Aims and Scope
The conference will focus on recent advances in applied and computational harmonic analysis. Topics will include, but are not limited to, compressed sensing, frame theory, phase retrieval, randomized algorithms, convolutional neural networks, deep learning, graph-based signal processing, quantum computing, wavelet theory, time-frequency analysis, sampling theory, image processing, and related aspects of machine learning, data science and applied mathematics.
- Luis Daniel Abreu (Acoustics Research Institute Vienna)
- "Time-frequency analysis: from the plane to the flat torus. Deterministic and random aspects"
- Akram Aldroubi (Vanderbilt University)
- "Optimal transport transforms in signal processing and data science"
- Rima Alifari (ETH Zürich)
- "Recent advances in phase retrieval"
- Afonso Bandeira (ETH Zürich)
- "Computation, statistics, and optimization of random functions"
- Mikhail Belkin (Ohio State University)
- "The mathematical challenges of modern machine learning"
- Helmut Boelcskei (ETH Zürich)
- "Fundamental limits of generative deep neural networks"
- Annie Cuyt (University of Antwerp)
- "Exponential analysis: solving open problems and unlocking new potential"
- Mark Iwen (Michigan State University)
- "Generalized sparse Fourier transforms for approximating functions of many variables"
- Hrushikesh Mhaskar (Claremont Graduate University)
- "Super-resolution meets machine learning"
- Dustin Mixon (Ohio State University)
- "Optimal projective codes"
- Justin Romberg (Georgia Institute of Technology)
- "Distributed stochastic approximation: reinforcement learning and optimization with communication constraints"
- Karin Schnass (University of Innsbruck)
- "A peek at the landscape of dictionary learning"
- Joel Tropp (California Institute of Technology)
- "Scalable semidefinite programming"
- Holger Boche (TU Munich)
- Charles Chui (Hongkong Baptist University)
- Massimo Fornasier (TU Munich)
- Felix Krahmer (TU Munich)
- Gitta Kutyniok (LMU Munich)
- Götz Pfander (KU Eichstätt)