. This delay can result in a GPS positioning error. Thus, it is important to monitor and forecast the TEC maps. However, the observed TEC maps have big patches of missingness in the ocean and scattered small areas on the land. Thus, precise imputation and prediction of the TEC maps are crucial in space weather forecasting. <br />
<br />
In this talk, I first present several extensions of existing matrix completion algorithms to achieve TEC map reconstruction, accounting for spatial smoothness and temporal consistency while preserving essential structures of the TEC maps. We show that our proposed method achieves better reconstructed TEC maps than existing methods in the literature. I will also briefly describe the use of our large-scale complete TEC database. Then, I present a new model for forecasting time series data distributed on a matrix-shaped spatial grid, using the historical spatiotemporal data and auxiliary vector-valued time series data. Large sample asymptotics of the estimators for both finite and high dimensional settings are established. Performances of the model are validated with extensive simulation studies and an application to forecast the global TEC distributions.<br />]]></description>
</item>

<item>
	<title>Arboreal Galois groups: an introduction</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Wed, 07 Feb 2024 15:15:00 EST</pubDate>
	<description><![CDATA[When: Wed, February 7, 2024 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Robert Benedetto (Amherst College) - https://rlbenedetto.people.amherst.edu/<br />
Abstract: Let \(K\) be a field, over the field of rational numbers. Let \(f(z)\in<br />
K[z]\) be a polynomial of degree \(d\geq 2\) with coefficients in \(K\), and let<br />
\(x_0\in K\). <br />
The roots of \(f^n(x)-x_0\) are the iterated preimages of \(x_0\) under<br />
\(f\), and together they have the natural structure of a \(d\)-ary <br />
rooted tree \(T\).<br />
Thus, the Galois groups of the equations \(f^n(z)=x_0\) are known as arboreal<br />
Galois groups, because they act as automorphisms of this tree. Our focus in<br />
this talk will be on cases when the arboreal Galois groups are strictly smaller<br />
than the full automorphism group of \(T\), which can happen when the dynamical<br />
orbits of the critical points of \(f\) have certain special properties.<br />]]></description>
</item>

<item>
	<title>Higher theta series </title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Wed, 28 Feb 2024 15:15:00 EST</pubDate>
	<description><![CDATA[When: Wed, February 28, 2024 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Zhiwei Yun (MIT) - https://math.mit.edu/~zyun/<br />
Abstract: Theta series play an important role in the classical<br />
theory of modular forms. In the modern language of automorphic<br />
representations, they are constructed from a pair of groups G and<br />
H (one orthogonal and one symplectic, or both unitary groups) and<br />
the remarkable Weil representation of G×H.  Kudla introduced<br />
an analogue of theta series in arithmetic geometry, by forming a<br />
generating series of algebraic cycles on Shimura varieties. The<br />
arithmetic theta series has since become a very active program.<br />
<br />
In joint work with Tony Feng and Wei Zhang, we consider analogues of<br />
arithmetic theta series over function fields, and try to go further<br />
than what was done over number fields.  Our work concentrated on<br />
unitary groups. We defined a generating series of algebraic cycles on<br />
the moduli stack of unitary Drinfeld Shtukas (called the higher theta<br />
series). We made the Modularity Conjecture:  the higher theta series<br />
is an automorphic form valued in a certain Chow group. This is a<br />
function field analogue of the special cycles generating series<br />
defined by Kudla and Rapoport, but with an extra degree of freedom<br />
namely the number of legs of the Shtukas.<br />
<br />
One concrete formula we proved was a higher derivative version of the<br />
Siegel-Weil formula. It is an equality between degrees of<br />
0-dimensional special cycles on the moduli of unitary Shtukas and<br />
higher derivatives of the Siegel-Eisenstein series of another unitary<br />
group. More recently, we have obtained a proof of a weaker version of<br />
the Modularity Conjecture, confirming that the cycle class of the<br />
higher theta series (valued in the cohomology of the generic fiber) is<br />
automorphic.<br />
<br />
The series of talks will feature a colloquium-style introduction to<br />
some representation-theoretic and geometric background (the second talk), the other<br />
two being more technical talks in which I will explain some ingredients in<br />
the proofs of the higher Siegel-Weil formula and the weak Modularity<br />
Conjecture.<br />]]></description>
</item>

<item>
	<title>Random lattices and their applications in number theory, geometry and statistical mechanics</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Fri, 01 Mar 2024 15:15:00 EST</pubDate>
	<description><![CDATA[When: Fri, March 1, 2024 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Jens Marklof (School of Mathematics, University of Bristol) - https://www.bristol.ac.uk/people/person/Jens-Marklof-6eb63e14-a018-4833-9cf8-b95272b5a09e/<br />
Abstract: Lattices are fundamental objects in physics, mathematics and computer science. Starting from a cubic lattice, say, we can perturb the structure by linear transformations (shearing, stretching, rotating) to obtain a whole family of lattices. I will discuss the resulting &quot;space of lattices&quot;, the dynamics of group actions on this space, natural probability measures, as well as some fascinating applications to long-standing problems in various areas of mathematics and mathematical physics. My plan is to tell you about kinetic transport in crystals and quasicrystals (the Lorentz gas), pseudo-random properties of simple<br />
arithmetic sequences, knapsack problems, diameters of random Cayley graphs and (time permitting) subtle lattice point counting problems in hyperbolic geometry.<br />
<br />
Bio: Jens Marklof is Professor of Mathematical Physics at the University of Bristol, specialising in dynamical systems and ergodic theory, quantum chaos, and the theory of automorphic forms. Marklof received his PhD in 1997 from the University of Ulm, and held research fellowships at Princeton University, Hewlett-Packard, the Isaac Newton Institute in Cambridge, the Institut des Hautes Etudes Scientifique and the Laboratoire de Physique Theorique et Modeles Statistiques near Paris. He delivered a plenary address at the<br />
International Congress of Mathematical Physics in Prague 2009, and was an invited section speaker at the International Congress of Mathematicians in Seoul 2014. Major awards include a 2010 LMS Whitehead Prize and a five-year ERC Advanced Grant. In 2015 Marklof was elected a Fellow of the Royal Society, the UK&#039;s national academy of sciences. From November 2023 he will serve a two-year term as President of the London Mathematical Society.<br />]]></description>
</item>

<item>
	<title>TBA</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Thu, 14 Mar 2024 15:00:00 EDT</pubDate>
	<description><![CDATA[When: Thu, March 14, 2024 - 3:00pm<br />Where: Kirwan Hall 3206<br />Speaker: Svetlana Jitomirskaya (University of California, Berkeley) - https://math.berkeley.edu/people/faculty/svetlana-jitomirskaya<br />
<br />]]></description>
</item>

<item>
	<title>Instantaneous everywhere-blowup of parabolic stochastic PDEs</title>
	<link>http://www-math.umd.edu/research/seminars.html</link>
	<pubDate>Wed, 03 Apr 2024 15:15:00 EDT</pubDate>
	<description><![CDATA[When: Wed, April 3, 2024 - 3:15pm<br />Where: Kirwan Hall 3206<br />Speaker: Davar Khoshnevisan (University of Utah) - http://www.math.utah.edu/~davar/<br />
Abstract: We consider a one-dimensional heat equation of the form $\partial_t u = \partial^2_x u + b(u) + \sigma(u)\dot{W}$, where the forcing term $\dot{W}$  is space-time white noise. We survey aspects of a long history of the study of blowup for this family of heat equations, and conclude with recent joint work with Mohammud Foondun and Eulalia Nualart on instantaneous, everywhere blowup which seems to be a novel property. Time permitting, we might mention also aspects of the proof of the latter results, especially as they relate to new developments in the ergodic theory of parabolic stochastic partial differential equations, developed by Le Chen, the speaker, David Nualart, and Fei Pu (2021, 2022).<br />
<br />
This is based on joint work with Mohammud Foondun and Eulalia Nualart.<br />]]></description>
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