Neil Tenenholtz retweeted
I'm recruiting PhD interns for Summer 2027! If you’re excited about using generative protein language models to tackle real functional protein problems, please apply here!
Are you a PhD student interested in the intersection of machine learning and biology or healthcare?
Come do an internship with the BioML Group at MSR!
Make sure you submit a research statement!
apply.careers.microsoft.com/…
Neil Tenenholtz retweeted
If you're a PhD student interested in interning with me or one of my amazing colleagues at Microsoft Research New England (@MSRNE, @MSFTResearch) this summer, please apply here apply.careers.microsoft.com/…
Neil Tenenholtz retweeted
Are you a PhD student interested in the intersection of machine learning and biology or healthcare?
Come do an internship with the BioML Group at MSR!
Make sure you submit a research statement!
apply.careers.microsoft.com/…
Interested in training multimodal generative models for biology? How about developing methods and algorithms that recursively self-improve?
We’re hiring ML Engineers at @MSFTResearch to advance the frontier of what's possible.
You’ll be intellectually challenged, never bored, and hopefully have quite a bit of fun along the way!
I can’t make it to #COLM this year, but my DMs are open if you’re there (or elsewhere!) and interested in learning more.
Apply below! 👇
Link to Application: apply.careers.microsoft.com/…
Job Link: apply.careers.microsoft.com/…
Congrats @allisonkoe and @irenetrampoline !
Today, Schmidt Sciences is excited to announce 28 new AI2050 Fellows tackling the defining AI challenges of our time, from understanding its risks to expanding what it can help us discover.
schmidtsciences.org/ai2050-f…
Neil Tenenholtz retweeted
Next Tuesday, our MSR NE Generative Modeling and Sampling Seminar will feature @VesseronNina speaking about "Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures"!
Information to join below 👇
Neil Tenenholtz retweeted
After the *best* 3-year review/revise saga, the final version of EvoDiff is finally out at @eLife!
I'm still as excited about protein sequence models as ever, and extremely proud of the team that came together for this.
EvoDiff combines evolutionary-scale data with diffusion models for controllable protein sequence generation.
In addition to generating plausible proteins, we can scaffold structural motifs in sequence space!
Preprint: biorxiv.org/cgi/content/shor…
Code: github.com/microsoft/evodiff
Neil Tenenholtz retweeted
Can we tell whether data domains cooperate or compete during pretraining?
Adding code to the mix makes models better at math, while some other combinations hurt each other. We call this data synergy. Turns out you can incorporate data synergy into scaling laws and estimate it 🧵
Am very curious to see how they propose getting around the "ultrasound hates bone / air" problem and "frequency / resolution vs depth of penetration" tradeoff.
It's very easy to unintentionally learn the mean geometry -- it's highly common in the modality 1 -> modality 2 translation models, especially when the underlying physics of acquisition is different and the underlying signal is weak / not present.
The task may not be impossible, but it's definitely quite difficult.
Apparently, yesterday @midjourney pivoted from AI image generation to...whole body ultrasound , presumably AI-augmented. I spent some time trying to find hard data and did not come up with much beyond the video. Some thoughts based on the X reactions today.
1) "Nobody's ever done this before." This seems to be a variant on ultrasound tomography, with Butterfly sensors arranged in rings. Ultrasound tomography is not new, with commercial systems available for breast imaging. The system proposed here seems very similar to Garrett et al 2024 (pmc.ncbi.nlm.nih.gov/article…) which provided fuzzy images of the abdomen and extremity. By collating sound wave return from the ring array the system can attempt to minimize artifact from bone and gas. So, we have known this is possible since 2024.
2) "So easy for the patient." The system requires the patient to submerge themselves in water. For commercial breast UT, this simply requires laying on top of a shaped tank, but not going fully in water. As some people have difficulty lying on a DEXA or CT scanner (the latter of which can image in seconds) I will be interested to see how this is received by patients.
3) "This will revolutionize medical imaging." Ultrasound is limited by bone, air (lung, stomach, bowel) and depth of penetration. In the abdomen, this system can "see" around bowel and spine by adding together the full set of images. There is no workaround for the head or lungs and you'll notice that's why they didn't offer any pictures of those areas. It also means it won't be great at screening inside the stomach, intestine, or colon. I also note the volunteers appear fairly skinny. Ultrasound is always more limited in heavier patients.
4) "But AI can fix it!"...not really. Current DL-based reconstruction techniques require at least some undersampling of a region in order to reconstruct the image. You'll get a lovely picture of the outside of the skull. I can AI-upsample a fuzzy photograph but that doesn't mean what comes out of it actually existed. We have a variant of this issue already with MRI DL reconstruction.
5) "But this is better than MRI in the 1970s!" Yes, true. But the competition is not 1970s MRI, it's modern CT/MRI/US and most especially low-field MRI. For brain, for example, low-field MRI is already diagnostic quality and doesn't need shielding. A low-field scanner costs 50k and can be used in an ICU or put in a van. Why would I send a patient to get an experimental full body US when there's whole body MRI available that's already diagnostic quality? science.org/doi/abs/10.1126/…
6) "The FDA has no idea how to regulate this." This one made me laugh. There's an entire set pathway for this. Commercial ultrasound tomography already exists as an easy reference of a similar technology in the application. If they haven't submitted to the FDA, it's because they plan to try at a later date, or because they're not planning on submitting it at all.
Do I think this is new and exciting? Yes. It looks like it's going to be great for body composition, and I do think there will be improvement in the future.
Is it currently medical-grade diagnostic quality? No, not based on what they showed us. Apparently in-person there was a great hand demo. I don't see why that would be an improvement over routine US or MRI in visualizing hand soft tissues.
To quote @khakrish: "It feels like all the same problems as full body MRI with the added problem of an unproven imaging modality and no FDA clearance."
The MSR New England Generative Modeling and Sampling Summer Workshop is HERE! We feature a stellar list of speakers in the beautiful New England on August 10-11th!
Seats limited, sign-up early, contribute a talk or present a poster!
🤖 Made with AI
Neil Tenenholtz retweeted
If you are interested in methods or applications of generative modeling or sampling, sign up for our summer workshop in Cambridge, MA!
Neil Tenenholtz retweeted
MAI is a really cool team of kind, highly motivated and skilled people.
Our team worked with them in the final stretch of this model contributing some of our swe 🧙♀️
proud of our Froggy team 🐸 and expect further cool updates from us...
Neil Tenenholtz retweeted
Incredible work by the MAI team, building a model **without any distillation**.
Also shoutout to the Froggy Team 🐸 for their part in the RL data env creation. Go BugPilot!
👀
The Hallmarks of Cancer brought clarity through abstraction - but cancer is more complex than we can reduce.
honored to build on their legacy in our @CellCellPress perspective on generative models as unified, multimodal systems for cancer discovery
📄 msft.it/6013QhkUS !
Neil Tenenholtz retweeted
The Hallmarks of Cancer brought clarity through abstraction - but cancer is more complex than we can reduce.
honored to build on their legacy in our @CellCellPress perspective on generative models as unified, multimodal systems for cancer discovery
📄 msft.it/6013QhkUS !
Neil Tenenholtz retweeted
protein language models capture rich structural signals, but where that knowledge lives in the network is still unclear
we show that small subnetworks inside PLMs encode structural concepts, from residues to folds
journals.plos.org/ploscompbi… @PLOSCompBiol
work led by @riavinod_!
Neil Tenenholtz retweeted
(1/9) Most LM fine-tuning optimizes next-token loss or scalar rewards.
What if we fine-tune language models so that feature statistics of partial rollouts match those of ground-truth completions?
That leads to Energy-Based Fine-Tuning (EBFT).
arXiv: arxiv.org/abs/2603.12248























