Sharp Fourier Restriction onto the Parabola and the Cubic Monomial - Lecture 1
Explore sharp Fourier restriction estimates for parabola and cubic monomial curves, advancing understanding in harmonic analysis and algebraic geometry applications.
Sharp Fourier Restriction onto the Parabola and the Cubic Monomial - Lecture 2
Explores sharp Fourier restriction estimates for parabola and cubic monomial curves, advancing understanding of harmonic analysis and polynomial methods in mathematical research.
The Variance of a General Class of Multiplicative Functions in Short Intervals
Explore the variance of multiplicative functions in short intervals, applying Fourier analysis and rational point counting to derive asymptotic estimates for k-free numbers and other functions.
Distance Problems and Their Many Variants - Lecture 2
Explores distance problems in harmonic analysis, focusing on variants and recent developments. Discusses polynomial methods and discrete approaches, offering insights for researchers in the field.
Nikodym Sets and Maximal Functions Associated with Spheres - Lecture 2
Explores advanced concepts in harmonic analysis, focusing on Nikodym sets and maximal functions related to spheres. Builds on recent developments in the field.
How Much Math Do You Need to Know to Solve an Initial Value Problem - SIAM PDE Seminar
Explore mathematical techniques for solving nonlinear Schrödinger equations, from harmonic analysis to dynamical systems, focusing on long-time dynamics and energy transfer in initial value problems.
Orthogonality Sampling Methods for Electromagnetic Inverse Scattering Problems
Exploring orthogonality sampling methods for electromagnetic inverse scattering problems, focusing on implementation simplicity, noise robustness, and applications in 2D and 3D experimental data.
Bayesian Reimaging of Sparsity in Inverse Problems - SIAM-IS Virtual Seminar
Bayesian approach to sparse inverse problems in imaging and dictionary learning, featuring efficient computational framework and hierarchical prior models for sparsity promotion and automatic model reduction.
Bayesian Reimaging of Sparsity in Inverse Problems - IMAGINE Seminar Series
Explore Bayesian approaches to sparse inverse problems in imaging and dictionary learning, focusing on efficient computational frameworks and hierarchical prior models.
A Fourier Transform for Abelian Fibrations
Explore Fourier transform theory for abelian fibrations, extending Beauville decomposition and examining perverse filtration applications in algebraic geometry and motivic theory.
Fourier Analysis of Equivariant Quantum Cohomology II - 1-27-2024
Explore advanced concepts in Fourier analysis and equivariant quantum cohomology, building on previous research in homological mirror symmetry and mathematical physics.
Complexity of Sparse Linear Regression
Explore new methods for solving sparse linear regression, overcoming limitations of traditional approaches and exploiting structural properties of covariance matrices in machine learning and statistics.
Fourier Analysis of Equivariant Quantum Cohomology - Part I
Explore Teleman's conjecture, equivariant quantum cohomology, and Fourier spectral analysis in symplectic geometry. Gain insights into D-module decomposition for projective bundles and blowups.
Spectrum of Sparse Inhomogeneous Random Graphs
Explore the spectrum of sparse inhomogeneous random graphs, examining structural properties and applications in network science, probability theory, and statistical physics.
Towards a Formalized Proof of Carleson's Theorem
Explores formalizing Carleson's theorem on Fourier series convergence, discussing its significance, challenges, and recent generalizations in metric spaces using the Lean theorem prover.
Fourier Uniformity of Multiplicative Functions
Explore Fourier uniformity of multiplicative functions, its connection to prime number distribution, and related conjectures like Chowla and Sarnak in this advanced mathematics lecture.
Fourier Integral - Cosine and Sine Integrals with Important Problems
Explore Fourier Integral concepts, including cosine and sine integrals, through clear explanations and problem-solving techniques for engineering applications.
Laplace Transform of Periodic Function - Engineering Mathematics Problem
Learn to apply Laplace transforms to periodic functions, focusing on square waves. Gain practical skills for solving engineering math problems involving periodic signals.
Quantum Diffusion Convolution Kernels on Graphs
Explore quantum diffusion convolution kernels for graph neural networks, improving predictive accuracy through quantum particle propagation and multiscale message passing techniques.
Signal Recovery, Restriction Theory, and Applications - Lecture 3
Explore signal recovery techniques using Fourier transforms and restriction theory. Delve into improved recovery conditions and multi-linear approaches for multiple transmissions.
MM Principle of Optimization - CGSI 2023
Explore the MM Principle of Optimization in computational genomics, covering algorithms, applications in signal processing, and machine learning techniques.