Journal-first submissions deadline
8 Aug
Priority submissions deadline 30 Jul
Final abstract submissions deadline 15 Oct
Manuscripts due for FastTrack publication
17 Nov

Early registration ends 15 Dec

Short Courses
16 Jan
Symposium begins
17 Jan
All proceedings manuscripts due
31 Jan

Machine Learning for Scientific Imaging 2022

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

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ATTENTION: EI 2022 will occur live ONLINE. 
The program will include a variety of opportunities to interact with colleagues and presenters live, in formal and informal settings. IS&T is committed to providing the best online experience possible and supporting the community in light of travel/reimbursement restrictions. Join us for EI 2022 . . . submit your work today!

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.

2022 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

2022 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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