IMPORTANT DATES
Dates currently being confirmed; check back.
 

2022
Call for Papers Announced 2 May
Journal-first (JIST/JPI) Submissions

∙ Submission site Opens 2 May 
∙ Journal-first (JIST/JPI) Submissions Due 1 Aug
∙ Final Journal-first manuscripts due 28 Oct
Conference Papers Submissions
∙ Abstract Submission Opens 1 June
∙ Priority Decision Submission Ends 15 July
∙ Extended Submission Ends  19 Sept
∙ FastTrack Conference Proceedings Manuscripts Due 16 Dec 
∙ All Outstanding Proceedings Manuscripts Due
 6 Feb 2023
Registration Opens late summer
Demonstration Applications Due 9 Dec
Early Registration Ends 18 Dec


2023
Hotel Reservation Deadline 6 Jan
Symposium begins
15 Jan


Machine Learning for Scientific Imaging 2023

Conference keywords: machine learning, physics inspired machine learning, artificial intelligence, scientific imaging, deep learning

On this page

ATTENTION: EI 2023 will occur IN-PERSON. 

Conference Overview

Machine learning for scientific imaging is a rapidly growing area of research used to characterize physical, material, chemical, and biological processes in both large and small scale scientific experiments. Physics inspired machine learning differs from more general machine learning research in that it emphasizes quantitative reproducibility and the incorporation of physical models. ML methods used for scientific imaging typically incorporate physics-based imaging processes or physics-based models of the underlying data. These models can be based on partial differential equations (PDEs), integral equations, symmetries or other regularity conditions in two or more dimensions. Physics aware models enhance the ability of the ML methods to generalize and robustly operate in the presence of modeling error, incomplete data, and measurement uncertainty. Contributions to the conference are solicited on topics ranging from fundamental theoretical advances to detailed implementations and novel applications for scientific discovery.

2023 Conference Topics

Algorithms and methodologies

  • Machine learning-inspired scientific imaging system design
  • Learning-based modeling and algorithms for scientific imaging
  • Novel machine learning theory for scientific imaging
  • Physics-inspired machine learning theory and applications
  • Machine learning-inspired new physics

Key problem areas

  • Tomography (transmission and emission)
  • NMR, MRI
  • Diffraction theory
  • Synchrotron X-ray imaging
  • XFEL
  • Crystallography
  • Synthetic aperture radar
  • Electron microscopy
  • Structural biology
  • Phase retrieval
  • Phase contrast imaging
  • Fourier ptychography
  • Astronomical imaging
  • Physics based machine learning of dynamic processes
 

2023 Committee

Conference Chairs

Marc Louis Klasky, Los Alamos National Laboratory (United States)
Jong Chul Ye, Korea Advanced Institute of Science & Technology (KAIST) (Republic of Korea)

Program Committee

Raja Giryes, Tel Aviv University (Israel)
Ulugbek Kamilov, Washington University in St. Louis (United States)
Christopher Vincent Rackauckas, Massachusetts Institute of Technology (United States)
Daniel M. Tartakovsky, Stanford University (United States)
Lei Tian, Boston University (United States)
Nathaniel Trask, Sandia National Laboratories (United States)

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