http://www-math.umd.edu/research/seminars.html http://www-math.umd.edu/research/seminars.html Thu, 26 Oct 2023 09:45:00 EDT Where: Kirwan Hall 3206
Speaker: Various () - https://www.norbertwiener.umd.edu/fft/2023/index.html
Abstract: The entire schedule is posted here: https://www.norbertwiener.umd.edu/fft/2023/index.html. It features talks by 15 distinguished speakers.
]]> http://www-math.umd.edu/research/seminars.html Fri, 27 Oct 2023 09:45:00 EDT Where: Kirwan Hall 3206
Speaker: Various () - https://www.norbertwiener.umd.edu/fft/2023/index.html
Abstract: The entire schedule is posted here: https://www.norbertwiener.umd.edu/fft/2023/index.html. It features talks by 15 distinguished speakers.
]]> http://www-math.umd.edu/research/seminars.html Wed, 29 Nov 2023 11:00:00 EST Where: Kirwan Hall 3206
Speaker: Dominique Maldague (MIT) - https://math.mit.edu/~dmal/
Abstract: This talk will provide an overview of recent developments in Fourier restriction theory, which is the study of exponential sums over restricted frequency sets with geometric structure, typically arising in pde or number theory. Decoupling inequalities measure the square root cancellation behavior of these exponential sums. I will highlight recent work which uses the latest tools developed in decoupling theory to prove much more delicate sharp square function estimates for frequencies lying in the cone in R^3 (Guth-Wang-Zhang) and moment curves (t,t^2,...,t^n) in all dimensions (Guth-Maldague).
]]> http://www-math.umd.edu/research/seminars.html Mon, 26 Feb 2024 14:00:00 EST Where: Kirwan Hall 1311
Speaker: Simon Foucart (Texas A & M University) -
Abstract: This talk showcases the speaker’s recent results in the field of Optimal Recovery, viewed as a trustworthy Learning Theory focusing on the worst case. At the core of several results presented here is a scenario, resolved in the global and the local settings, where the model set is the intersection of two hyperellipsoids. This has implications in optimal recovery from deterministically inaccurate data and in optimal recovery under a multifidelity-inspired model. In both situations, the theory becomes richer when considering the optimal estimation of linear functionals. This particular case also comes with additional results in the presence of randomly inaccurate data.
]]> http://www-math.umd.edu/research/seminars.html Mon, 11 Mar 2024 14:00:00 EDT Where: https://umd.zoom.us/j/2611189411?omn=98105719510
Speaker: Charles Fefferman (Princeton) -
Abstract: How can we tell whether a given rational function F=P/Q is continuous on R^n? If the polynomials P and Q have common zeros, the question is subtle. It’s a special case of a problem on the existence of solutions of systems of linear equations for unknown C^m functions. That problem has unexpected connections to a problem on extension of functions, posed by Whitney in 1934. Whitney’s problem in turn is related to manifold learning.

This talk explains the connections and sketches some relevant ideas. A follow-on talk in a few weeks will cover Whitney’s problem in greater depth.

Joint work with Garving (Kevin) Luli and Janos Kollar.
]]> http://www-math.umd.edu/research/seminars.html Mon, 25 Mar 2024 14:00:00 EDT Where: Kirwan Hall 1313
Speaker: Edinah Gnang (Johns Hopkins University) - https://engineering.jhu.edu/faculty/edinah-gnang/
Abstract: There has been extensive study of diagonalization of matrices. Diagonalization can be viewed as using a similarity transform to concentrate the magnitude of all entries within as small a subset of entries as possible. We present results in our talk on what can be viewed as reversing this process, namely spreading out the magnitudes of entries as uniformly as possible.
]]> http://www-math.umd.edu/research/seminars.html Mon, 01 Apr 2024 14:00:00 EDT Where: https://umd.zoom.us/j/2611189411?omn=98105719510
Speaker: Garry Jacyna (MITRE) -

]]> http://www-math.umd.edu/research/seminars.html Mon, 06 May 2024 14:00:00 EDT Where: Kirwan Hall 1313
Speaker: Matthias Wellershoff (UMD) -
Abstract: Phase retrieval refers to mathematical problems in which one aims to recover the phases of a large structured collection of complex numbers from their moduli: if the collection has enough redundancy, it is possible to reconstruct the complex phases. In this talk, we investigate the problem of recovering real-valued signals from the magnitudes of their wavelet frame coefficients. We will demonstrate that such signals can be uniquely recovered (up to global phase) from certain multi-wavelet frame coefficients based on the Poisson wavelet. Notably, our results do not require imposing any bandlimiting constraints or a priori knowledge on the signals.
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