Exploratory microscopy is simultaneously driven by observations and hypotheses. Consequently, we need to develop robust interfaces that connect massive, complex, noisy, and incomplete data with foundational scientific knowledge.
Our group is interested in combining machine learning with scientific and instrument priors to create computational lenses that help make sense of the chaotic and nearly invisible dynamics that occur at the nanometer-scale. These lenses combine statistical learning with priors (i.e., probe-sample interaction Physics, Optics, and instrumentation).
We first developed these computational lenses for single-particle diffractive imaging with X-ray Free-electron Lasers, where unsupervised statistical learning can discover transient intermediate states and spontaneous order formation in highly heterogeneous and dynamic systems.
Our group then extended these noise-robust methods to electron-based imaging sciences to develop core ideas and technologies towards far-reaching computational lenses that are too challenging for hardware-based electron microscopy alone.
Human visual perception is part-eye and part-brain. In the context of imaging, imaging algorithms form the brain of our optical instruments. In doing so, these algorithms can help us measure instead of merely see.
High-throughput detectors and efficient computation have enabled us to create computational versions of physical lenses. These computational lenses are capable of imaging very small objects in noisy and challenging situations, often beyond the designed capabilities of their physical counterparts. For example, we are able to augment the functions of computational lenses by inserting known prior knowledge about the capabilities of the underlying optics and/or characteristics of the specimen under study. Our group pushes and helps define the capabilities of such computational lenses.
Computational Lenses for X-ray Imaging.
Coherent diffractive imaging.
Iterative phase retrieval with noisy and incomplete diffraction patterns.
Unsupervised clustering techniques for very large diffraction datasets.
Resolving structural heterogeneity from serial imaging of disordered ensembles.
3D Serial Femtosecond Imaging for x-ray free-electron lasers.
Ptychography (for wavefront sensing).
Ewald sphere sections (Source: Duane@Mathematica).
Computational Lenses for Electron Microscopy.
3D pop-out metrology from single 2D transmission electron micrographs.
Robust motion correction at ultra-low electron doses.
Data reduction and compression.
4DSTEM: CoM, ptychography, Lorentz imaging.
Single particle analyses for cryo-electron microscopy.
Phase retrieval for electron microscopy.
Performant simulators for STEM, and TEM.
Midjourney thinks this is a transmission electron microscope… 😂
Order-disorder Transitions.
Data Science and Machine Learning.
A dramatic enactment of how we infer the orientations of diffraction patterns (source: Duane@Blender).
Pictures of us about to eat food.
Pictures of us doing activities besides eating.
Detailed list of publications.
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