
Researcher @Microsoft Research, PhD @Mines_Paris. Alumni @ENS_ULM. Computer vision - histopathology.
Paris, France
Joined April 2011
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Trislaz retweeted
Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback, helping researchers sharpen future research directions. msft.it/6014acaRn
Trislaz retweeted
We will soon be hiring postdocs, engineers and researchers (on site in Heidelberg, DE) in:
- Biomedical AI (multimodal foundation models, biomarkers, agents)
- Robotics (lab automation, physical action surfaces for agents)
- AI social sciences (multi-agent systems, agent "civilizations")
- Deploying AI in a large hospital system
More info at imai-heidelberg.de and kather.ai
If you are interested in learning more, reach out to [email protected]
Trislaz retweeted
As many other oncologists will also attest, we were taught this was a dead end
It was, dogmatically, never going to work — kras was too much of a “greasy ball” to be targeted
And yet here we are, with truly meaningful survival curves👇
Inspiration on multiple levels #ASCO26
Trislaz retweeted
Biological networks too :)
Here is the neural geometry of mice navigating a figure-8 maze.
Trislaz retweeted
My course on consciousness is now online: six 90´ lectures featuring both history and recent experimental results on ignition, P3b, working memory, neural manifolds… unpublished results too.
tinyurl.com/cz9yb4z3
Wow..!
We at @noetik_ai are thrilled to introduce the *first world model of spatial, multimodal cancer biology*:
Oncology Counterfactual Therapeutics Oracle (OCTO)
Technical report below + 🧶 on how we trained OCTO directly on patient data and use it to find new cancer drug targets!
Trislaz retweeted
The boundary between trainable and untrainable neural network hyperparameter configurations is *fractal*! And beautiful!
Here is a grid search over a different pair of hyperparameters -- this time learning rate and the mean of the parameter initialization distribution.
Trislaz retweeted
Still blows my mind that you can take ANY MIT COURSE you want, online, for free.
Machine learning? Nuclear physics? Quantum computing? All there.
Truly unreal.
Trislaz retweeted
An honor to be external PhD examiner for #AI pathology superstar Tristan Lazard, supervised by @ThomasW04410315 at @Mines_Paris @psl_univ. Love the ambiente 🇪🇺🇫🇷🍷 & the discussions about self-supervised learning, foundation models & biomarkers 👉linkinghub.elsevier.com/retr…
Trislaz retweeted
V1.0!
Free phone-formatted pdf:
fleuret.org/public/lbdl.pdf
Pocket book:
fleuret.org/lbdl-order
Note that given an other recent prototype everything should be okay with the pocket book version, but I have not *yet* checked this one (it takes ~2 weeks to get it)
Trislaz retweeted
This is absolutely bananas.
There is NOTHING like this on the instrumental record. Its likely impact is probably immeasurable.
But is it leading our news bulletins & on newspaper front pages?
Nope.
Trislaz retweeted
I am looking for a postdoc in Computer Vision.
We have several exciting projects in the field of Digital Pathology, where we aim at predicting treatment response from Whole Slide Images (WSI).
WSI are huge and complex, containing hundreds of thousands of individual cells.
Trislaz retweeted
🥇🥉🥉Elle l’a fait ! Après avoir terminé cinq fois sur la deuxième marche du podium en coupe du monde, Oriane Bertone remporte sa première victoire à Prague. Flavy Cohaut et Mejdi Schalck décrochent le bronze !
👉 bit.ly/3oRhvrF
📷 Jan Virt - IFSC
Trislaz retweeted
ÅNGSTRÖM-RESOLUTION FLUORESCENCE MICROSCOPY. Can we do structural biology with an optical microscope? Yes!
Check out how we resolved *single bases of DNA* and much more using fluorescence microscopy, out today in @Nature!
nature.com/articles/s41586-0…
@JungmannLab @MPI_Biochem
Trislaz retweeted
🙂Au Collège de France, les #enseignements sont #gratuits & libres d’accès.
🙂Certains le savent, d’autres l’ignorent.
🙂Que ceux qui le savent partagent ce tweet avec ceux qui l’ignorent 😉
college-de-france.fr
Trislaz retweeted
Very happy to share our latest preprint 🎉 "Fully transformer-based biomarker prediction from #colorectal #cancer histology". This is a massive collaborative study in >9000 patients which is led by @sophiajwagner: arxiv.org/abs/2301.09617 #AI (1/4)
Trislaz retweeted
Shoutout to the legend who answers literally every single PyTorch question out there!
Trislaz retweeted
Happy to share our latest publication in @NatureMedicine 🩺🧬🔬 nature.com/articles/s41591-0…. We designed a multistain deep learning model and trained, validated, and tested it on imaging data of different immune cell subtypes of over 1000 patients with #colorectal #cancer. Our …
Trislaz retweeted
Reinforcement learning from human feedback (RLHF) can teach LLMs a variety of interesting skills. As an example, Sparrow, a chatbot developed by @DeepMind, is taught (via RLHF) to support its factual claims by finding relevant information on Google... 🧵 [1/7]









































