From 4acc9fd20c3dbc5b7fb249aa2ec313c29074c0af Mon Sep 17 00:00:00 2001 From: marinkaz Date: Wed, 19 Jul 2023 14:13:45 -0400 Subject: [PATCH] Update PINNACLE website --- .../07/19/PINNACLE}/index.html | 68 ++-- 2023/08/02/AI4Science-Nature/index.html | 183 +++++++++ 2023/09/17/Future-NetBio/index.html | 183 +++++++++ DMAI/index.html | 180 ++++----- bio/index.html | 4 +- data/index.html | 174 ++++----- feed.xml | 2 +- img/ivy_liang.png | Bin 0 -> 383527 bytes img/jonathan_schwarz.png | Bin 0 -> 390582 bytes index.html | 186 +++++----- jobs/index.html | 196 +++++----- meetings/index.html | 187 +++++----- news/index.html | 174 ++++----- news/page2/index.html | 168 ++++----- news/page3/index.html | 84 +++++ people/index.html | 350 ++++++++---------- postdoc-ML/index.html | 180 ++++----- postdoc-biomedicalAI-MGB/index.html | 174 ++++----- postdoc-biomedicalAI/index.html | 174 ++++----- postdoc-cancerTxAI/index.html | 174 ++++----- 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/huan_he/index.html | 4 +- .../{ziyuan_zhao => ivy_liang}/index.html | 26 +- .../index.html | 30 +- 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 | 16 +- products/xiang_zhang/index.html | 4 +- products/yasha_ektefaie/index.html | 4 +- products/yepeng_huang/index.html | 8 +- products/zaixi_zhang/index.html | 8 +- projects/G-Meta/index.html | 174 ++++----- projects/GNNDelete/index.html | 174 ++++----- projects/GNNGuard/index.html | 174 ++++----- projects/GraphXAI/index.html | 174 ++++----- projects/Milieu/index.html | 174 ++++----- projects/NIFTY/index.html | 174 ++++----- projects/PINNACLE/index.html | 194 +++++----- projects/PrimeKG/index.html | 174 ++++----- projects/REMAP/index.html | 174 ++++----- projects/Raincoat/index.html | 178 ++++----- projects/Raindrop/index.html | 174 ++++----- projects/SHEPHERD/index.html | 174 ++++----- projects/SIPT/index.html | 174 ++++----- projects/SubGNN/index.html | 174 ++++----- projects/TF-C/index.html | 174 ++++----- projects/TimeX/index.html | 174 ++++----- projects/TxGNN/index.html | 174 ++++----- projects/metapaths/index.html | 174 ++++----- projects/patient-safety/index.html | 174 ++++----- publications/index.html | 174 ++++----- publications/slides/2020-COVID-19-Final.pdf | Bin 0 -> 9498410 bytes .../thumbnails/AI4Science-Nature23.png | Bin 0 -> 10029 bytes .../thumbnails/AI4Science-nature23.png | Bin 9407 -> 0 bytes publications/thumbnails/Future-NetBio.png | Bin 0 -> 14879 bytes publications/thumbnails/PINNACLE23.png | Bin 0 -> 14737 bytes pubs.json | 54 ++- sitemap.xml | 76 ++-- software/index.html | 174 ++++----- 73 files changed, 3727 insertions(+), 3090 deletions(-) rename {products/jg_brasier => 2023/07/19/PINNACLE}/index.html (71%) create mode 100644 2023/08/02/AI4Science-Nature/index.html create mode 100644 2023/09/17/Future-NetBio/index.html create mode 100644 img/ivy_liang.png create mode 100644 img/jonathan_schwarz.png rename products/{ziyuan_zhao => ivy_liang}/index.html (86%) rename products/{isuru_herath => jonathan_schwarz}/index.html (82%) create mode 100644 publications/slides/2020-COVID-19-Final.pdf create mode 100644 publications/thumbnails/AI4Science-Nature23.png delete mode 100644 publications/thumbnails/AI4Science-nature23.png create mode 100644 publications/thumbnails/Future-NetBio.png create mode 100644 publications/thumbnails/PINNACLE23.png diff --git a/products/jg_brasier/index.html b/2023/07/19/PINNACLE/index.html similarity index 71% rename from products/jg_brasier/index.html rename to 2023/07/19/PINNACLE/index.html index 1683f1a4..baa35ad4 100644 --- a/products/jg_brasier/index.html +++ b/2023/07/19/PINNACLE/index.html @@ -4,33 +4,31 @@ - <a href="#">Jean-Guillaume Brasier</a> - Zitnik Lab + PINNACLE - Contextual AI protein model - Zitnik Lab -Jean-Guillaume Brasier | Zitnik Lab +PINNACLE - Contextual AI protein model | Zitnik Lab - + - - - - + + + + - - + - - + +{"mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/2023/07/19/PINNACLE/"},"url":"https://zitniklab.hms.harvard.edu/2023/07/19/PINNACLE/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"PINNACLE - Contextual AI protein model","description":"Contextual AI, Protein Representation Learning","dateModified":"2023-07-19T00:00:00-04:00","datePublished":"2023-07-19T00:00:00-04:00","@type":"BlogPosting","@context":"https://schema.org"}
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Scientific Discovery in the Age of AI

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Published: Aug 2, 2023

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New paper on the role of artificial intelligence in scientific discovery is published in Nature.

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Future Directions in Network Biology

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Published: Sep 17, 2023

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Excited to share our perspectives on current and future directions in network biology.

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-Data, Machines, and AI (DMAI) is a reading group to discuss AI theory and methods, with a strong bent towards understanding what AI methods are most suitable for groundbreaking problems in biology and medicine, and how to advance the state-of-the-art. +Data, Machines, and AI (DMAI) is a reading group to discuss AI theory and methods, with a strong bent towards understanding what AI methods are most suitable for problems in biology and medicine, and how to advance state-of-the-art AI algorithms.

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Virtual meetings on Zoom. We will resume in-person meetings as soon as we return to campus.

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Hybrid meetings on Zoom and in-person in Countway Bldg.

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Reach out to the reading group coordinator (Huan He, <huan_he@hms.harvard.edu>) with questions, comments, and suggestions.

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Reach out to the reading group coordinator (Wanxiang Shen, <WanXiang_Shen@hms.harvard.edu>) with questions, comments, and suggestions.

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Sep 2023:   Future Directions in Network Biology

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Excited to share our perspectives on current and future directions in network biology.

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Aug 2023:   Scientific Discovery in the Age of AI

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Jul 2023:   PINNACLE - Contextual AI protein model

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PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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Dec 2022:   Can we shorten rare disease diagnostic odyssey?

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New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

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Nov 2022:   Can AI transform the way we discover new drugs?

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Marinka Zitnik is an Assistant Professor at Harvard University in the Department of Biomedical Informatics. Dr. Zitnik is Associate Faculty at the Kempner Institue for the Study of Natural and Artificial Intelligence, Broad Institute of MIT and Harvard, and Harvard Data Science. Dr. Zitnik investigates the foundations of AI to enhance scientific discovery and to realize individualized diagnosis and treatment.

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Marinka Zitnik is an Assistant Professor at Harvard University in the Department of Biomedical Informatics. Dr. Zitnik is Associate Faculty at the Kempner Institue for the Study of Natural and Artificial Intelligence, Broad Institute of MIT and Harvard, and Harvard Data Science. Dr. Zitnik investigates foundations of AI to enhance scientific discovery and facilitate individualized diagnosis and treatment in medicine.

Dr. Zitnik joined Harvard as an Assistant Professor in December 2019. Before joining Harvard, she was a postdoctoral scholar in Computer Science at Stanford University. She was also a member of the Chan Zuckerberg Biohub at Stanford. She received her bachelor’s degree, double majoring in computer science and mathematics, and then graduated with a Ph.D. in Computer Science from University of Ljubljana just three years later while also researching at Imperial College London, University of Toronto, Baylor College of Medicine, and Stanford University.

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Dr. Zitnik is an ELLIS Scholar in the European Laboratory for Learning and Intelligent Systems (ELLIS) Society. She is a member of the Science Working Group at NASA Space Biology.

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Dr. Zitnik co-founded Therapeutics Data Commons and is the faculty lead of the AI4Science initiative.

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Dr. Zitnik co-founded Therapeutics Data Commons and is the faculty lead of the AI4Science initiative.

Dr. Zitnik is the recipient of the 2022 Young Mentor Award at Harvard Medical School.

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Sep 2023:   Future Directions in Network Biology

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Excited to share our perspectives on current and future directions in network biology.

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Aug 2023:   Scientific Discovery in the Age of AI

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Jul 2023:   PINNACLE - Contextual AI protein model

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PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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Dec 2022:   Can we shorten rare disease diagnostic odyssey?

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diff --git a/feed.xml b/feed.xml index 1520bc4a..619ac607 100644 --- a/feed.xml +++ b/feed.xml @@ -1 +1 @@ -Jekyll2023-07-14T11:27:26-04:00https://zitniklab.hms.harvard.edu/feed.xmlZitnik LabHarvard Machine Learning for Medicine and ScienceMarinka ZitnikOur 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.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.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.New Study in Nature Machine Intelligence2023-04-03T00:00:00-04:002023-04-03T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/04/03/MultimodalGraphLearning-NatMachIntelligence<p>New paper in Nature Machine Intelligence introducing the <a href="https://www.nature.com/articles/s42256-023-00624-6">blueprint for multimodal learning with graphs</a>.</p>Marinka ZitnikNew paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.Self-Driving Labs in Nature Machine Intelligence2023-03-23T00:00:00-04:002023-03-23T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/03/23/BiologicalResearchDeepSpace-NatMachIntelligence<p>New paper with NASA in Nature Machine Intelligence on <a href="https://www.nature.com/articles/s42256-023-00618-4">biological research and self-driving labs in deep space supported by artificial intelligence</a>.</p>Marinka ZitnikNew paper with NASA in Nature Machine Intelligence on biological research and self-driving labs in deep space supported by artificial intelligence.Precision Health in Nature Machine Intelligence2023-03-23T00:00:00-04:002023-03-23T00:00:00-04:00https://zitniklab.hms.harvard.edu/2023/03/23/PrecisionHealthDeepSpace-NatMachIntelligence<p>New paper with NASA in Nature Machine Intelligence on <a href="https://www.nature.com/articles/s42256-023-00617-5">biomonitoring and precision health in deep space supported by artificial intelligence</a>.</p>Marinka ZitnikNew paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence. \ No newline at end of file +Jekyll2023-09-17T21:57:56-04:00https://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 diff --git a/img/ivy_liang.png b/img/ivy_liang.png new file mode 100644 index 0000000000000000000000000000000000000000..72be42fca620fbe05a530d4e0f199b23d77fbe3d GIT binary patch literal 383527 zcmZ^~2Uru$*EdX25NRq(r~&mBR7w<(5<(&(Aku7f2q0a04-mS5NQnqY?^P+G_m1@5 zA(Rk`bO=2O0Y2~l^Stl-d~NsIYtEiBzcVwtv$K27hQ3i#WW3ICoq~dbQR&socN7#< z3V)C5*J%E7Iu-rA{uY$Z?-XB96c2K3{5^SNrmbYIs!H+rFMW-Iit-i(^}i~AcZv`z ziYx!2DJb~6HIRnojk3Q7n$Pc+S|^*s3KM$v|Y4SRlp|pw!+4y 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Machine Learning Foundations

-Artificial intelligence is poised to enable breakthroughs in science and reshape medicine. We investigate machine learning with a current focus on learning systems informed by geometry, structure, and symmetry and grounded in knowledge. This approach creates foundational models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization. Our methods produce actionable outputs to advance state of the art and open up new opportunities. + + +Our overarching goal is to lay the foundations for AI that contribute to the scientific understanding of medicine and therapeutic design and ultimately acquire such knowledge autonomously. + +

+

+We focus on foundational innovation in artificial intelligence and machine learning with an emphasis on AI systems that are informed by geometry, structure, and grounded in scientific knowledge. This involves building AI models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization.

AI for Medicine | Individualized Diagnosis and Treatment

-The state of a person is described with increasing precision incorporating modalities like genetic code, behaviors, therapeutics, and the environment—the challenge is how to reason over these data to develop powerful disease diagnostics and empower new kinds of therapies. Our research creates new avenues for accelerating the development of therapies, fusing biomedical knowledge and patient data, and giving the right patient the right treatment at the right time to have medicinal effects that are consistent from person to person and with results in the laboratory. +The state of a person is described with increasing precision incorporating modalities like genetic code, cellular atlases, molecular datasets, and therapeutics—the challenge is how to reason over these data to develop powerful disease diagnostics and empower new kinds of therapies. Our research creates new avenues for fusing knowledge and patient data to give the right patient the right treatment at the right time and have medicinal effects that are consistent from person to person and with results in the laboratory.

AI for Science | Therapeutic Science

-For centuries, the method of discovery—the fundamental practice of science that scientists use to explain the natural world systematically and logically—has remained largely the same. We are using artificial intelligence to change that. The natural world is interconnected, from the various facets of genome regulation to the molecular and organismal levels. These interactions across different levels yield a bewildering degree of complexity. Our research seeks to disentangle this complexity, developing artificial intelligence systems to contribute to scientific understanding of drug design and therapies or acquire this understanding autonomously. +For centuries, the method of discovery—the fundamental practice of science that scientists use to explain the natural world systematically and logically—has remained largely the same. We are using AI to change that. The natural world is interconnected, from the various facets of genome regulation to the molecular and organismal levels. These interactions across different levels yield a bewildering degree of complexity. Our research seeks to disentangle this complexity, developing AI models that advance drug design and help develop new kinds of therapies.

@@ -172,6 +178,96 @@

AI for Science | Therapeutic Science

+ + +
+
+ +
+ +

Aug 2023:   Scientific Discovery in the Age of AI

+ + +
+ +
+ + + +

New paper on the role of artificial intelligence in scientific discovery is published in Nature.

+ + + + + + +
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+
+ +
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+ +

Jul 2023:   PINNACLE - Contextual AI protein model

+ + +
+ +
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PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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AI for Science | Therapeutic Science

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Dec 2022:   Can we shorten rare disease diagnostic odyssey?

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New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

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Nov 2022:   Can AI transform the way we discover new drugs?

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Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

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Oct 2022:   New Paper in Nature Biomedical Engineering

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New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

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diff --git a/jobs/index.html b/jobs/index.html index d9309878..d5e3f5fd 100644 --- a/jobs/index.html +++ b/jobs/index.html @@ -140,35 +140,37 @@
-

Thank you for being so interested in joining our group! Impactful research requires quality mentoring relationships. Prof. Zitnik is the recipient of the 2022 Young Mentor Award at Harvard Medical School—this most prestigious award acknowledges that recognition to her.

+

Thank you for being so interested in joining our group! Impactful research requires quality mentoring relationships. Prof. Zitnik is the recipient of the 2022 Young Mentor Award at Harvard Medical School—this prestigious award acknowledges that recognition to her.

Graduate students

-

We are taking on new PhD students each year.

+

We are taking on new PhD students every year.

If you are a current or a newly admitted PhD student interested in AI, machine learning and/or applications in genomics, therapeutics, medicine, and health, email Prof. Zitnik directly. Include your CV and a brief description of your research interests.

-

We are recruiting PhD students from a number of graduate programs, including Bioinformatics and Integrative Genomics, Systems Biology, Biological and Biomedical Sciences, programs within Harvard Integrated Life Sciences, and other programs at Harvard. We also recruit graduate students from Health Sciences & Technology at Harvard and MIT and other programs at MIT.

+

We are recruiting PhD students from a number of graduate programs, including Artificial Intelligence in Medicine, Bioinformatics and Integrative Genomics, Biomedical Informatics, Systems Biology, Biological and Biomedical Sciences, programs within Harvard Integrated Life Sciences, and other programs at Harvard.

+ +

We also recruit graduate students from Health Sciences & Technology at Harvard and MIT and other programs at MIT.


Postdoctoral research fellows in foundational AI

-

We have openings for postdoctoral research fellows in fundamental methods in the broad area of geometric deep learning, transfer learning, large-scale knowledge graphs, large language models, and generative AI.

+

We have multiple openings for postdoctoral research fellows in the broad area of foundation models focusing on geometric deep learning, multimodal learning, large-scale knowledge graphs, large language models, and/or generative AI.

-

Applications are reviewed on a rolling basis. Interested candidates are encouraged to submit their applications early.

+

Applications are reviewed on a rolling basis. Interested candidates are encouraged to submit their applications as soon as possible.

NOW OPEN: Request For Applications

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+
@@ -229,6 +231,96 @@

Visitors, interns, and short-t
+
+
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+ +

Sep 2023:   Future Directions in Network Biology

+ + +
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Excited to share our perspectives on current and future directions in network biology.

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Aug 2023:   Scientific Discovery in the Age of AI

+ + +
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New paper on the role of artificial intelligence in scientific discovery is published in Nature.

+ + + + + + +
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+ +

Jul 2023:   PINNACLE - Contextual AI protein model

+ + +
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PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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Visitors, interns, and short-t

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Dec 2022:   Can we shorten rare disease diagnostic odyssey?

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New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

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Nov 2022:   Can AI transform the way we discover new drugs?

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Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

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Oct 2022:   New Paper in Nature Biomedical Engineering

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New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

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diff --git a/meetings/index.html b/meetings/index.html index e05df9e5..66826980 100644 --- a/meetings/index.html +++ b/meetings/index.html @@ -161,6 +161,7 @@
  • International Workshops and Conferences
      +
    • AI for Scientific Discovery: From Theory to Practice (NeurIPS 2023)
    • Graph Learning Benchmarks (KDD 2023)
    • Graph Representations and Algorithms in Biomedicine (PSB 2023)
    • AI4Science: Progress and Promises (NeurIPS 2022)
    • @@ -287,6 +288,18 @@

      Biomedical Data Fusion (EMBC a

      International Workshops and Conferences

      +

      AI for Scientific Discovery: From Theory to Practice (NeurIPS 2023)

      + +
      +For centuries, the method of discovery—the fundamental practice of science that scientists use to systematically and logically explain the natural world—has remained largely unchanged. Artificial intelligence and machine learning hold tremendous promise for revolutionizing the way scientific discovery is conducted at a fundamental level. However, to realize this potential, it's imperative to identify priorities and address the pressing open questions that stand at the cutting edge of AI. We are especially keen on exploring topics such as solving grand challenges in structural biology, scaling dynamical system modeling to millions of particles, visualizing the previously unimaginable black hole, incorporating physical insights into AI methods, and accelerating drug discovery. +
      + +

      This workshop is presented at the the International Conference on Neural Information Processing Systems (NeurIPS).

      + + +

      Graph Learning Benchmarks (KDD 2023)

      @@ -525,6 +538,96 @@

      PhD Forum (ECML/PKDD 2020)

      +
      +
      + +
      + +

      Sep 2023:   Future Directions in Network Biology

      + + +
      + +
      + + + +

      Excited to share our perspectives on current and future directions in network biology.

      + + + + + + +
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      +
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      +
      + +
      + +

      Aug 2023:   Scientific Discovery in the Age of AI

      + + +
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      New paper on the role of artificial intelligence in scientific discovery is published in Nature.

      + + + + + + +
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      +
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      + +

      Jul 2023:   PINNACLE - Contextual AI protein model

      + + +
      + +
      + + + +

      PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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      @@ -1029,90 +1132,6 @@

      PhD Forum (ECML/PKDD 2020)

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      Dec 2022:   Can we shorten rare disease diagnostic odyssey?

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      New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

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      Nov 2022:   Can AI transform the way we discover new drugs?

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      Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

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      Oct 2022:   New Paper in Nature Biomedical Engineering

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      New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

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      diff --git a/news/index.html b/news/index.html index 334ce851..159019ce 100644 --- a/news/index.html +++ b/news/index.html @@ -170,6 +170,96 @@
      +
      +
      + +
      + +

      Sep 2023:   Future Directions in Network Biology

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      Excited to share our perspectives on current and future directions in network biology.

      + + + + + + +
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      + +

      Aug 2023:   Scientific Discovery in the Age of AI

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      New paper on the role of artificial intelligence in scientific discovery is published in Nature.

      + + + + + + +
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      + +

      Jul 2023:   PINNACLE - Contextual AI protein model

      + + +
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      PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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      Jan 2022:   Probing GNN Explainers Accepted to AISTATS

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      Paper on probing GNN explainers through rigorous theoretical and empirical analysis of GNN explanation methods accepted to AISTATS. Congratulations, Chirag!

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      Jan 2022:   Marissa Sumathipala selected as Churchill Scholar

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      Marissa Sumathipala is selected for the prestigious Churchill Scholarship. Congratulations, Marissa!

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      Jan 2022:   Therapeutics Data Commons User Meetup

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      We invite you to join the growing open-science community at the User Group Meetup of Therapeutics Data Commons! Register for the first live user group meeting on Tuesday, January 25 at 11:00 AM EST.

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      Jan 2022:   Probing GNN Explainers Accepted to AISTATS

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      Paper on probing GNN explainers through rigorous theoretical and empirical analysis of GNN explanation methods accepted to AISTATS. Congratulations, Chirag!

      + + + + + +
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      Jan 2022:   Marissa Sumathipala selected as Churchill Scholar

      + + +
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      Marissa Sumathipala is selected for the prestigious Churchill Scholarship. Congratulations, Marissa!

      + + + + + +
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      Jan 2022:   Therapeutics Data Commons User Meetup

      + + +
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      We invite you to join the growing open-science community at the User Group Meetup of Therapeutics Data Commons! Register for the first live user group meeting on Tuesday, January 25 at 11:00 AM EST.

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      @@ -1509,90 +1593,6 @@
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      May 2020:   Special Issue on AI for COVID-19

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      Marinka is co-editing a special issue of IEEE Big Data on AI for COVID-19. In light of the urgent need for data-driven solutions to mitigate the COVID-19 pandemic, the special issue will aim for a fast-track peer review.

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      May 2020:   Multiscale Interactome

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      May 2020:   Molecular Interaction Networks

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      A new preprint describing a graph neural network approach for the prediction of molecular interactions, including drug-drug, drug-target, protein-protein, and gene-disease interactions.

      - - - - - -
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      -
      -
      +
      +
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      May 2020:   Special Issue on AI for COVID-19

      + + +
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      + + + +

      Marinka is co-editing a special issue of IEEE Big Data on AI for COVID-19. In light of the urgent need for data-driven solutions to mitigate the COVID-19 pandemic, the special issue will aim for a fast-track peer review.

      + + + + + +
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      May 2020:   Multiscale Interactome

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      May 2020:   Molecular Interaction Networks

      + + +
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      A new preprint describing a graph neural network approach for the prediction of molecular interactions, including drug-drug, drug-target, protein-protein, and gene-disease interactions.

      + + + + + +
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      +
      diff --git a/people/index.html b/people/index.html index f77b3d05..218e0843 100644 --- a/people/index.html +++ b/people/index.html @@ -268,20 +268,20 @@
      - +
      - <a href=Guadalupe Gonzalez" /> + <a href=Valentina Giunchiglia" />
      -

      Guadalupe Gonzalez

      +

      Valentina Giunchiglia

      PhD Student
      Imperial College London

      @@ -297,20 +297,20 @@
      - +
      - <a href=Valentina Giunchiglia" /> + <a href=Guadalupe Gonzalez" />
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      Valentina Giunchiglia

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      Guadalupe Gonzalez

      PhD Student
      Imperial College London

      @@ -326,21 +326,21 @@
      - +
      - <a href=Tianlong Chen" /> + <a href=Yepeng Huang" />
      -

      Tianlong Chen

      -

      Postdoctoral Fellow

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      Yepeng Huang

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      PhD Student

      @@ -355,21 +355,21 @@
      - +
      - <a href=Ruth Johnson" /> + <a href=Zaixi Zhang" />
      -

      Ruth Johnson

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      Postdoctoral Fellow
      Harvard Berkowitz Fellow

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      Zaixi Zhang

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      Visiting PhD Student

      @@ -382,12 +382,6 @@ - - - - - -
      @@ -419,20 +413,20 @@
      - +
      - <a href=Huan He" /> + <a href=Wanxiang Shen" />
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      Huan He

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      Wanxiang Shen

      Postdoctoral Fellow

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      Shen Wanxiang

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      Postdoctoral Fellow

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      Shanghua Gao

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      Jonathan Richard Schwarz

      Postdoctoral Fellow

      @@ -504,26 +475,23 @@ - - -
      - +
      - <a href=Owen Queen" /> + <a href=Ruth Johnson" />
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      Owen Queen

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      Graduate Researcher

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      Ruth Johnson

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      Postdoctoral Fellow
      Harvard Berkowitz Fellow

      @@ -538,21 +506,21 @@
      - +
      - <a href=Tom Cobley" /> + <a href=Shanghua Gao" />
      -

      Tom Cobley

      -

      Graduate Researcher
      MIT

      +

      Shanghua Gao

      +

      Postdoctoral Fellow

      @@ -567,21 +535,21 @@
      - +
      - <a href=Yepeng Huang" /> + <a href=Tianlong Chen" />
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      Yepeng Huang

      -

      Graduate Researcher

      +

      Tianlong Chen

      +

      Postdoctoral Fellow

      @@ -594,23 +562,26 @@ + + +
      - +
      - <a href=Jean-Guillaume Brasier" /> + <a href=Tom Cobley" />
      -

      Jean-Guillaume Brasier

      -

      IACS Graduate Student

      +

      Tom Cobley

      +

      Graduate Researcher
      MIT

      @@ -625,20 +596,20 @@
      - +
      - <a href=Michelle Dai" /> + <a href=Owen Queen" />
      -

      Michelle Dai

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      Owen Queen

      Graduate Researcher

      @@ -712,50 +683,21 @@
      - - -
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      Zaixi Zhang

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      Visiting Fellow

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      Visiting Fellow

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      Michelle Dai

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      Graduate Researcher

      @@ -770,20 +712,20 @@
      - +
      - <a href=Ziyuan Zhao" /> + <a href=Ivy Liang" />
      -

      Ziyuan Zhao

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      Ivy Liang

      Undergraduate Researcher
      Harvard

      @@ -856,6 +798,10 @@

      Associate members

      + + + + @@ -941,14 +887,6 @@

      Associate members

      - - - - - - - - @@ -967,6 +905,8 @@

      Lab alumni

    • Marissa Sumathipala (Undergraduate Researcher at Harvard College; Gates Cambridge Scholarship; Churchill Scholarship; Cambridge University → Neuroscience PhD Student, Harvard University)
    • Julia Balla (Undergraduate Researcher at MIT → Computer Science PhD Student, MIT)
    • Jiali Cheng (Undergraduate Researcher → Computer Science PhD Student, UMass Lowell)
    • +
    • Isuru Herath (Undergraduate Researcher → Data Scientist, Merck)
    • +
    • Jean-Guillaume Brasier (Harvard IACS Graduate Student → Data Scientist, Moderna)
    2022: @@ -1011,6 +951,96 @@

    Lab alumni

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    Sep 2023:   Future Directions in Network Biology

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    Excited to share our perspectives on current and future directions in network biology.

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    Aug 2023:   Scientific Discovery in the Age of AI

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    New paper on the role of artificial intelligence in scientific discovery is published in Nature.

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    Jul 2023:   PINNACLE - Contextual AI protein model

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    PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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    Lab alumni

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    Dec 2022:   Can we shorten rare disease diagnostic odyssey?

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    New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

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    Nov 2022:   Can AI transform the way we discover new drugs?

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    Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

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    Oct 2022:   New Paper in Nature Biomedical Engineering

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    New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

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    diff --git a/postdoc-ML/index.html b/postdoc-ML/index.html index 4beabfa4..bc50e15f 100644 --- a/postdoc-ML/index.html +++ b/postdoc-ML/index.html @@ -144,7 +144,7 @@

    Overview

    Prof. Marinka Zitnik invites applications for a Postdoctoral Research Fellowship position at Harvard University.

    -

    Selected candidates will be expected to lead research in novel methods in geometric deep learning, large-scale knowledge graphs, large language models, transfer learning, and generative AI. In addition, fellows will have opportunities to transition novel algorithms to applications in therapeutic science and precision medicine.

    +

    Selected candidates will be expected to lead research in foundation models focusing on geometric deep learning, large-scale knowledge graphs, large language models, multimodal learning, and/or generative AI. In addition, fellows will have opportunities to transition novel algorithms to applications in therapeutic science and precision medicine.

    Interested candidates are invited to review our recent publications and research directions.

    @@ -154,7 +154,7 @@

    Qualifications

    We are looking for applicants with demonstrably strong research skills, ideally, with multiple publications in top venues in machine learning and artificial intelligence, and/or top-tier scientific journals.

    -

    Candidates must have a Ph.D. or equivalent degree in computer science, statistics, or a closely related field. Strong programming skills and practical experience with leading machine learning frameworks are required. Experience and/or interest in applications of AI to science, biology and medicine is a plus.

    +

    Candidates must have a Ph.D. or equivalent degree in computer science, statistics, or a closely related field. Strong programming skills and practical experience with leading machine learning frameworks are required. Experience and/or interest in applications of AI to science, biology and medicine is a strong plus.

    Application process

    @@ -171,7 +171,7 @@

    Application process

    Advisor

    -

    Marinka Zitnik is an Assistant Professor at Harvard University with appointments in the Department of Biomedical Informatics, Broad Institute of MIT and Harvard, and Harvard Data Science. We investigate machine learning with a current focus on learning systems informed by geometry, structure, and symmetry and grounded in knowledge. This approach creates foundational models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization. Our methods produce actionable outputs to advance biological problems past the state of the art and open up new opportunities.

    +

    Marinka Zitnik is an Assistant Professor at Harvard University with appointments in the Department of Biomedical Informatics, Kempner Institute for the Study of Natural and Artificial Intelligence, Broad Institute of MIT and Harvard, and Harvard Data Science. We investigate machine learning with a current focus on learning systems informed by geometry, structure, and symmetry and grounded in knowledge. This approach creates foundational models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization. Our methods produce actionable outputs to advance biological problems past the state of the art and open up new opportunities.

    Dr. Zitnik has published extensively in top ML venues, such as NeurIPS, ICLR, ICML, and leading scientific journals, including Nature, Nature Methods, Nature Communications, and PNAS. She has organized numerous workshops and tutorials in the nexus of AI, deep learning, AI4Science and AI4Medicine at leading conferences, where she is also in the organizing committees.

    @@ -191,6 +191,96 @@

    Advisor

    +
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    Sep 2023:   Future Directions in Network Biology

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    Excited to share our perspectives on current and future directions in network biology.

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    Aug 2023:   Scientific Discovery in the Age of AI

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    New paper on the role of artificial intelligence in scientific discovery is published in Nature.

    + + + + + + +
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    Jul 2023:   PINNACLE - Contextual AI protein model

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    PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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    @@ -695,90 +785,6 @@

    Advisor

    -
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    Dec 2022:   Can we shorten rare disease diagnostic odyssey?

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    New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

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    Nov 2022:   Can AI transform the way we discover new drugs?

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    Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

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    Oct 2022:   New Paper in Nature Biomedical Engineering

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    New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

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    Application process

    +
    +
    + +
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    Sep 2023:   Future Directions in Network Biology

    + + +
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    Excited to share our perspectives on current and future directions in network biology.

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    Aug 2023:   Scientific Discovery in the Age of AI

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    New paper on the role of artificial intelligence in scientific discovery is published in Nature.

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    Jul 2023:   PINNACLE - Contextual AI protein model

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    PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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    Application process

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    Dec 2022:   Can we shorten rare disease diagnostic odyssey?

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    New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

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    Nov 2022:   Can AI transform the way we discover new drugs?

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    Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

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    Oct 2022:   New Paper in Nature Biomedical Engineering

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    New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

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    Advisor

    +
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    Sep 2023:   Future Directions in Network Biology

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    Excited to share our perspectives on current and future directions in network biology.

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    Aug 2023:   Scientific Discovery in the Age of AI

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    New paper on the role of artificial intelligence in scientific discovery is published in Nature.

    + + + + + + +
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    Jul 2023:   PINNACLE - Contextual AI protein model

    + + +
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    PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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    Advisor

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    Dec 2022:   Can we shorten rare disease diagnostic odyssey?

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    New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

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    Nov 2022:   Can AI transform the way we discover new drugs?

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    Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

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    Oct 2022:   New Paper in Nature Biomedical Engineering

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    New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

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    Faculty and mentors

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    Sep 2023:   Future Directions in Network Biology

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    Excited to share our perspectives on current and future directions in network biology.

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    Aug 2023:   Scientific Discovery in the Age of AI

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    New paper on the role of artificial intelligence in scientific discovery is published in Nature.

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    Jul 2023:   PINNACLE - Contextual AI protein model

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    PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

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    Faculty and mentors

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    Nov 2022:   Can AI transform the way we discover new drugs?

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    Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

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    Oct 2022:   New Paper in Nature Biomedical Engineering

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    -
    -
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