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Ava Amini
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Ava Amini
@avapamini
principal researcher @MSFTResearch | AI for biomedicine | instructor @MITDeepLearning | alumna @MIT @Harvard
avaamini.com
Joined September 2014
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  • Pinned
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    Ava Amini
    @avapamini
    Jul 26, 2025
    thrilled to share The Dayhoff Atlas of protein language data and models 🚀 protein biology in the age of AI! aka.ms/dayhoff/prepri… we built + open source the largest natural protein dataset, w/ 3.3 billion seqs & a first-in-class dataset of structure-based synthetic proteins
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    Ava Amini
    @avapamini
    May 28, 2025
    How well do single-cell foundation models perform w/o finetuning? Our work @GenomeBiology shows that in zero-shot settings, scGPT and Geneformer often underperform traditional methods, raising questions about their utility for biological discovery. 📰 genomebiology.biomedcentral.com/articles/10.11…
    Single-cell foundation models perform worse than traditional approaches on the zero-shot cell clustering task, where colors represent cell types.
    26K
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    Ava Amini
    @avapamini
    Nov 7, 2024
    wet-lab validation of proteins designed by EvoDiff! - generated proteins express and are structured - inpainted, disordered mitochondrial targeting signals function in yeast - sequence-space motif scaffolding designs functional binders to Ca2+, MDM2 biorxiv.org/content/10.110…
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    Ava Amini
    @avapamini
    Sep 13, 2023
    presenting EvoDiff: new generative models for controllable protein design from sequence data alone! ✅generate high-quality proteins ✅scaffold functional motifs 🤩apply to therapeutic design + more! 👉biorxiv.org/content/10.110… 💻github.com/microsoft/evod… 🎙️microsoft.com/en-us/research…
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    Ava Amini
    @avapamini
    May 6, 2025
    awesome sharing my work at @MSFTResearch on AI x Biology during our @MITDeepLearning Introduction to Deep Learning course! 🧬💻 lecture: youtube.com/watch?v=SSzSOe…
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    MIT Intro to Deep Learning
    @MITDeepLearning
    May 5, 2025
    ⭐️⭐️ Lecture 10 of @MITDeepLearning 2025 is now available online #FREE for ALL! 🧬 Learn about the cutting edge of AI for Biology with @avapamini from @Microsoft @MSFTResearch! Tune in to learn how AI can learn the language of nature to help us understand and treat disease 🧪
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    Ava Amini
    @avapamini
    Sep 13, 2023
    presenting EvoDiff: new generative models for controllable protein design from sequence data alone! ✅generate high-quality proteins ✅scaffold functional motifs 🤩apply to therapeutic design + more! 👉biorxiv.org/content/10.110… 💻github.com/microsoft/evod… 🎙️microsoft.com/en-us/research…
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    Kevin K. Yang 楊凱筌
    @KevinKaichuang
    Sep 13, 2023
    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/sh… Code: github.com/microsoft/evod…
    85K
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    Ava Amini
    @avapamini
    Oct 28, 2024
    excited to share & release ProtNote! 🧬📰 ProtNote enables protein-function annotation on new, unseen functions by fusing LLM embeddings of text descriptions w/ protein sequence embeddings 📎biorxiv.org/content/10.110… 🖥️ github.com/microsoft/prot… led by the incredible @Samir_char
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    Ava Amini
    @avapamini
    May 22, 2025
    The final version of our work on enabling protein-function annotation on new, unseen functions with ProtNote is now published, with code and models available! 📰 doi.org/10.1093/bioinf… 💻 github.com/microsoft/prot… with @Samir_char, Nate Corley, @SarahAlamdari, @KevinKaichuang
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    Ava Amini
    @avapamini
    Oct 28, 2024
    excited to share & release ProtNote! 🧬📰 ProtNote enables protein-function annotation on new, unseen functions by fusing LLM embeddings of text descriptions w/ protein sequence embeddings 📎biorxiv.org/content/10.110… 🖥️ github.com/microsoft/prot… led by the incredible @Samir_char
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    Ava Amini
    @avapamini
    Mar 3, 2025
    presenting CleaveNet: an AI pipeline for the generative design of protease substrates We designed novel, selective substrates for MMPs by conditioning on an enzyme activity profile and validated them through a large-scale in vitro screen. for protease biology and beyond! 🚀
    CleaveNet, an end-to-end AI pipeline for the design of protease substrates
    14K
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    Ava Amini
    @avapamini
    Aug 25, 2025
    When it comes to deep learning for protein engineering, there is strength in simplicity. In a Preview piece @CellCellPress, we highlight work from @CaixiaGaoLab on using fixed-backbone sequence design to engineer genome editors with improved function. 📄 authors.elsevier.com/a/1leKGL7PXuFlF
    AI-informed constraints for protein engineering (AiCE), an approach that facilitates efficient protein evolution using generic protein inverse folding models
    10K
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    Ava Amini
    @avapamini
    Apr 1, 2020
    Our work on developing responsive nanoparticles that detect #LungCancer as a urinary readout is now published in @ScienceTM! We use #MachineLearning for accurate classification of lung cancer in mice. Paper 👉 bit.ly/2wMBFs6 MIT News 👉 bit.ly/3bN22Nn
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    Ava Amini
    @avapamini
    Sep 2, 2019
    Our work on developing a simple color-change urine test for detection of #cancer is now published in @NatureNano! A fantastic joint effort b/w @MIT and @imperialcollege with @cnloynachan! Paper 👉 rdcu.be/bP6Lx @snbhatia @JaideepDudani @kochinstitute @KI_Nanomedicine
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    Ava Amini
    @avapamini
    Oct 2, 2025
    Applications for @MSFTResearch undergrad research internships for rising juniors and seniors are due Monday Oct 6! Apply to work with us in BioML 👉 aka.ms/msr-ugrad w/ @KevinKaichuang, @alexijielu, @lorin_crawford, Kristen Severson, @ntenenz, @SarahAlamdari, and more!
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    Ava Amini
    @avapamini
    Dec 12, 2020
    Excited to present our work on guided drug discovery and prediction using evidential uncertainty! @NeurIPS2020 #ML4Molecules Spotlight talk 👉 slideslive.com/38942396 Poster 31 @ 4pm EST today 👉 neurips.gather.town/app/GAZQSaarzr… Drop by with questions or to discuss!
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    Ava Amini
    @avapamini
    Nov 13, 2025
    single-cell models tend to learn from the many - and miss the rare we introduce an Adaptive Resampling approach to help models learn from underrepresented cells, improving generalization & discovery biorxiv.org/content/10.110… github.com/microsoft/sc-AR great work by @NavidiZeinab!
    overview of the adaptive resampling approach
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