Content Gazette - June 2020 - 3

Date: 2 June 2020
Speaker: Laura Waller, UC Berkeley
Title: End-To-End Learning for Computational Microscopy
Abstract: Computational imaging involves the joint design of imaging system hardware and
software, optimizing across the entire pipeline from acquisition to reconstruction. Computers
can replace bulky and expensive optics by solving computational inverse problems. This talk
will describe end-to-end learning for development of new microscopes that use
computational imaging to enable 3D fluorescence and phase measurement. Traditional
model-based image reconstruction algorithms are based on large-scale nonlinear non-convex
optimization; we combine these with unrolled neural networks to learn both the image
reconstruction algorithm and the optimized data capture strategy.
About the presenter:
Laura Waller is the Ted Van Duzer Associate Professor of Electrical Engineering
and Computer Sciences (EECS) at UC Berkeley, a Senior Fellow at the Berkeley
Institute of Data Science, and affiliated with the UCB/UCSF Bioengineering
Graduate Group. She received the B.S., M.Eng., and Ph.D. degrees from the
Massachusetts Institute of Technology (MIT) in 2004, 2005, and 2010, and was
a Postdoctoral Researcher and Lecturer of Physics at Princeton University from
2010-2012.
She is a Packard Fellow for Science & Engineering, Moore Foundation Datadriven Investigator, Bakar Fellow, OSA Fellow and Chan-Zuckerberg Biohub Investigator.
Dr. Waller has received the Carol D. Soc Distinguished Graduate Mentoring Award, Agilent Early Career
Professor Award (Finalist), NSF CAREER Award, and the SPIE Early Career Achievement Award.

Date: 16 June 2020
Speaker: Michael Unser, EPFL, Lausanne, Switzerland
Title: CryoGAN: A novel paradigm for single-particle analysis and 3D reconstruction in cryoEM microscopy
Abstract: Single-particle cryo-EM has revolutionized structural biology over the last decade
and remains an active topic of research. In particular, the reconstruction task is an enduring
technical challenge due to the imaged 3D particles having unknown orientations. Scientists
have spent the better part of the last 30 years designing a solid computational pipeline that
can reliably deliver 3D structures with atomic resolution. The result is an intricate multi-steps
procedure that permits the regular discovery of new structures, but that is yet still prone to
overfitting and irreproducibility. The most notable difficulties with the current paradigms are
the need for pose-estimation methods, the reliance on user expertise for appropriate
parameter tuning, and the non-straightforward extension to the handling of structural
heterogeneity. To overcome these limitations, we recently proposed a completely new
paradigm for single-particle cryo-EM reconstruction that leverages the remarkable capability
of deep neural networks to capture data distribution. Based on an adversarial learning
scheme, the CryoGAN algorithm can resolve a 3D structure in a single algorithmic run using
www.signalprocessingsociety.org

[3]

JUNE 2020
http://www.signalprocessingsociety.org
Content Gazette - June 2020

Table of Contents for the Digital Edition of Content Gazette - June 2020
