Interested in all things deep learning, biology, and molecular dynamics. @MSRNE #diversity & #representation matter (she/her)

Cambridge, MA
Joined April 2019
Deep sea gene editor is crazy
🧬🌊 New preprint from our lab: DO1 Cas9, the first Cas9 nuclease characterised from the deep ocean — recovered from a metagenome at 1,500 m in the Atlantic. ❄️Cold-adapted: it cuts DNA efficiently at low temperature, where mesophilic SpCas9 retains only residual activity. 🧵1/4
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Sarah Alamdari retweeted
Continuing with with early life origins, Weiss et al. analyzed 6.1 million genes and found 355 protein families likely present in the last common universal ancestor, suggesting it was anaerobic, CO2-fixing, H2-dependent, N2-fixing, and thermophilic: nature.com/articles/nmicrobi…
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Sarah Alamdari retweeted
🧬🌊 New preprint from our lab: DO1 Cas9, the first Cas9 nuclease characterised from the deep ocean — recovered from a metagenome at 1,500 m in the Atlantic. ❄️Cold-adapted: it cuts DNA efficiently at low temperature, where mesophilic SpCas9 retains only residual activity. 🧵1/4
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Sarah Alamdari retweeted
Can we design proteins that cut other proteins? Our de novo cysteine protease paper is out in Nature! We designed new folds that cleave peptide backbones, with activity demonstrated in mammalian cells. Paper and code 👇 nature.com/articles/s41586-0…
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Today we're sharing an update on our diffusion method from last year, RFdiffusion3, in @ScienceMagazine. Notably, we show significant improvement in design of sequence specific proteases for amyloid sequences and metallohydrolases for pesticide degradation.
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Sarah Alamdari retweeted
Today, RFdiffusion3 is out in @ScienceMagazine! science.org/doi/10.1126/scie… We have many exciting updates to share! We're also releasing Chaperone, an agent harness for general bio-molecular design. 1/n
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Calling graduate students 📣. Come work on AI x Bio @MSFTResearch 🎉! We are currently recruiting graduate student interns for summer 2027! Apply and come work with me on problems in regulatory genomics, epigenomics, biosecurity, and wearables!
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/…
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Sarah Alamdari retweeted
Yes they are!
Absolutely cracked team and opportunity to intern at!👇
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Absolutely cracked team and opportunity to intern at!👇
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/…
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Sarah Alamdari retweeted
Very fortunate to have contributed a bit to these two new Nature papers from the Baker lab! Especially exciting cool to see that MLIPs hold signal for this cool chemistry. Stay tuned for more! Metalloproteases: nature.com/articles/s41586-0… Cysteine Proteases: nature.com/articles/s41586-0…
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Sarah Alamdari retweeted
I had a chance to present at the tail end of the BioML Symposium this summer and was blown away by the work and progress of the students! Also a great opportunity to work with amazing mentors like @KevinKaichuang @SarahAlamdari @AshleyMaeConard and the rest of the BioML team. Highly recommend for PhD students nearing graduation who are interested in exploring industry research careers!
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/…
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Sarah Alamdari retweeted
The 2027/2028 NVIDIA Graduate Fellowship is open! PhD students can receive up to $60,000, plus a summer internship with NVIDIA. I want to especially encourage life science students to apply! Applications and recommendation letters are due October 30. research.nvidia.com/graduate…
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We're hiring research interns in machine learning, generative modeling, and their applications! Our research scope is broad, so if you're interested, feel free to reach out and tell us a bit about your research interests! Apply here: apply.careers.microsoft.com/…
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Sarah Alamdari retweeted
We are hiring research interns in the broad area of machine learning and statistics @MSRNE @MSFTResearch! Come work with us! Personally I have very broad interests and I’d love to learn about your favorite problems!
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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/…
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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/…
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Sarah Alamdari retweeted
For biomolecules, smaller is better: easier to synthesize, manufacture & deliver. 💊 The challenge is shrinking a molecule while retaining/improving what makes it work. 🤏 In our newly-accepted @NeurIPSConf paper, we introduce pCoMole to do this!! 🥑 📜: arxiv.org/abs/2610.01663 🤗: huggingface.co/ChatterjeeLab…
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Sarah Alamdari retweeted
Genomes are full of dark matter of unknown functions. We present Minerva, a new method for discovery guided by genome language models. Minerva reveals the interactions hidden in non-coding DNA, pointing to hundreds of new putative RNAs and repetitive elements per bacterial genome. Minerva allows us to find and study elements invisible to traditional methods at orders of magnitude greater scale than before.
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Sarah Alamdari retweeted
Can genomic language models design biological systems to access new regions of chemical space? Some of the most complex molecules in nature are made by biosynthetic gene clusters: massive enzymes that carry out complex chemistry. But engineering these systems is challenging due to complex cross-domain interactions. To solve this, we applied gLM2 to controllably generate new domain architectures by learning from vast evolutionary data.
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