Category Archives: science life


Lead by lidar

At the moment (April 2019), the economy is in a weird quantum superposition of doom (yield curve inversion) and exaltation, with Lyft recently joining the public markets (always loved the irony of the term…)

I’ve been talking to a few people involved in driverless cars and AI lately, asking them…. when? They usually tell me soon, the problem they have is that the main drawback with learned neural networks is that they are almost impossible to debug. You can try to get insight on how they work (see the fascinating Activation Atlas), but it’s really difficult to rewire them.

And it’s actually pretty easy to hack them — you can easily do some random addition to find noise that will activates the whole network. In the real world, you can put stickers at just the right location and… make every other car crash. Pretty scary…

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In other news I was recently promoted to a staff position at Lawrence Berkeley National Lab!

It’s still tenure track (hopefully the position will be fully secured in two years from now) , but I’m getting closer to my childhood dream: to become a savant.

I am extremely lucky to be there, working on a major US scientific infrastructure project (ALS-U), in a fantastic lab with my window literally overlooking the Silicon Valley.

The golden gate

Right now, I’m mostly doing beamline design and simulation, with some wavefront sensing and adaptive optics. Lots of very challenging topics (we have to deal with a laser-like beam hundred times smaller than a hair blasting kilowatts of light), and I’m learning a lot along the way. Amazing times!


Somehow my art piece has been accepted! A delightful play on the wavelike behavior of light, and the particle0like behavior of silicon atoms, in a tribute to Malevich. Instant Classic!

Incoherent on coherent

You can now see it at the Vision+Light exhibition on Berkeley Campus, from February 20th to March 14th, 2019

XFELs as non-kinetic weapons

A family tale goes that my grandfather, who was working in an ammunition factory near Chatellerault which became under German control during WWII. As he was ordered by the German command to paint the factory, he chose to buy buckets of red and white, and paint the factory with a beautiful bull’s eye ornament, what didn’t escape the British RAF notice. They were able destroyed the critical facility that fell into the hands of the invader…

I’m not sure how much of this story is true. I do not have reason to doubt it, however!

As we celebrate the hundredth anniversary of the first world war, I can only think of the many ways, under the promise of surgical interventions, making war has become worse. But maybe — maybe — if we can keep the enemy from dropping bombs on us, we can be safer.
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Hi there!

Preparing for the new generation of synchrotron light source, I’ve just started (Diffraction-Limited Storage Ring), and created relevant articles on Wikipedia (entries for (Diffraction-Limited Storage Ring  and Beijing’s High Energy Photon Source.)

The goal is to have platform to share knowledge and ideas in a format more flexible than conferences and papers (it takes inspiration from Rüdiger Paschotta’s momentous Encyclopedia of Laser Physics and Technology, though it does not aim to be as comprehensive!)

Let me know if you’re interested in contributing!

How to retrieve and handle x-ray data

A bit wonky, but here’s where you can get x-ray data, how to use it in python, and some common conversion.

Here are two important database to know:

CXRO database

NIST database

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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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