Intae Moon @IntaeMoon
Postdoc fellow at @HarvardDBMI | PhD at @MITEECS | @DanaFarber | Ex-Student researcher at @GoogleHealth | Research on machine learning and biomedicine itmoon7.github.io Cambridge, MA Joined March 2020-
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📢 🧬 New preprint! Can we predict which cancer patients will benefit, before treatment begins? @WanXiang_Shen Immunotherapy saves lives but many patients don’t respond to treatment, and we still lack reliable tools to predict who will benefit We introduce COMPASS, foundation…
Excited to share TxGNN, a model that identifies potential therapies from existing medicines for thousands of diseases. Trained across 17,080 diseases, TxGNN predicts drug candidates for conditions with limited or no treatment options, including rare diseases @NatureMedicine…
In case you missed it: @MarzyehGhassemi (@MIT_IMES,@MITEECS) and Alexander Gusev (@DanaFarber) comment on limiting bias in AI models for improved and equitable cancer care. Check it out: go.nature.com/3TdcndB
I am tremendously excited to announce that my work on 3D computational pathology has been published in @CellCellPress !! 🥰🎉 This has been truly a wonderful journey for the past few years. I really want to thank my wonderful mentors Professors @AI4Pathology and @jonliu123
I am tremendously excited to announce that my work on 3D computational pathology has been published in @CellCellPress !! 🥰🎉 This has been truly a wonderful journey for the past few years. I really want to thank my wonderful mentors Professors @AI4Pathology and @jonliu123
At the #AACR24 #PrecisionOncology #AI session? Check out this recent work led by @SashaGusevPosts describing a #MachineLearning approach for classifying cancer of unknown primary. nature.com/articles/s4159…
I'm hiring a postdoc at @UVA to edit and detoxify LLMs! This is an exciting opportunity to join a vibrant research community and to collaborate with @StevenLJohnson and @MaartenSap Feel free to get in touch and please help spread the word! tinyurl.com/yeywuu3b
Submit your work to the ICLR'24 Time Series for Health Workshop! Papers are due Feb 14 🎉
Submit your work to the ICLR'24 Time Series for Health Workshop! Papers are due Feb 14 🎉
Decades of hard work has gone into making commercial aviation shockingly safe. What lessons can we draw to plan ahead for safe and reliable deployments of AI for Health? Check out our new paper at #EAMMO2023!✈️ dl.acm.org/doi/10.1145/36…
Decades of hard work has gone into making commercial aviation shockingly safe. What lessons can we draw to plan ahead for safe and reliable deployments of AI for Health? Check out our new paper at #EAMMO2023!✈️ dl.acm.org/doi/10.1145/36…
Super excited to see our review paper on AI for computational pathology finally out!! We provide an extensive coverage of how AI has and will shape the field of pathology. Such a fun experience with my co-author @GuillaumeJaume, and @AI4Pathology Link: nature.com/articles/s4422…
Excited to present UNI - a general-purpose self-supervised visual model for #CPath pretrained using 100M+ images across 100K+ WSIs! Co-led with @TongDing99 @MYLu97 @DFKW_MD @AI4Pathology @harvardmed Summary: bit.ly/3EzEFr0 Preprint: arxiv.org/abs/2308.15474 1/
A significant challenge in cancer diagnosis in not knowing the primary site of origin in 3-5% of patients. That's changing with #AI, along with the ability to come up with an accurate prognosis nature.com/articles/s4159… @NatureMedicine @DanaFarber @IntaeMoon @MITEECS & colleagues
🚨 New work, now public 🚨 With Haoran Zhang, @tom_hartvigsen and @avapamini, I'm pleased to share: "Continuous Time Evidential Distributions for Irregular Time Series" which I'll present tomorrow at #ICML23🏝️ in the IMLH Workshop (sites.google.com/view/imlh2023/…) 🪡🧵
Excited to share MAMBA, a deep learning computational platform for 3D pathology analysis, validated on microcomputed tomography and open-top light-sheet microscopy 3D datasets! #3dpathology #computationalpathology Pre-print: arxiv.org/abs/2307.14907
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