Category Archives: language

Ladder of causation

I’ve read an interesting piece on Twitter from the always excellent Kareem Carr on the ladder of causation. I found it very interesting, because it allows you to go beyond the mantra “corelation is not causation“, and links statistics to the concept of falsifiability that Thomas Kuhn puts as central to sciences.

The Ladder of Causation

The Ladder of Causation has three levels:

1. Association. This involves the prediction of outcomes as a passive observer of a system.

2. Intervention. This involves the prediction of the consequences of taking actions to alter the behavior of a system.

3. Counterfactuals. This involves prediction of the consequences of taking actions to alter the behavior of a system had circumstances been different.

I even read the book from which – “The Book of Why” [Full book on the Internet Archive] by Judea Pearl, a Turing prize recipient who worked on Bayesian network. The book quite illuminating, mentioning a bit too often  dark figures such as Galton, Pearson and Fisher (it seems statistician get really high on their own supply.)

This certainly begs the question – “Why not?”

Continue reading

On Mentorship

This last month, I received two awards related to mentorship from Berkeley Lab. They both came as a surprise, since I consider myself more a student of mentorship than someone who has something to show for.

Berkeley Lab Outstanding Mentorship Award

Director’s award for For building the critical foundations of a complex mentoring ecosystem

I began to be interested in mentorship after I realized that mentorship plays a large role in the success of young scientist, (1) having experience myself the difference between having no mentorship and having appropriate mentorship (I’ll be forever grateful to my mentor/colleague/supervisor Ken Goldberg), (2) having had tepid internship supervision experience due to the lack of guidance, (3) realizing that academia is ill-equipped to provide the resources necessary for success.

While I was running Berkeley Lab Series X, I always asked the speakers (typically Nobel prize laureates, stellar scientists and directors of prominent research institutions) how they learned to manage a group, and they answer was generally: “on the spot, via trial and error”, what struck me as awfully wrong. If people don’t get the proper resources/training, many are likely to fail, and drag their own group down the abyss. In this post, I will try to share resources I gathered along the years, and what I learned about mentorship, and provide some resources I found useful. This is more descriptive of my experience than prescriptive, but I hope you find this useful.

Continue reading

Dr. and Drs.

Last week, my partner and I got married, and we both decided to take the same surname.

I will now be known as Antoine İşlegen-Wojdyla – edit your contact info!

Golden Gate Bridge (credit: Jake Ricker)

Continue reading


It’s been a few months since the ChatGPT craze started, and we’re finally seeing some interesting courses and guidelines, particularly for coding, where I found the whole thing quite impressive.

Ad hoc use of LLaMa

Here’s a few that can be of interest, potentially growing over time (this is mostly a notes to self.)

Plus – things are getting really crazy: Large language models encode clinical knowledge (Nature, Google Research.)


Party lk- it’s 99

I leave Twitter for a few month, and the science world is all upside down!

The superconductivity community was simmering, with the news that a new compound name LK-99 may be superconducting at room temperature. Eventually, things quenched abruptly, but not without an interesting foray on how science works nowadays, some good takes and a decent media coverage.

I first learn about it when I read an article in Ars Technica “What’s going on with the reports of a room-temperature superconductor?” where I saw the name of my friend Sinead popping up. She was in the spotlight because she had run some very complicated simulations to determine whether LK-99 could be a candidate for superconductivity, and found that the material has indeed some interesting features – volume collapse and flat bands – the latter being a common feature of superconductors.

Alas, it seems that the results from the initial paper failed to be reproduced by other teams, who in passing found some interesting properties for this class of material. Inna Vishik, who was running the ALS UEC Seminar Series: Science Enabled by ALS-U with me, summarized it well:

“The detective work that wraps up all of the pieces of the original observation — I think that’s really fantastic,” she says. “And it’s relatively rare.”

LK-99 isn’t a superconductor — how science sleuths solved the mystery – Nature

Updates on AI for big science

There’s a lot of things happening on the front of AI for Big Science (AI for large scale facilities, such as synchrotrons.)

The recently published DOE report in AI for Science, Energy, and Security Report provides interesting insights, and a much-needed update to the AI for Science Report of 2020.

Computing Facilities are upgrading to provide scientists the tools to engage with the latest advances in machine learning. I recently visited NERSC’s Perlmutter supercomputer, and it is LOADED with GPU for AI training.

A rack of Tesla A100 from the Perlmutter supercomputer at NERSC/Berkeley Lab

Meanwhile, companies with large computing capabilities are making interesting forays in using AI for science, for instance Meta, which is developing OpenCatalyst in collaboration with Carnegie-Mellon University, where the goal is to create AI models to speed up the study of catalysts, which are generally very computer-intensive (see the Berkeley Lab Materials Project.) Now the cool part is to verify these results using x-ray diffraction at a synchrotron facilities. Something a little similar happened with AlphaFold where newly derived structure may need to be tested with x-rays at the Advanced Light Source: Deep-Learning AI Program Accurately Predicts Key Rotavirus Protein Fold (ALS News)

Continue reading

Institutional Open Data

Things are moving in terms of Open Data! The Department of Energy has just released an update to it Public Access Plan (initially published in 2014), and embracing the use of persistent identifiers for papers and data, to promote the FAIR principles (Findability, Accessibility, Interoperability, and Reusability of data and metadata.)

Mariposa Lillies, from Alexis Madrigal of the Oakland Garden Club

And let me insist on the last bit:

Data without metadata is mostly useless

At the time where Twitter was a nice place to share thoughts and disseminate bite-sized knowledge, I thought the Twitter posts/URL were something akin to Digital Object Identifiers – you could post an image with caption, and share the link on your blog or with anyone (now Twitter doesn’t allow to share those so easily.) Zenodo allows you to creat actual DOI for your data (data will include your ORCID and metadata.), albeit not as user-friendly – and to some extent, github works the same way (the visualization and graphical content is not the best)

At Berkeley Lab, the Office of Research Compliance has updated its guideline, providing excellent resources to build a Data Management Plan.

Out Of Many

Last week I was lucky to meet with Vanessa Chan, the Chief Commercialization Officer for the Department of Energy and Director of the Office of Technology Transitions. She wanted to hear what kind of hurdles when it comes to start a company (hint: a lot.) I told her that a major, overlooked issue is that you generally to be a permanent resident to start at company in the US, whereas two-thirds of postdocs are foreign nationals and on visas. There are ways to get around the requirement (such as Unshackled), but it’s a little sad not more is done to provide support to those willing and able (plus – it is a well-known trope that many US companies are founded by foreign nationals, what I tend to believe is among what sets California apart from other states and other countries, where entrepreneurship doesn’t flourish as much as expected despite many efforts)

Conversation with Vanessa Chan

Resources on writing and presenting

Scientist lean many things while they study their subject, but never formally learn how to make a good presentation. Here’s a few resources that can be helpful to get started.

Making a presentation to an audience is important to get your ideas through, and while communication is a basic human trait, communicating effectively requires some thoughts (TED talk speakers go through a thorough training to get their point across.)

Don’t trust Tufte, go for gold

Someone who has a good resource is Jean-luc Doumont – he’s a regular speaker at the lab and Stanford, and a contributor to Nature and other publications. Here’s a few resources available online:
(maybe this video at 1.5x speed is a good start:
Another interesting speaker is Peter Fiske, who also made a few presentations at the lab; I’m enclosing his slides from some time ago (another interesting but unrelated talk by him is
Careers In Physics Workshop: Putting Your Science to WORK, about career perspectives, networking etc.)
In general, the workshops for postdocs are really good, and should be attended:

Here are a few things learned, for the use of postdocs where one can easily drown everyone else. These are based on my experience, but there are many resources around the web to draw from.

Some practical presentation rules 

(These are stretch goals; I rarely follow these rules myself, but they are useful if you don’t know where to start.)

Use 16:9 format (these days most presentations are online, and most screen are wide)

Start with an outline.

Often, using the title to summarize the main point of the slide is a good use of the title.

No text should be smaller than 18pts.

Use animations sparingly, to expose your thoughts point by point. Avoid fancy animation or transition between slides

No more than one slide per minute (if there’s more, you can probably merge a few points)

Continue reading

Two ways to say things

I am a native French speaker, and I have always been confused by the ubiquity of English, language which is actually quite difficult to speak (why is tough, though, thought and enough so different?) And I was also puzzled the difference between liberty and freedom – no one could ever explain me the difference, even though “Freedom” is probable the most overused concept in American society (French has “Liberté” in its national motto, but is has nothing to do with “free” as in “free sample.”)

Finally, I found an interesting explanation by Jorge Luis Borges, who sees this as a feature, not a bug:

I have done most of my reading in English. I find English a far finer language than Spanish.

Firstly, English is both a Germanic and a Latin language. Those two registers—for any idea you take, you have two words. Those words will not mean exactly the same. For example if I say “regal” that is not exactly the same thing as saying “kingly.” Or if I say “fraternal” that is not the same as saying “brotherly.” Or “dark” and “obscure.” Those words are different. It would make all the difference—speaking for example—the Holy Spirit, it would make all the difference in the world in a poem if I wrote about the Holy Spirit or I wrote the Holy Ghost, since “ghost” is a fine, dark Saxon word, but “spirit” is a light Latin word. Then there is another reason.

The reason is that I think that, of all languages, English is the most physical of all languages. You can, for example, say “He loomed over.” You can’t very well say that in Spanish.

And then you have, in English, you can do almost anything with verbs and prepositions. For example, to “laugh off,” to “dream away.” Those things can’t be said in Spanish. To “live down” something, to “live up to” something—you can’t say those things in Spanish. They can’t be said. Or really in any Roman language.

(thanks Jordan Poss for the transcription!)

I really enjoy this notion of physicality – onomatopoeia are a vibrant part of the language: whisper, gulp, slam, rumble, slushy, etc.

Wovon man nicht sprechen kann, darüber muss man schweigen.
Whereof one cannot speak, thereof one must be silent.
– Ludwig Wittgenstein

Let’s all clap for Borges!