Category Archives: science life

SPIE DCS 2018: CCSI – Computational Imaging

This year I’m chairing the Computational Imaging session at the SPIE Defense + Commercial Sensing, in Orlando, Fla., April 16-19, 2018, together with Aamod Shanker. We have invited a lot of amazing speakers and we are organizing a panel discussion on the trends in computational imaging.

Here’s the program:

SESSION 6 TUE APRIL 17, 2018 – 11:10 AM TO 12:00 PM
Computational Imaging I
[10656-22] “Ultra-miniature…”David G. Stork, Rambus Inc. (USA)
[10656-36] “Computed axial lithography: volumetric 3D printing of arbitrary geometries” Indrasen Bhattacharya
Lunch/Exhibition Break Tue 12:00 pm to 1:50 pm

SESSION 7 TUE APRIL 17, 2018 – 1:50 PM TO 3:30 PM
Computational Imaging II
[10656-24] “Terahertz radar for imaging…”Goutam Chattopadhyay
[10656-23] “Computational imaging…” Lei Tian
[10656-26] “Achieving fast high-resolution 3D imaging” Dilworth Y. Parkinson
[10656-27] “Linear scattering theory in phase space” Aamod Shanker



SESSION 8 WED APRIL 18, 2018 – 8:00 AM TO 10:05 AM
Computational Imaging III
[10656-28] “High resolution 3D imaging…” Michal Odstrcil
[10656-29] “A gigapixel camera array…” Roarke Horstmeyer
[10656-30] “EUV photolithography mask inspection using Fourier ptychography” Antoine Wojdyla,
[10656-31] “New systems for computational x-ray phase imaging…” Jonathan C. Petruccelli,
[10656-68] “Low dose x-ray imaging by photon counting detector”, Toru Aoki

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With increasingly tight beamline specifications, optical modeling software becomes necessary in order to design and predict the performances of conceptual beamlines. This becomes particularly true with the advent of highly coherent light sources (such the proposed upgrade of the ALS), where additional considerations such mirror deformation under heat load and effects of partial coherence needs to be studied. Luca Rebuffi will present the latest features of OASYS/Shadow, an optical beamline modeling tool widely used in the synchrotron community and show how to get started with beamline simulations.

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Moore’s wall

A single chip such has Intel Xeon Phi has a computational power in excess of 1TFLOPS and features more than a hundred billion transistors. Few people  outside the world of semi-conductor engineering appreciate this, but that is a fantastical number: 100,000,000,000. If every transistor was a pixel, you would need a wall 0f 100 x 100 4K TV screen to display them all!

Over the past fifty years, the semiconductor industry has achieved incredible things, in part thanks to planar technology, which allowed to exponentially scale the manufacturing process, following Moore’s law. But it seems that we’re about to hit a wall soon.


Let’s give an overview of where we stand, and where do we go from here!

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Learning Deep

In the past four years, there’s been a lot of progress in the field of machine learning, and here’s a story seen from the outskirts.

Eight years ago, for a mock start-up project, we tried to do some basic headtracking. At that time, my professor Stéphane Mallat told us that the most efficient way to do this was the Viola-Jones algorithm, which was still based on hard-coded features (integral images and Haar features) and a hard classifier (adaboost.)

(I was thrilled when a few years later Amazon Firephone was embedding similar features; unfortunately, this was a complete bomb — better technologies now exist and will make a splash pretty soon.)

By then, the most advanced book on machine learning was “Information Theory, Inference, and Learning” by David McKay, a terrific book to read, and also “Pattern Recognition and Machine Learning” by Chris Bishop (which I never read past chapter 3, lack of time.)

Oh boy, how things have changed!

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Julia Language

A few years ago, I got interested in the then-nascent Julia language (, a new open source language based on Matlab syntax with C-like performances, thanks to its just-in-time compiler.

Large Synoptic Survey Telescope (LSST, Chile) data being processed with Julia on super computers with 225x speedup

Large Synoptic Survey Telescope (LSST, Chile) data being processed with Julia on super computers with 200x speedup (from

It now seems that the language is gaining traction, with many available packages, lots of REPL integration (it works with Atom+Hydrogen, and I suspect Jupyter gets its first initial from Julia and Python) and delivering on performances.

Julia is now used on supercomputers, such as Berkeley Lab’s NERSC, taught at MIT (by no less than Steven G Johnson, the guy who brought us FFTW and MEEP!), and I’ve noticed that some of the researchers from Harvard’s RoLi Lab I’ve invited to SPIE DCS 2018 are sharing their Julia code from their paper “Pan-neuronal calcium imaging with cellular resolution in freely swimming zebrafish“. Pretty cool!

Julia used for code-sharing in a Nature publication. I wish I could see that every day!

Julia used for code-sharing in a Nature publication. I wish I could see that every day!

I got a chance to attend parts of Julia Con 2017 in Berkeley. I was amazed by how dynamic was the the community, in part supported by Moore’s foundation (Carly Strasser, now head of Coko Foundation), and happy to see Chris Holdgraf (my former editor at the Science Review) thriving at the Berkeley Institute for Data Science (BIDS).

Julia picking up speed at Intel (picture taken dusing JuliaCon 2017)

Julia picking up speed at Intel (picture taken dusing JuliaCon 2017)

I started sharing some code for basic image processing (JLo) on Github. Tell me what you think!

(by the way, I finally shared my meep scripts on github, and it’s here!)

Sexism in academia

This year, the recipients of the Nobel Prize were 100% men. That’s at the same sad and scary; sad because, and scary because it seems that things are not changing at the pace they should.movie_shade

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Some may have been wondering what I have been up to lately!
At the beginning of the year, I started working on the ALS-U project, which is the  upgrade of the Advanced Light Source, the main synchrotron at Lawrence Berkeley National Laboratory. The goal is to improve the facility with a Diffraction-Limited Storage Ring (DLSR), in order to increase the brilliance of the beam, so as allow scientists from all over the world to perform the most precise experiments, allowing bright and full coherent beams with diameters as small as 10 nanometer, or twice the width of a strand of DNA. (here’s a report on all the niceties you can do with such a tool: ALS-U: Solving Scientific Challenges with Coherent Soft X-Rays)

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SHARP & MET5 – EUV Lithography at Lawrence Berkeley National Laboratory

Over the past four years, I’ve been working on two of the EUV tools at the Center for X-Ray Optics, and while I’m moving to new projects, it’s time I give some explanations about what these two projects are about, the SHARP EUV microscope, and the 0.5NA Micro-Exposure Tool (MET5.)

A 6" EUV photomask

A 6″ EUV photomask

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I’ve run my first quantum computation!

Since I was working on the latest iteration of classical computer manufacturing techniques (EUV lithography), everyone asked me what were my thought on the future of Moore’s law, and what did I think about quantum computing. To the first question, I could mumble things about transistor size and the fact that we’re getting awfully close to the atomic size; to the latter question… I just had to go figure out myself!

Back in April, I’ve invited Irfan Siddiqi (, founding director of the brand new Center for Quantum Coherent Science, and his postdocs at Berkeley lab to give a talk to postdocs, and last the lab announced the first 45-qubits quantum simulations on the NERSC… things are going VERY fast! (read the Quantum manifesto)

Kevin O'Brien on multiplexing qubit readouts

Kevin O’Brien on multiplexing qubit readouts

This is thanks to Rigetti, a full-stack quantum computer startup based in Berkeley (Wired, IEEE Spectrum).

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Open Access

My article for the Berkeley Science Review on Open Access is out, and it is available here (for free, of course!): Science to the people.

“Astronomers and physicists have been sharing pre-prints since before the web existed,” says Alberto Pepe, founder of the authoring and pre-printing platform Authorea. “Pre-prints are an effective (and fully legal) way to make open access a reality in all scholarly fields.” Within hours, articles are available online, and scientists can interact with the author, leaving comments and feedback. Importantly, submission, storage, and access are all free. The pre-printing model ensures that an author’s work is visible and properly indexed by a number of tools, such as Google Scholar.

Special thanks to Rachael Samberg from thee UC Library and Alberto Pepe from Authorea.

Things seems to change quickly in that field, thanks to institutional efforts:

Here’s a list of resources that I’ve compiled from the talk by Laurence Bianchini from MyScienceWork when I invited at LBL, and a piece written by Nils Zimmerman on Open Access at LBNL: Open Access publishing at Berkeley Lab.