ICCHA2022

8th International Conference on
Computational Harmonic Analysis

September 12-16, 2022

Munich, Germany

On-site ICCHA is postponed to 2022 - Online ICCHA takes place in 2021

Dear prospective participants,

Considering the recent developments surrounding the coronavirus pandemic, we have decided to postpone the event by another year to September 2022.

Currently, we are planning an online conference which will be focused on providing opportunity to young researchers (up to 5 years past PhD) to present their work. This event (tentatively named "Online ICCHA 2021") is expected to be held during the originally planned week of ICCHA 2021 (September 13-17, 2021). Submission deadlines for potential contributed talks will not be earlier than June 1, 2021. We will be publishing a special issue in SASIDA (Sampling Theory, Signal Processing, and Data Analysis) which is open to contributors of Online ICCHA 2021. More details will be announced soon, latest by the end of May.

Conference Chairs
Holger Boche
Charles Chui
Massimo Fornasier
Felix Krahmer
Gitta Kutyniok
Götz Pfander



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.

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 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"

Conference Chairs

  • 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)
TUM
LMU
KU