Workshop I: Diffractive Imaging with Phase Retrieval

Part of the Long Program Computational Microscopy
October 10 - 14, 2022

Schedule

All times in this Schedule are Pacific Time (PT)

Monday, October 10, 2022

Morning Session

8:00 - 8:55 Breakfast (hosted by IPAM)
8:55 - 9:00 Welcome & Opening Remarks

Session Chair (AM): Margaret Murnane (Univ. Colorado Boulder)

9:00 - 9:40
Jianwei (John) Miao (University of California, Los Angeles (UCLA))

Computational Microscopy: From Coherent Diffractive Imaging to Atomic Electron Tomography

 
9:50 - 10:05 Break
10:05 - 10:45
10:55 - 11:10 Break
11:10 - 11:50
12:00 - 2:00 Lunch (on your own)

Afternoon Session

Session Chair (PM): Laura Waller (UC Berkeley)

2:00 - 2:40
2:50 - 3:05 Break
3:05 - 3:45
3:55 - 4:15 Lightning Poster Presentations
4:15 - 4:45 Break - Go to NRB
5:00 - 6:00
6:15 - 7:45 Poster Session & Reception (Hosted by IPAM)

Tuesday, October 11, 2022

Morning Session

8:00 - 9:00 Breakfast (hosted by IPAM)

Session Chair (AM): Stan Osher (UCLA)

9:00 - 9:40
Michael Unser (École Polytechnique Fédérale de Lausanne (EPFL))

Virtual Talk: High-Speed Fourier Ptychography with Deep Spatio-Temporal Priors

 
9:50 - 10:05 Break
10:05 - 10:45
George Barbastathis (Massachusetts Institute of Technology)

 

 
10:55 - 11:10 Break
11:10 - 11:50
Jason Fleischer (Princeton University)

Hybrid machine learning for enhanced phase retrieval

 
12:00 - 2:00 Lunch (on your own)

Afternoon Session

Session Chair (PM): Hyungjung Kim (Sogang Univ.)

2:00 - 2:40
Laura Waller (University of California, Berkeley (UC Berkeley))

3D phase imaging with scattering samples

 
2:50 - 3:05 Break
3:05 - 3:45
Albert Fannjiang (University of California, Davis (UC Davis))

From Tomographic Phase Retrieval to Projection Tomography

 
3:55 - 4:45 Break - Go to NRB
5:00 - 6:00

Wednesday, October 12, 2022

Morning Session

8:00 - 9:00 Breakfast (hosted by IPAM)

Session Chair (AM): John Miao

9:00 - 9:40
Sebastian Seung (Princeton University)

 

 
9:50 - 10:05 Break
10:05 - 10:45
Katie Bouman (California Institute of Technology)

 

 
10:55 - 11:10 Break
11:10 - 11:50
Chris Jacobsen (Argonne National Laboratory/Northwestern University)

Coherent x-ray imaging: how big can we go small?

 
12:00 - 2:00 Lunch (on your own)

Afternoon Session

Session Chair (PM): Chris Jacobsen (ANL/Northwestern Univ.)

2:00 - 2:40
2:50 - 3:05 Break
3:05 - 3:45
3:55 - 4:10 Break
4:10 - 4:50

Thursday, October 13, 2022

Morning Session

8:00 - 9:00 Breakfast (hosted by IPAM)

Session Chair (AM): Tim Salditt (Georg-August-Universität zu Göttingen)

9:00 - 9:40
9:50 - 10:05 Break
10:05 - 10:45
Demetri Psaltis (École Polytechnique Fédérale de Lausanne (EPFL))

Machine Learning for 3D Optical Imaging

 
10:55 - 11:10 Break
11:10 - 11:50
Monika Ritsch-Marte (Medical University of Innsbruck)

Virtual Talk: Phase Retrieval and Optical Trapping’

 
12:00 - 2:00 Lunch (on your own)

Afternoon Session

Session Chair (PM): Oleg Shpyrko (UC San Diego)

2:00 - 2:40
Stefano Marchesini (SLAC National Accelerator Laboratory)

Framewise discrepancies in Ptychographic phase retrieval

 
2:50 - 3:05 Break
3:05 - 3:45
3:55 - 4:10 Break
4:10 - 4:50
 

Friday, October 14, 2022

Morning Session

8:00 - 9:00 Breakfast (hosted by IPAM)

Session Chair (AM): Ian Robinson

9:00 - 9:40
Anne Sentenac (Fresnel Institute)

Virtual Talk: TBD

 
9:50 - 10:05 Break
10:05 - 10:45
Margaret Murnane (University of Colorado Boulder)

Attosecond Quantum Technologies for Advanced Materials Metrologies

 
10:55 - 11:10 Break
11:10 - 11:50
Ivan Vartaniants (Deutsches Elektronen-Synchrotron (DESY))

 

 
12:00 - 2:00 Lunch (on your own)

Afternoon Session

Session Chair (PM): Katie Bouman (Caltech)

2:00 - 2:40
Aydogan Ozcan (University of California, Los Angeles (UCLA))

Diffractive Optical Networks & Computational Imaging Without a Computer

 
2:50 - 3:05 Break
3:05 - 3:45
Mathew Cherukara (Argonne National Laboratory)

HPC+AI-Enabled Real-Time Coherent X-ray Diffraction Imaging

 
3:55 - 4:10 Break
4:10 - 4:50
Ju Sun (University of Minnesota, Twin Cities)

Toward practical phase retrieval with deep learning