Category Archives: science life

How to promote diversity among scientists

This is a compendium of the things I’ve learned discussing the issue with other trained scientists and running an association with many young researchers. This is not meant to be a comprehensive list, but should help with the discussion. This discussion is primarily based on gender imbalance, but intersectionality applies (sometimes in weird ways), though the case is not as thoroughly documented.

(I wrote this initially for colleagues in my organization, since I couldn’t find a good resource. Here’s a bunch of additional resources – Ideas In Action – that have sprung out since. I’d be very grateful if you could point me to other concise lists)

*   *

The lab touts about the number of Nobel Prizes in spurned out, but it is remarkable, if not surprising that none of them are women (Nobel Prize in physics awarded to women (3) are rarer than total solar eclipses in California.) While there are many great women scientists at the lab who will eventually receive the coveted distinction, we must, given the historical significance of the lab and its stature, move forward, lead the way set some guidelines.


Personal safety

The first and most important step to promote diversity among scientists is to promote and enforce personal safety, first in terms of fighting against harassment (sexual or other), not only in physical safety terms and how women are depicted (sexist and lewd jokes, prejudiced opinions; see Tim Hunt’s comment on how “Women in labs ‘fall in love with you … you criticize them, they cry’”), but also in terms of financial safety, especially when some populations can experience hardship due to delayed entry into the (paid) workforce without external support (see also “parental leave” below.) It is also generally a good idea not to eschew virtue signaling when it can make a difference (a rainbow flag on a door can go a long way.)


Acknowledge your bias

A common objection from researchers is that they are not biased because, you know, they only judge with the data in hands, and that they can tell a good scientist from another using objective information – a resume, a publication list, a curriculum. The trouble here is that biased are ingrained in us (see Daniel Kahneman), which are somehow necessary for us to navigate a complex world (see Gigerenzer), and that even women have biases against women (see Walton). It is therefore important to acknowledge our own biases in order to try to compensate when needed (e.g. in hiring committees.)

In my experience, the mere fact of pointing at an implicit bias does wonder. People are often willing to help, but they don’t even realized there is a problem. It’s often good to point out to organizers of conferences or panels that they are actually manels when you see one.


The luster of meritocracy

The most common bias in science is the idea that no matter who you are, your application should be based purely on your academic records, and not on other factors… such as gender. While it seems logical at first glance, we know better and should acknowledge that these seemingly objective metrics must be understood in a more general social context (for example have learned that even artificial intelligence, supposedly fair and objective, is far from immune to bias, see Katie O’Neil.)


The need to fight for diversity

Some scientist would come and say, hey, yes there’s an imbalance, but maybe we should let it be (don’t force women into physics if they don’t like it.) The trouble is that the end result is deeply shaped by the system (Fig. 1) and it is important to act early, ideally at the PhD level by making sure that the contingent is not too skewed towards one gender.)


Figure 1. The making of inequality (from Paul Walton)


While in a democracy it should be obvious that laws should be made by a legislative body with balanced gender, one might notice that the representative democracy in America is not very representative (only 20% of US representatives are women, while roughly half of US constituents are indeed women), while there is no reason other than history to explain this imbalance.

Though science is not a democratic process (there is no expressed need for equal representation), similar historical factors are at play, and diversity or lack thereof can cancel any competitive advantage in terms of science (see Marie Hicks) and inclusivity of technology (see Caroline Criado Perez.)


Alleged differences

Some people will go as far as to say that women are actually undesirable in science, based on alleged differences in mental capacity (see Saini), or more subtly in the “variance” of the population (men supposedly show more variance, therefore more chances of fringe cases; see Strumia, Fig. 2)


Figure 2. Excerpt of the infamous Strumia’s talk at the workshop on gender diversity at CERN


Similar arguments from the Charles Murray’s racist book Bell Curve are used to promote borderline anti-semitic ideas – see also Jordan Peterson.

Ahh! intersectionality….


Role models

It is important in order to bring more balance to have role models to whom young scientist can identify, ideally more recent (and more diverse) than Albert Einstein or Marie Curie. The role models should be invited to give talks on site, but ideally *not in the context diversity* — it is important not to fall in the Bechdel test trap, since (i) you will lose a lot of speaker who are tired of having to repeat again and again the many hurdles they faced (ii) these interventions are usually not very interesting outside the TED talk format (iii) the point of role models is to inspire to do science because of it, not in spite of it (that’s what we have experimented with Series X).



Similar to role models but closer to the person, it is important to have mentors, that can come naturally or through some kind of pairing. These mentors can provide help and support, through sharing their personal experience, advices, and promoting their mentees through invitations to talks and workshops (that’s what we experimented with forum@MSD and  forum@ESA). Be careful that mentors should promote their mentee, not undermine them – the mentor should acknowledge the accomplishments of the mentee, not their own.


Provide a platform

Given the current imbalance in gender and diversity as whole, we must make sure that people from underrepresented groups get invited to the lab and are being able to leverage this position, through announcements and support. Success begets success, and the lab is a good reference when someone wants to get booked in other places. Given that diverse speaker are paradoxically more rare, a budget must be set aside to fly them here and/or for an honorarium (they should not work for free, especially when they are themselves not in a position of power.)

Scientists at the lab usually yield considerable power in their own field, and they should be encouraged to seek and promote diversity when they look for invited speakers (best practices and guidelines can be useful here, e.g. never have an all-male panel or seminar series.)


Promote scientists to leadership position

Academia is a very competitive environment, and any differentiating factor is useful when it comes to apply to position, especially when women face external factors that gradually push them outside academia (Fig. 1) Encouraging women to pursue ancillary activities (association, EAA or ERGs), where they can learn leadership skills and strengthen their network, is seen as important, and the lab should further its support to extra-curricular activities.


Work-life balance

Work-life balance is a vague concept that still has very real implications: while men do not face the discrimination related to their potential being pregnant, women do. The consequence of childbearing should be shared between the two parents, and initiatives such as (non-gendered) parental leaves are very useful to bridge the gap.

Some policies to make it more convenient to raise children can be implemented, such as policies against emails past 5pm during week-ends, or enforcing one day per week without meeting, to allow for telecommuting (parking at the lab is nearly impossible when an appointment to a doctor pushes your commute later in the day.)


Smash the patriarchy

Oftentimes I hear people (old white male) arguing about the current push for equality, dismissed as a PC coup and a threat to the freedom of expression, where they feel that *they* might become victims. This is baloney, and they should learn about the distinction between men (them) and patriarchy (the system), and not feel threatened – this is not about them, it is about all of us. They might have to change their habits coming from a position of power they rarely acknowledge, and learn to speak up when they see something wrong.

This applies to men… but also to women. I seen many times over



Paul Walton: Gender equal­ity in Aca­demia – what we have learnt

Angela Saini, Inferior: How Science Got Women Wrong

Daniel Kahneman, Thinking, fast and slow

Katie O’Neal, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Marie Hicks, Programmed Inequality: How Britain Discarded Women Technologists and Lost Its Edge in Computing

Nearly half of US female scientists leave full-time science after first child, Nature, 19 February 2019

Caroline Criado Perez, Invisible Women: Exposing Data Bias in a World Designed for Men

OSA and SPIE Professional Conduct Research Assessing And Addressing The Level Of Harassment At Scientific Meetings

(edit March 9th, 2020)

The researcher journey through a gender lens – Elsevier (March 2020)




Wikipedia is probably the best thing on Earth after sunsets, but it’s still far from perfect. Some articles are quite amazing, but oftentimes article about science topics or science personalities are nowhere near where they should be, and it seems that researchers should spend more time trying spread knowledge. Unfortunately, two things are in the way: the writers never get credit for it, and it’s bad optics in science to be the judge of notoriety for others.

It is amazing what you can accomplish if you do not care who gets the credit.
– Harry S Truman

Recently I became aware of an effort to improve the representation of scientists on Wikipedia, which is the go-to place to look up someone and evaluate their authority – in a world when men seems to preternaturally commend more than women. Let’s fix this!

Here’s a few people for who I have started a page (I’ll keep this list updated as I go – yes, I do take credit, on a page no one will ever read in hopes this may inspire some wandering soul.)

  1. Sophie Carenco (French Chemist)
  2. James Mickens (Computer Scientist, very witty)
  3. Carolyn Larabell (Biologist, UCSF; director of BCSB)
  4. Felicie Albert (High Power Laser, Livermore)
  5. Linda Horton (head of DOE Basic Energy Science, Material Science)
  6. Hope Ishii (University of Hawaii)
  7. Tabbetha Dobbins (Light Sources for Africa, Americas, Asia and the Middle East)
  8. Yves Petroff (synchrotron pioneer)
  9. Athena Sefat (Physicist, ORNL)
  10. Susan Celniker (Biologist, LBNL)
  11. David Veesler (Biologist, UW)
  12. Regina Soufli (Physicist, LLNL)
  13. Hatice Altug (Physicist, EPFL)
  14. Boubacar Kante (Physicist, UC Berkeley)
  15. Fadji Maina (Hydrologist, LBNL)
  16. Harriet Kung (Physicist, DOE)
  17. Elaine diMasi (Physicist, LBNL)
  18. Hélène Perrin (Physicist, Paris-Nord)
  19. Susan Celniker (Biologist, LBNL)
  20. Sakura Pascarelli (Physicist, EuXFEL)
  21. Regina Soufli (Physicist, LLNL)
  22. Pascal Elleaume (physicist, ESRF)
  23. Na Ji (Physicist, UC Berkeley)
  24. Anne Sakdinawat (Physicist, SLAC)
  25. David Attwood (Physicist, UC Berkeley)
  26. Sasa Bajt (Physicist, BESSY)
  27. Henry Chapman (Physicist, BESSY)
  28. Nathalie Picqué (Physicist, Max Planck Institute of Quantum Optics)
  29. Anne-Laure Dalibard (Physicist, Laboratoire Jacques-Louis Lions)
  30. Céline Guivarch (Climate scientist, CIRED)
  31. Irene Waldspurger (Mathematician, CEREMADE)
  32. Sandrine Leveque-Lefort (Physicist, CNRS)


  1. fr: Boubacar Kante
  2. fr: David Veesler
  3. fr: Fadji Maina
  4. fr: Ibrahim Cissé
  5. fr: Stéphane Bancel
  6. fr: Kizzmekia Corbett
  7. fr: Janelia Research Campus
  8. en: Centre for Nanosciences and Nanotechnologies

Scientific Topics:

People than need to be put on Wikipedia:
  • Daniela Ushizima –
  • Haimei Zheng –
  • Pascal Elleaume – synchroton radiation pioneer;
  • Bianca Jackson
  • Ashley White (AAAS Fellow, scientific communication)
  • Lady Idos (DEI Officer at Berkeley Lab)
  • Tara de Boer (CEO) –  BioAmp diagnostics
  • Chrysanthe Preza – Computational Imaging, University of Memphis
  • Teresa Williams (TechWomen/AAAS fellow) –
  • Tokiwa Smith  –
  •  –
update August 2019
I went to a workshop organized by SPIE and led by the very Jess Wade; it was quite useful.
Here’s what I learned:
  • Do not paraphrase bios found on other website –– but you somehow can. Better than nothing!
  • You can use pictures from governmental sources for illustration, it’s always ok to use them (copyrights)
  • You can help with translating pages to other languages.

Also, if you wonder what other people will think of you for doing the right thing, remember:


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…

Continue reading

Kolmogorov Access

Back in undergrad, I remember being fascinated by the notion of Kolmogorov complexity in computer science.

Put simply, the Kolmogorov complexity is the minimal length (number of lines) of the code needed to generate a signal, would it be a mathematical sequence (such as one listed in the OEIS) or an image, irrespective to the size needed to store it. It bears deep relations with the notion of entropy (a great book on the topic is Information Theory, Inference, and Learning Algorithms by the late David MacKay.)

For example, a series of eight billion ones in a row would require 1GB of memory, but can be written in a few lines of code:

for i in 1:1e9; print 1; end

(To some extent, this is why computer science is often problematic, since one of the goal of a good code is sometimes to reduce its Kolmogorov complexity, but the final code does not show all the lines that have been erased to get there…)

In the field of arts, culture and science, this description seems naive: can you really generate a book based on a script, or has it infinite entropy?

Science is organized knowledge. Wisdom is organized life.
– Immanuel Kant

In the age of the Internet, can we do better?

update 6/10/2019: I’ve seen recently on Twitter the embodiment of these ideas, see Nicole R.‘s thread. Way to go!

Continue reading


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!

Continue reading


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.
Continue reading

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

Continue reading

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

Continue reading