Microscopy is critical for discovery and innovation in science and technology, accelerating advances in physics, chemistry, biology, materials science, nanoscience and energy sciences. Recent years have witnessed at least three revolutions in microscopy. First, the 2014 Nobel Prize in chemistry recognized super-resolved fluorescence microscopy, which brings optical microscopy into the nanoscale. Second, the 2017 Nobel Prize in chemistry was awarded to cryo-electron microscopy (cryo-EM) for the high-resolution structure determination of biomolecules in solution. Third, coherent diffractive imaging (CDI) has been developed to transform our conventional view of microscopy by replacing the physical lens with computational algorithms, allowing lensless imaging with a resolution only limited by the diffraction signal. All these groundbreaking developments require the use of advanced computational algorithms and mathematical tools.
The goal of this long program proposal is to bring together senior and junior applied mathematicians, physicists, chemists, materials scientists, engineers and biologists to discuss and debate on the current status and future perspectives of modern microscopy using computation, mathematics and modeling. Cryo-EM has revolutionized biology and life science (including very recently solving the 3D atomic structure of COVID-19, which has been greatly facilitating the development of the vaccines) and aberration-corrected electron optics and high brightness X-ray sources have transformed physical science imaging. The next steps in these fields will advance by orders of magnitude the temporal resolution and energy resolution, while maintaining atomic spatial resolution, in a variety of sample environments from near zero Kelvin in vacuum to temperatures of a thousand degrees in a highly corrosive atmosphere. These advances will transform research in macromolecules, materials, energy technologies, quantum devices, and other fields. However, they all result in multidimensional, multimodal, big and extremely noisy data. Therefore, sophisticated mathematical and computational methods to derive the maximum possible useful scientific information from the minimum possible quanta of radiation are urgently needed. The four workshops will bring together leading applied mathematicians, physicists, data scientists and computational scientiststo discuss strategies to tackle these major scientific challenges through a combination of advanced algorithms, mathematical modeling, computational tools, big data processing and deep learning.
(University of South Carolina, Mathematics)
Angus Kirkland (University of Oxford, Materials)
Gitta Kutyniok (Technische Universität Berlin, Mathematics)
John Miao (University of California, Los Angeles (UCLA), Physics & Astronomy)
Margaret Murnane (University of Colorado Boulder, Physics)
Deanna Needell (University of California, Los Angeles (UCLA), Mathematics)
Stanley Osher (University of California, Los Angeles (UCLA), Mathematics)
Zineb Saghi (Commissariat à l'Énergie Atomique (CEA))
Amit Singer (Princeton University, Mathematics)
Paul Voyles (University of Wisconsin-Madison, Materials Science & Engineering)
Laura Waller (University of California, Berkeley (UC Berkeley), Electrical Engineering & Computer Sciences)