From 8f5c46af9ac1835d999ddb8e1f8c052572b7955b Mon Sep 17 00:00:00 2001 From: marinkaz Date: Fri, 22 Sep 2023 15:12:05 -0400 Subject: [PATCH] Update news posts --- .../09/22/NewPapersNeurIPS}/index.html | 68 +++---- 2023/10/24/GraphAIMedicine/index.html | 183 ++++++++++++++++++ 2023/10/24/SBDD/index.html | 183 ++++++++++++++++++ DMAI/index.html | 176 +++++++++-------- data/index.html | 176 +++++++++-------- feed.xml | 2 +- index.html | 176 +++++++++-------- jobs/index.html | 176 +++++++++-------- meetings/index.html | 176 +++++++++-------- news/index.html | 174 +++++++++-------- news/page2/index.html | 178 ++++++++--------- news/page3/index.html | 94 +++++++++ people/index.html | 180 ++++++++--------- postdoc-ML/index.html | 176 +++++++++-------- postdoc-biomedicalAI-MGB/index.html | 176 +++++++++-------- postdoc-biomedicalAI/index.html | 176 +++++++++-------- postdoc-cancerTxAI/index.html | 176 +++++++++-------- products/ada_fang/index.html | 4 +- products/ayush_noori/index.html | 4 +- products/chirag_agarwal/index.html | 4 +- products/diego_trujillo/index.html | 4 +- products/george_dasoulas/index.html | 4 +- products/guadalupe_gonzalez/index.html | 4 +- products/ivy_liang/index.html | 4 +- products/jonathan_schwarz/index.html | 4 +- products/joshua_pan/index.html | 4 +- products/marinka_zitnik/index.html | 4 +- products/michelle_dai/index.html | 4 +- products/michelle_li/index.html | 4 +- products/owen_queen/index.html | 4 +- products/ruth_johnson/index.html | 4 +- products/shanghua_gao/index.html | 4 +- products/tianlong_chen/index.html | 4 +- products/tom_cobley/index.html | 4 +- products/valentina_giunchiglia/index.html | 4 +- products/varun_ullanat/index.html | 4 +- products/wanxiang_shen/index.html | 4 +- products/xiang_zhang/index.html | 4 +- products/yasha_ektefaie/index.html | 4 +- products/yepeng_huang/index.html | 4 +- products/zaixi_zhang/index.html | 4 +- projects/G-Meta/index.html | 176 +++++++++-------- projects/GNNDelete/index.html | 176 +++++++++-------- projects/GNNGuard/index.html | 176 +++++++++-------- projects/GraphXAI/index.html | 176 +++++++++-------- projects/Milieu/index.html | 176 +++++++++-------- projects/NIFTY/index.html | 176 +++++++++-------- projects/PINNACLE/index.html | 176 +++++++++-------- projects/PrimeKG/index.html | 176 +++++++++-------- projects/REMAP/index.html | 176 +++++++++-------- projects/Raincoat/index.html | 176 +++++++++-------- projects/Raindrop/index.html | 176 +++++++++-------- projects/SHEPHERD/index.html | 176 +++++++++-------- projects/SIPT/index.html | 176 +++++++++-------- projects/SubGNN/index.html | 176 +++++++++-------- projects/TF-C/index.html | 176 +++++++++-------- projects/TimeX/index.html | 180 ++++++++--------- projects/TxGNN/index.html | 176 +++++++++-------- projects/metapaths/index.html | 176 +++++++++-------- projects/patient-safety/index.html | 176 +++++++++-------- publications/index.html | 176 +++++++++-------- publications/thumbnails/FAIR23.png | Bin 0 -> 10157 bytes publications/thumbnails/GraphAIMedicine23.png | Bin 0 -> 11333 bytes publications/thumbnails/SBDD23.png | Bin 0 -> 11311 bytes pubs.json | 86 ++++++-- sitemap.xml | 60 +++--- software/index.html | 176 +++++++++-------- talks/index.html | 8 +- 68 files changed, 3617 insertions(+), 2979 deletions(-) rename {products/huan_he => 2023/09/22/NewPapersNeurIPS}/index.html (71%) create mode 100644 2023/10/24/GraphAIMedicine/index.html create mode 100644 2023/10/24/SBDD/index.html create mode 100644 publications/thumbnails/FAIR23.png create mode 100644 publications/thumbnails/GraphAIMedicine23.png create mode 100644 publications/thumbnails/SBDD23.png diff --git a/products/huan_he/index.html b/2023/09/22/NewPapersNeurIPS/index.html similarity index 71% rename from products/huan_he/index.html rename to 2023/09/22/NewPapersNeurIPS/index.html index 01bfda70..5e1e6a35 100644 --- a/products/huan_he/index.html +++ b/2023/09/22/NewPapersNeurIPS/index.html @@ -4,33 +4,31 @@ - <a href="https://hehuannb.github.io">Huan He</a> - Zitnik Lab + New papers accepted at NeurIPS - Zitnik Lab -Huan He | Zitnik Lab +New papers accepted at NeurIPS | Zitnik Lab - + - - - - + + + + - - + - - + +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/2023/09/22/NewPapersNeurIPS/"},"url":"https://zitniklab.hms.harvard.edu/2023/09/22/NewPapersNeurIPS/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"New papers accepted at NeurIPS","description":"Geometric deep learning, protein design, explainability, self-supervised learning","dateModified":"2023-09-22T00:00:00-04:00","datePublished":"2023-09-22T00:00:00-04:00","@type":"BlogPosting","@context":"https://schema.org"}
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Graph AI in Medicine

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Published: Oct 24, 2023

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Zitnik Lab +  ·  Artificial Intelligence in Medicine and Science +  ·  Harvard +  ·  Department of Biomedical Informatics

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Structure-Based Drug Design

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Published: Oct 24, 2023

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Zitnik Lab +  ·  Artificial Intelligence in Medicine and Science +  ·  Harvard +  ·  Department of Biomedical Informatics

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Oct 2023:   Structure-Based Drug Design

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Oct 2023:   Graph AI in Medicine

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Sep 2023:   New papers accepted at NeurIPS

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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   PrimeKG published in Scientific Data

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Oct 2023:   Structure-Based Drug Design

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Sep 2023:   New papers accepted at NeurIPS

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diff --git a/feed.xml b/feed.xml index 161b821a..c0613301 100644 --- a/feed.xml +++ b/feed.xml @@ -1 +1 @@ -Jekyll2023-09-18T20:28:41-04:00https://zitniklab.hms.harvard.edu/feed.xmlZitnik LabHarvard Machine Learning for Medicine and ScienceMarinka ZitnikFuture Directions in Network Biology2023-09-17T00:00:00-04:002023-09-17T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/09/17/Future-NetBio<p>Excited to share our perspectives on <a href="https://arxiv.org/abs/2309.08478">current and future directions in network biology.</a></p>Marinka ZitnikExcited to share our perspectives on current and future directions in network biology.Scientific Discovery in the Age of AI2023-08-02T00:00:00-04:002023-08-02T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/08/02/AI4Science-Nature<p>New paper on the role of <a href="https://rdcu.be/dinBA">artificial intelligence in scientific discovery is published in Nature.</a></p>Marinka ZitnikNew paper on the role of artificial intelligence in scientific discovery is published in Nature.PINNACLE - Contextual AI protein model2023-07-19T00:00:00-04:002023-07-19T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/07/19/PINNACLE<p><a href="https://www.biorxiv.org/content/10.1101/2023.07.18.549602">PINNACLE is a contextual AI model for protein understanding</a> that dynamically adjusts its outputs based on biological contexts in which it operates. <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p>Marinka ZitnikPINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.Our Group is Joining the Kempner Institute2023-06-21T00:00:00-04:002023-06-21T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/06/21/Kempner<p>Excited to join <a href="https://news.harvard.edu/gazette/story/newsplus/kempner-institute-announces-appointment-of-four-new-associate-faculty/">Kempner’s inaugural cohort of associate faculty</a> to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.</p>Marinka ZitnikExcited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.Welcoming a New Postdoctoral Fellow2023-06-11T00:00:00-04:002023-06-11T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/06/11/NewPostdoc<p>An enthusiastic welcome to <a href="https://scholar.google.com/citations?user=zW32dXsAAAAJ">Shanghua Gao</a> who is joining our group as a postdoctoral research fellow.</p>Marinka ZitnikAn enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.On Pretraining in Nature Machine Intelligence2023-06-01T00:00:00-04:002023-06-01T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/06/01/Pretraining-NatMachIntelligence<p>Excited to share our new study on <a href="https://www.nature.com/articles/s42256-023-00647-z">language model pretraining and general-purpose methods for biological sequences.</a> <a href="https://zitniklab.hms.harvard.edu/projects/SIPT/">Project website.</a></p>Marinka ZitnikExcited to share our new study on language model pretraining and general-purpose methods for biological sequences. Project website.Congratulations to Ada and Michelle2023-05-15T00:00:00-04:002023-05-15T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/05/15/CongratsMichelleAda<p>Congrats to PhD student Michelle on being selected as the <a href="http://www.albertjryanfoundation.org/fellows.cfm">2023 Albert J. Ryan Fellow</a> and also to participate in the <a href="https://www.heidelberg-laureate-forum.org/">Heidelberg Laureate Forum</a>. Congratulations to PhD student Ada for being selected as the <a href="https://www.harvard.edu/kempner-institute">Kempner Institute Graduate Fellow</a>!</p>Marinka ZitnikCongrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!Universal Domain Adaptation at ICML 20232023-04-24T00:00:00-04:002023-04-24T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/04/24/RaincoatICML23<p>New paper introducing <a href="https://arxiv.org/abs/2302.03133">the first model for closed-set and universal domain adaptation on time series</a> accepted at <a href="https://icml.cc/Conferences/2023">ICML 2023</a>. Raincoat addresses feature and label shifts and can detect private labels. <a href="https://zitniklab.hms.harvard.edu/projects/Raincoat/">Project website.</a></p>Marinka ZitnikNew paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.Celebrating Achievements of Our Undergrads2023-04-13T00:00:00-04:002023-04-13T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/04/13/Undergrads<p>Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological &amp; Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.</p>Marinka ZitnikUndergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological &amp; Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.Welcoming a New Postdoctoral Fellow2023-04-13T00:00:00-04:002023-04-13T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/04/13/NewPostdoc<p>An enthusiastic welcome to <a href="https://scholar.google.com/citations?user=LE3ctn0AAAAJ&amp;hl=en">Tianlong Chen</a>, our newly appointed postdoctoral fellow.</p>Marinka ZitnikAn enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow. \ No newline at end of file +Jekyll2023-10-24T23:25:05-04:00https://zitniklab.hms.harvard.edu/feed.xmlZitnik LabHarvard Machine Learning for Medicine and ScienceMarinka ZitnikStructure-Based Drug Design2023-10-24T00:00:00-04:002023-10-24T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/10/24/SBDD<p><a href="https://arxiv.org/abs/2306.11768">Geometric deep learning has emerged as a valuable tool</a> for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.</p>Marinka ZitnikGeometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.Graph AI in Medicine2023-10-24T00:00:00-04:002023-10-24T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/10/24/GraphAIMedicine<p><a href="https://arxiv.org/abs/2310.13767">Graph AI models in medicine</a> integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.</p>Marinka ZitnikGraph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.New papers accepted at NeurIPS2023-09-22T00:00:00-04:002023-09-22T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/09/22/NewPapersNeurIPS<p>Congratulations to <a href="https://zitniklab.hms.harvard.edu/people/">Owen and Zaixi</a> for having their papers accepted <a href="https://nips.cc/">as spotlights at NeurIPS</a>! These papers introduce techniques for <a href="https://arxiv.org/abs/2306.02109">explaining time series models through self-supervised learning</a> and <a href="https://arxiv.org/abs/2310.02553">co-designing protein pocket sequences &amp; 3D structures</a>.</p>Marinka ZitnikCongratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences &amp; 3D structures.Future Directions in Network Biology2023-09-17T00:00:00-04:002023-09-17T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/09/17/Future-NetBio<p>Excited to share our perspectives on <a href="https://arxiv.org/abs/2309.08478">current and future directions in network biology.</a></p>Marinka ZitnikExcited to share our perspectives on current and future directions in network biology.Scientific Discovery in the Age of AI2023-08-02T00:00:00-04:002023-08-02T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/08/02/AI4Science-Nature<p>New paper on the role of <a href="https://rdcu.be/dinBA">artificial intelligence in scientific discovery is published in Nature.</a></p>Marinka ZitnikNew paper on the role of artificial intelligence in scientific discovery is published in Nature.PINNACLE - Contextual AI protein model2023-07-19T00:00:00-04:002023-07-19T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/07/19/PINNACLE<p><a href="https://www.biorxiv.org/content/10.1101/2023.07.18.549602">PINNACLE is a contextual AI model for protein understanding</a> that dynamically adjusts its outputs based on biological contexts in which it operates. <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p>Marinka ZitnikPINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.Our Group is Joining the Kempner Institute2023-06-21T00:00:00-04:002023-06-21T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/06/21/Kempner<p>Excited to join <a href="https://news.harvard.edu/gazette/story/newsplus/kempner-institute-announces-appointment-of-four-new-associate-faculty/">Kempner’s inaugural cohort of associate faculty</a> to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.</p>Marinka ZitnikExcited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.Welcoming a New Postdoctoral Fellow2023-06-11T00:00:00-04:002023-06-11T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/06/11/NewPostdoc<p>An enthusiastic welcome to <a href="https://scholar.google.com/citations?user=zW32dXsAAAAJ">Shanghua Gao</a> who is joining our group as a postdoctoral research fellow.</p>Marinka ZitnikAn enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.On Pretraining in Nature Machine Intelligence2023-06-01T00:00:00-04:002023-06-01T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/06/01/Pretraining-NatMachIntelligence<p>Excited to share our new study on <a href="https://www.nature.com/articles/s42256-023-00647-z">language model pretraining and general-purpose methods for biological sequences.</a> <a href="https://zitniklab.hms.harvard.edu/projects/SIPT/">Project website.</a></p>Marinka ZitnikExcited to share our new study on language model pretraining and general-purpose methods for biological sequences. Project website.Congratulations to Ada and Michelle2023-05-15T00:00:00-04:002023-05-15T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/05/15/CongratsMichelleAda<p>Congrats to PhD student Michelle on being selected as the <a href="http://www.albertjryanfoundation.org/fellows.cfm">2023 Albert J. Ryan Fellow</a> and also to participate in the <a href="https://www.heidelberg-laureate-forum.org/">Heidelberg Laureate Forum</a>. Congratulations to PhD student Ada for being selected as the <a href="https://www.harvard.edu/kempner-institute">Kempner Institute Graduate Fellow</a>!</p>Marinka ZitnikCongrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow! \ No newline at end of file diff --git a/index.html b/index.html index c49e111a..40a1d110 100644 --- a/index.html +++ b/index.html @@ -178,6 +178,96 @@

AI for Science | Therapeutic Science

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Oct 2023:   Structure-Based Drug Design

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Oct 2023:   Graph AI in Medicine

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Sep 2023:   New papers accepted at NeurIPS

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AI for Science | Therapeutic Science

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Sep 2023:   New papers accepted at NeurIPS

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Feb 2023:   PrimeKG published in Scientific Data

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Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

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Jan 2023:   GNNDelete published at ICLR 2023

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New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.

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Jan 2023:   New Network Principle for Molecular Phenotypes

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PhD Forum (ECML/PKDD 2020)

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Oct 2023:   Structure-Based Drug Design

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Oct 2023:   Graph AI in Medicine

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Sep 2023:   New papers accepted at NeurIPS

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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   PrimeKG published in Scientific Data

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Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

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Jan 2023:   GNNDelete published at ICLR 2023

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New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.

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Jan 2023:   New Network Principle for Molecular Phenotypes

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Oct 2023:   Structure-Based Drug Design

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Oct 2023:   Graph AI in Medicine

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Sep 2023:   New papers accepted at NeurIPS

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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2022:   Marissa Won the Gates Cambridge Scholarship

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Marissa Sumathipala is among the 23 outstanding US scholars selected be part of the 2022 class of Gates Cambridge Scholars at the University of Cambridge. Congratulations, Marissa!

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Jan 2022:   Inferring Gene Multifunctionality

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Hot off the press in Cell Systems. Webster is a tool to infer gene multifunctionality from high-dimensional gene perturbation data by applying sparse representation learning to large CRISPR-Cas9 fitness screens. Explore Webster’s web portal.

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Jan 2022:   Deep Graph AI for Time Series Accepted to ICLR

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Paper on graph representation learning for time series accepted to ICLR. Congratulations, Xiang!

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Feb 2022:   Marissa Won the Gates Cambridge Scholarship

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Marissa Sumathipala is among the 23 outstanding US scholars selected be part of the 2022 class of Gates Cambridge Scholars at the University of Cambridge. Congratulations, Marissa!

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Jan 2022:   Inferring Gene Multifunctionality

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Hot off the press in Cell Systems. Webster is a tool to infer gene multifunctionality from high-dimensional gene perturbation data by applying sparse representation learning to large CRISPR-Cas9 fitness screens. Explore Webster’s web portal.

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Jan 2022:   Deep Graph AI for Time Series Accepted to ICLR

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Paper on graph representation learning for time series accepted to ICLR. Congratulations, Xiang!

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Jun 2020:   Defense Against Adversarial Attacks

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GNNGuard can defend graph neural networks against a variety -of training-time attacks. Remarkably, -GNNGuard can restore state-of-the-art performance of any GNN in the face of adversarial attacks.

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Jun 2020:   Graph Meta Learning via Subgraphs

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G-Meta is a meta-learning approach for graphs that quickly adapts -to new prediction tasks using only a handful of data points. G-Meta works in most challenging, -few-shot learning settings and scales to massive interactomics data as we show -on our new Networks of Life dataset comprising of 1,840 networks.

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May 2020:   The Open Graph Benchmark

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A new paper introducing the Open Graph Benchmark, a diverse set of challenging and realistic benchmark datasets for graph machine learning.

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Jun 2020:   Defense Against Adversarial Attacks

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GNNGuard can defend graph neural networks against a variety +of training-time attacks. Remarkably, +GNNGuard can restore state-of-the-art performance of any GNN in the face of adversarial attacks.

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Jun 2020:   Graph Meta Learning via Subgraphs

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G-Meta is a meta-learning approach for graphs that quickly adapts +to new prediction tasks using only a handful of data points. G-Meta works in most challenging, +few-shot learning settings and scales to massive interactomics data as we show +on our new Networks of Life dataset comprising of 1,840 networks.

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May 2020:   The Open Graph Benchmark

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A new paper introducing the Open Graph Benchmark, a diverse set of challenging and realistic benchmark datasets for graph machine learning.

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Oct 2023:   Structure-Based Drug Design

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Oct 2023:   Graph AI in Medicine

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Sep 2023:   New papers accepted at NeurIPS

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Feb 2023:   PrimeKG published in Scientific Data

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Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

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Jan 2023:   GNNDelete published at ICLR 2023

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Jan 2023:   New Network Principle for Molecular Phenotypes

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New paper introducing mutual interactor-based GNN for molecular phenotype prediction at PSB. Project website.

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Advisor

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Oct 2023:   Structure-Based Drug Design

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Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

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Oct 2023:   Graph AI in Medicine

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Sep 2023:   New papers accepted at NeurIPS

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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   PrimeKG published in Scientific Data

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Our multimodal knowledge graph for precision medicine is published in Scientific Data. Project website.

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Jan 2023:   GNNDelete published at ICLR 2023

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New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.

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Jan 2023:   New Network Principle for Molecular Phenotypes

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New paper introducing mutual interactor-based GNN for molecular phenotype prediction at PSB. Project website.

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Application process

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Oct 2023:   Structure-Based Drug Design

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Sep 2023:   New papers accepted at NeurIPS

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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   PrimeKG published in Scientific Data

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Jan 2023:   GNNDelete published at ICLR 2023

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New paper on machine unlearning for graph neural networks accepted at ICLR 2023. Project website.

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Jan 2023:   New Network Principle for Molecular Phenotypes

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Advisor

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Oct 2023:   Structure-Based Drug Design

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Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers introduce techniques for explaining time series models through self-supervised learning and co-designing protein pocket sequences & 3D structures.

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Feb 2023:   PrimeKG published in Scientific Data

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Jan 2023:   GNNDelete published at ICLR 2023

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Jan 2023:   New Network Principle for Molecular Phenotypes

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Feb 2023:   PrimeKG published in Scientific Data

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