ICCHA2022

Announcement

Dear ICCHA 2022 participants,

we are happy to inform you that ACHA has now opened its submission portal for our special issue on Mathematical Insights in Machine Learning and Data Science. You are invited to submit papers relating to the focus areas of ICCHA via the submission page at https://www.sciencedirect.com/journal/applied-and-computational-harmonic-analysis. As article type, please select MLDS-ICCHA2022. Submission deadline is ~~March 1, 2023~~! Articles will be processed as received and will be published as soon as the reviewing process of the individual paper is complete.

NOTE: On request, the submission deadline is now changed to April 30, 2023.

We hope to receive many ACHA level submissions!

On behalf of the special issue guest editors

Mikhail Belkin
Götz Pfander
Holger Rauhut
David Walnut

The conference program

Aims and Scope

The conference will focus on recent advances in applied and computational harmonic analysis. The scope of the conference includes randomized algorithms, convolutional neural networks, deep learning, graph-based signal processing, quantum computing, wavelet theory, time-frequency analysis, sampling theory, image processing, compressed sensing, frame theory, phase retrieval, and related aspects of machine learning, data science and applied mathematics.

Plenary Speakers

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 Alaifari (ETH Zürich)
- "Recent advances in phase retrieval"
Afonso Bandeira (ETH Zürich)
- "Computation, statistics, and optimization of random functions"
Mikhail Belkin (University of California, San Diego)
- "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"

Conference Organizers

Holger Boche (TU Munich)
Charles Chui (Hongkong Baptist University)
Massimo Fornasier (TU Munich)
Felix Krahmer (TU Munich)
Gitta Kutyniok (LMU Munich)
Dae Gwan Lee (KU Eichstätt)
Johannes Maly (KU Eichstätt)
Götz Pfander (KU Eichstätt)
Dominik Stöger (KU Eichstätt)
Felix Voigtlaender (KU Eichstätt)