Published: Sep 25, 2024
+ +Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
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- <a href="#">Kushan Weerakoon</a> - Zitnik Lab
+ Graph AI in Medicine - Zitnik Lab
-Kushan Weerakoon | Zitnik Lab
+Graph AI in Medicine | Zitnik Lab
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Coordinator
-Reach out to the reading group coordinator (Wanxiang Shen, <WanXiang_Shen@hms.harvard.edu>) with questions, comments, and suggestions.
+Reach out to the reading group coordinator (Zhenglun Kong) with questions and suggestions.
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+ Aug 2024: How Proteins Behave in Context
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+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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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.
+Dr. Zitnik is the recipient of the 2022 Young Mentor Award at Harvard Medical School.
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-Jekyll 2024-07-27T17:49:29-04:00 https://zitniklab.hms.harvard.edu/feed.xml Zitnik Lab Harvard Machine Learning for Medicine and Science Marinka Zitnik PINNACLE in Nature Methods 2024-07-27T00:00:00-04:00 2024-07-27T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/07/27/PINNACLENatureMethods <p>PINNACLE contextual AI model is published in Nature Methods. <a href="https://www.nature.com/articles/s41592-024-02341-3">Paper.</a> <a href="https://www.nature.com/articles/s41592-024-02342-2">Research Briefing.</a> <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p> Marinka Zitnik PINNACLE contextual AI model is published in Nature Methods. Paper. Research Briefing. Project website. Digital Twins as Global Health and Disease Models of Individuals 2024-07-15T00:00:00-04:00 2024-07-15T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/07/15/DigitalTwins <p><a href="https://www.preprints.org/manuscript/202406.0357/">Paper on digitial twins</a> outlining strategies to leverage molecular and computational techniques to construct dynamic digital twins on the scale of populations to individuals.</p> Marinka Zitnik Paper on digitial twins outlining strategies to leverage molecular and computational techniques to construct dynamic digital twins on the scale of populations to individuals. Graph Diffusion Convolutions at ICML 2024-07-14T00:00:00-04:00 2024-07-14T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/07/14/GraphAdversarialDiffusion <p><a href="https://arxiv.org/abs/2406.02059">Graph diffusion convolution is a geometric deep learning architecture that aggregates information from higher-order network neighbors through a generalized graph diffusion</a> to enhance model robustness to noisy and incomplete datasets. <a href="https://arxiv.org/abs/2406.02059">Paper at ICML.</a></p> Marinka Zitnik Graph diffusion convolution is a geometric deep learning architecture that aggregates information from higher-order network neighbors through a generalized graph diffusion to enhance model robustness to noisy and incomplete datasets. Paper at ICML. Three Papers: TrialBench, 3D Structure Design, LLM Editing 2024-07-13T00:00:00-04:00 2024-07-13T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/07/13/TrialBenchLLM3DStructure <p>New papers on <a href="https://arxiv.org/abs/2407.00631">TrialBench with AI-ready clinical trial datasets</a>, <a href="https://arxiv.org/abs/2406.03403">structure-based drug design benchmark</a>, and <a href="https://arxiv.org/abs/2407.06483">composable interventions for language models</a>.</p> Marinka Zitnik New papers on TrialBench with AI-ready clinical trial datasets, structure-based drug design benchmark, and composable interventions for language models. TDC-2: Multimodal Foundation for Therapeutics 2024-06-23T00:00:00-04:00 2024-06-23T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/06/23/TDC2 <p><a href="https://tdcommons.ai/">The Commons 2.0 (TDC-2)</a> is an overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new tasks and benchmarks. <a href="https://www.biorxiv.org/content/10.1101/2024.06.12.598655v2">Our paper.</a></p> Marinka Zitnik The Commons 2.0 (TDC-2) is an overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new tasks and benchmarks. Our paper. Broad MIA: Protein Language Models 2024-05-29T00:00:00-04:00 2024-05-29T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/05/29/BroadMIA <p>Check out our Broad’s seminars on <a href="https://youtu.be/LcLmvtXHI1s?si=GABqGdFt5ze9leT_">Multimodal protein language models for deciphering protein function.</a></p> Marinka Zitnik Check out our Broad’s seminars on Multimodal protein language models for deciphering protein function. On Knowing a Gene in Cell Systems 2024-05-28T00:00:00-04:00 2024-05-28T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/05/28/KnowingAGene <p>We shed light on <a href="https://www.sciencedirect.com/science/article/pii/S2405471224001236">distributional gene representations and their potential applications in biology to characterize gene function from a broader and more holistic perspective.</a></p> Marinka Zitnik We shed light on distributional gene representations and their potential applications in biology to characterize gene function from a broader and more holistic perspective. Biomedical AI Agents 2024-04-04T00:00:00-04:00 2024-04-04T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/04/04/BIomedicalAIAgents <p>We envision <a href="https://arxiv.org/abs/2404.02831">‘AI scientists’ as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents</a> that integrate machine learning tools with experimental platforms.</p> Marinka Zitnik We envision ‘AI scientists’ as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate machine learning tools with experimental platforms. Efficient ML Seminar Series 2024-03-23T00:00:00-04:00 2024-03-23T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/03/23/EfficientMLSeminar <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> Marinka Zitnik We started a Harvard University Efficient ML Seminar Series. Congrats to Jonathan for spearheading this initiative. Harvard Magazine covered the first meeting focusing on LLMs. UniTS - Unified Time Series Model 2024-03-04T00:00:00-05:00 2024-03-04T00:00:00-05:00 https://zitniklab.hms.harvard.edu/2024/03/04/UniTS <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> Marinka Zitnik UniTS is a unified time series model that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. Project website. Jekyll 2024-09-27T23:26:33-04:00 https://zitniklab.hms.harvard.edu/feed.xml Zitnik Lab Harvard Machine Learning for Medicine and Science Marinka Zitnik Three Papers Accepted to NeurIPS 2024-09-27T00:00:00-04:00 2024-09-27T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers <p>Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.</p> Marinka Zitnik Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models. TxGNN Published in Nature Medicine 2024-09-25T00:00:00-04:00 2024-09-25T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine <p>Graph foundation model for drug repurposing published in <a href="https://www.nature.com/articles/s41591-024-03233-x">Nature Medicine</a>. <a href="https://news.harvard.edu/gazette/story/2024/09/using-ai-to-repurpose-existing-drugs-for-treatment-of-rare-diseases/">[Harvard Gazette]</a> <a href="https://hms.harvard.edu/news/researchers-harness-ai-repurpose-existing-drugs-treatment-rare-diseases">[Harvard Medicine News]</a> <a href="https://www.forbes.com/sites/greglicholai/2024/09/26/ai-tool-speeds-drug-repurposing-and-its-free/">[Forbes]</a></p> Marinka Zitnik Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes] Graph AI in Medicine 2024-08-28T00:00:00-04:00 2024-08-28T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/08/28/GraphAI <p>Excited to share a new perspective on <a href="https://go.shr.lc/4g0KpLV">Graph Artificial Intelligence in Medicine</a> in Annual Reviews.</p> Marinka Zitnik Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews. How Proteins Behave in Context 2024-08-15T00:00:00-04:00 2024-08-15T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/08/15/PINNACLENews <p><a href="https://hms.harvard.edu/news/new-ai-tool-captures-how-proteins-behave-context">Harvard Medicine News</a> on our new AI tool that captures how proteins behave in context. <a href="https://kempnerinstitute.harvard.edu/research/deeper-learning/context-matters-for-foundation-models-in-biology/">Kempner Institute</a> on how context matters for foundation models in biology.</p> Marinka Zitnik Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology. PINNACLE in Nature Methods 2024-07-27T00:00:00-04:00 2024-07-27T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/07/27/PINNACLENatureMethods <p>PINNACLE contextual AI model is published in Nature Methods. <a href="https://www.nature.com/articles/s41592-024-02341-3">Paper.</a> <a href="https://www.nature.com/articles/s41592-024-02342-2">Research Briefing.</a> <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p> Marinka Zitnik PINNACLE contextual AI model is published in Nature Methods. Paper. Research Briefing. Project website. Digital Twins as Global Health and Disease Models of Individuals 2024-07-15T00:00:00-04:00 2024-07-15T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/07/15/DigitalTwins <p><a href="https://www.preprints.org/manuscript/202406.0357/">Paper on digitial twins</a> outlining strategies to leverage molecular and computational techniques to construct dynamic digital twins on the scale of populations to individuals.</p> Marinka Zitnik Paper on digitial twins outlining strategies to leverage molecular and computational techniques to construct dynamic digital twins on the scale of populations to individuals. Graph Diffusion Convolutions at ICML 2024-07-14T00:00:00-04:00 2024-07-14T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/07/14/GraphAdversarialDiffusion <p><a href="https://arxiv.org/abs/2406.02059">Graph diffusion convolution is a geometric deep learning architecture that aggregates information from higher-order network neighbors through a generalized graph diffusion</a> to enhance model robustness to noisy and incomplete datasets. <a href="https://arxiv.org/abs/2406.02059">Paper at ICML.</a></p> Marinka Zitnik Graph diffusion convolution is a geometric deep learning architecture that aggregates information from higher-order network neighbors through a generalized graph diffusion to enhance model robustness to noisy and incomplete datasets. Paper at ICML. Three Papers: TrialBench, 3D Structure Design, LLM Editing 2024-07-13T00:00:00-04:00 2024-07-13T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/07/13/TrialBenchLLM3DStructure <p>New papers on <a href="https://arxiv.org/abs/2407.00631">TrialBench with AI-ready clinical trial datasets</a>, <a href="https://arxiv.org/abs/2406.03403">structure-based drug design benchmark</a>, and <a href="https://arxiv.org/abs/2407.06483">composable interventions for language models</a>.</p> Marinka Zitnik New papers on TrialBench with AI-ready clinical trial datasets, structure-based drug design benchmark, and composable interventions for language models. TDC-2: Multimodal Foundation for Therapeutics 2024-06-23T00:00:00-04:00 2024-06-23T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/06/23/TDC2 <p><a href="https://tdcommons.ai/">The Commons 2.0 (TDC-2)</a> is an overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new tasks and benchmarks. <a href="https://www.biorxiv.org/content/10.1101/2024.06.12.598655v2">Our paper.</a></p> Marinka Zitnik The Commons 2.0 (TDC-2) is an overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new tasks and benchmarks. Our paper. Broad MIA: Protein Language Models 2024-05-29T00:00:00-04:00 2024-05-29T00:00:00-04:00 https://zitniklab.hms.harvard.edu/2024/05/29/BroadMIA <p>Check out our Broad’s seminars on <a href="https://youtu.be/LcLmvtXHI1s?si=GABqGdFt5ze9leT_">Multimodal protein language models for deciphering protein function.</a></p> Marinka Zitnik Check out our Broad’s seminars on Multimodal protein language models for deciphering protein function.
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-
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-We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
- - - - - - -
-
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-
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-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
- - -
-
-
-
-
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-
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-New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
- - - - - - -
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
- - -
-
-
-
-
-
-
-
-We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
- - - - - - -
-
-
-
-
-
-
-
-
- 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.
- - - - - - -
diff --git a/jobs/index.html b/jobs/index.html
index 9fd146ed..77285556 100644
--- a/jobs/index.html
+++ b/jobs/index.html
@@ -140,7 +140,7 @@
- Postdoct
Postdoctoral research fe
Visitors, interns, and short-t
Thank you for being so interested in joining our group! Impactful research requires excellent mentoring. Prof. Zitnik is the recipient of the Young Mentor Award at Harvard Medical School—this prestigious award acknowledges that recognition to her.
+Thank you for being so interested in joining our group! Impactful research requires excellent mentoring. Prof. Zitnik is the recipient of the Young Mentor Award at Harvard Medical School—this prestigious award acknowledges that recognition to her.
Graduate students
@@ -170,9 +170,9 @@Postdoct
Postdoctoral research fellows in foundation 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, generative AI, and AI agents.
+We have multiple openings for postdoctoral research fellows in foundation models focusing on geometric deep learning, multimodal learning, large-scale knowledge graphs, large language models, generative AI, and AI agents.
-Applications are reviewed on a rolling basis. Interested candidates are encouraged to submit their applications as soon as possible.
+This position is available immediately. Interested candidates are encouraged to submit their applications as soon as possible.
NOW OPEN: Request For Applications
@@ -198,7 +198,7 @@Postdoctoral research fe
Postdoctoral research fellows in medical AI
-
We have an opening for a postdoctoral research fellowship in novel methods in the broad area of medical AI.
+We have multiple openings for postdoctoral research fellows in medical AI.
This position is available immediately. Interested candidates are encouraged to submit their applications as soon as possible.
@@ -259,6 +259,118 @@Visitors, interns, and short-t
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -713,126 +825,6 @@ Visitors, interns, and short-t
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
-
-
-
-
-
-
-
-
- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
-
-
-
-
-
-
-
-
- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
-
-
-
-
-
-
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/meetings/index.html b/meetings/index.html
index d27a6356..12b193db 100644
--- a/meetings/index.html
+++ b/meetings/index.html
@@ -594,6 +594,118 @@ Biomedical Data Fusion (EMBC a
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -1048,126 +1160,6 @@ Biomedical Data Fusion (EMBC a
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
-
-
-
-
-
-
-
-
- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
-
-
-
-
-
-
-
-
- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
-
-
-
-
-
-
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/news/index.html b/news/index.html
index b65ffa90..be051183 100644
--- a/news/index.html
+++ b/news/index.html
@@ -170,6 +170,118 @@
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -1510,118 +1622,6 @@
-
-
-
-
-
- Sep 2022: Self-Supervised Pre-Training at NeurIPS 2022
-
-
-
-
-
-
-
-
- New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Sep 2022: Best Paper Honorable Mention Award at IEEE VIS
-
-
-
-
-
-
-
-
- Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Sep 2022: Multimodal Representation Learning with Graphs
-
-
-
-
-
-
-
-
- New preprint! We present the blueprint for graph-centric multimodal learning.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Aug 2022: On Graph AI for Precision Medicine
-
-
-
-
-
-
-
-
- The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.
-
-
-
-
-
-
-
-
-
-
-
-
+
+
+
+
+
+ Sep 2022: Self-Supervised Pre-Training at NeurIPS 2022
+
+
+
+
+
+
+
+
+ New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2022: Best Paper Honorable Mention Award at IEEE VIS
+
+
+
+
+
+
+
+
+ Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2022: Multimodal Representation Learning with Graphs
+
+
+
+
+
+
+
+
+ New preprint! We present the blueprint for graph-centric multimodal learning.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2022: On Graph AI for Precision Medicine
+
+
+
+
+
+
+
+
+ The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -1469,118 +1581,6 @@
-
-
-
-
-
- Apr 2021: Representation Learning for Biomedical Nets
-
-
-
-
-
-
-
-
- In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Mar 2021: Receiving Amazon Research Award
-
-
-
-
-
-
-
-
- We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Mar 2021: Michelle's Graduate Research Fellowship
-
-
-
-
-
-
-
-
- Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Mar 2021: Hot Off the Press: Multiscale Interactome
-
-
-
-
-
-
-
-
- Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.
-
-
-
-
-
-
-
-
-
-
-
-
+
+
+
+
+
+ Apr 2021: Representation Learning for Biomedical Nets
+
+
+
+
+
+
+
+
+ In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Mar 2021: Receiving Amazon Research Award
+
+
+
+
+
+
+
+
+ We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Mar 2021: Michelle's Graduate Research Fellowship
+
+
+
+
+
+
+
+
+ Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Mar 2021: Hot Off the Press: Multiscale Interactome
+
+
+
+
+
+
+
+
+ Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/people/index.html b/people/index.html
index ebda32f1..c705e47d 100644
--- a/people/index.html
+++ b/people/index.html
@@ -181,50 +181,21 @@
-
-
-
-
-
-
-
- 
Michelle M. Li" />
-
-
-
-
-
-
- PhD Student
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+
- 
Robert Calef" />
+ 
Kevin Li" />
-
- PhD Student
+
+ PhD Student
Harvard-MIT HST
@@ -253,7 +224,7 @@
- PhD Student
+ PhD Student
Harvard CCB
@@ -268,21 +239,21 @@
-
+
- Kevin Li" />
+ Robert Calef" />
-
- PhD Student
+
+ PhD Student
MIT EECS
@@ -311,7 +282,7 @@
- PhD Student
+ PhD Student
Harvard BIG
@@ -369,7 +340,7 @@
- PhD Student
+ PhD Student
Harvard BBS
@@ -500,20 +471,20 @@
-
+
- 
Ruth Johnson" />
+ Michelle M. Li" />
-
+
Postdoctoral Fellow
Harvard Berkowitz Fellow
@@ -529,21 +500,21 @@
-
+
- 
Shanghua Gao" />
+ Ruth Johnson" />
-
- Postdoctoral Fellow
+
+ Postdoctoral Fellow
Harvard Berkowitz Fellow
@@ -558,20 +529,20 @@
-
+
- 
Wanxiang Shen" />
+ Shanghua Gao" />
-
+
Postdoctoral Fellow
@@ -587,20 +558,20 @@
-
+
- 
Xiang Lin" />
+ Wanxiang Shen" />
-
+
Postdoctoral Fellow
@@ -616,20 +587,20 @@
-
+
- 
Xiaorui Su" />
+ Xiang Lin" />
-
+
Postdoctoral Fellow
@@ -645,21 +616,21 @@
-
+
- 
Alejandro Velez Arce" />
+ Xiaorui Su" />
-
- Research Associate
+
+ Postdoctoral Fellow
@@ -674,21 +645,21 @@
-
+
- 
Pengwei Sui" />
+ 
Ying Jin" />
-
- Research Associate
+
+ Postdoctoral Fellow
Harvard Data Science Fellow
@@ -703,20 +674,20 @@
-
+
- 
Owen Queen" />
+ Pengwei Sui" />
-
+
Research Associate
@@ -732,21 +703,21 @@
-
+
- 
Zaixi Zhang" />
+ Alejandro Velez Arce" />
-
- Visiting PhD Student
+
+ Research Associate
@@ -775,7 +746,7 @@
- Graduate Researcher
+ Research Associate
@@ -819,20 +790,20 @@
-
+
- 
Ayush Noori" />
+ 
Richard Zhu" />
-
+
Undergraduate Researcher
Harvard
@@ -848,20 +819,20 @@
-
+
- Richard Zhu" />
+ Ayush Noori" />
-
+
Undergraduate Researcher
Harvard
@@ -938,8 +909,6 @@ Associate members
-
-
@@ -951,6 +920,8 @@ Lab alumni
2024:
+ - Zaixi Zhang (Visiting PhD Student → Postdoctoral Fellow, Princeton University)
+ - Owen Queen (Research Associate → Computer Science PhD Student, Stanford University)
- Varun Ullanat (HMS)
- Ivy Liang (Harvard College)
- Tianlong Chen (Postdoctoral Fellow → Assistant Professor, University of North Carolina at Chapel Hill)
@@ -1012,6 +983,118 @@ Lab alumni
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -1466,126 +1549,6 @@ Lab alumni
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
-
-
-
-
-
-
-
-
- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
-
-
-
-
-
-
-
-
- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
-
-
-
-
-
-
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/postdoc-ML/index.html b/postdoc-ML/index.html
index ee3999c3..e4880c16 100644
--- a/postdoc-ML/index.html
+++ b/postdoc-ML/index.html
@@ -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 strong plus.
+Candidates must have a Ph.D. or equivalent degree in computer science. 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
@@ -191,6 +191,118 @@ Advisor
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
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+ Aug 2024: Graph AI in Medicine
+
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+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
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+ Aug 2024: How Proteins Behave in Context
+
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+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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@@ -645,126 +757,6 @@ Advisor
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
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- Jan 2024: Combinatorial Therapeutic Perturbations
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- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
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- Nov 2023: Next Generation of Therapeutics Commons
-
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- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
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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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diff --git a/postdoc-TDC/index.html b/postdoc-TDC/index.html
index 26741e11..03b660bf 100644
--- a/postdoc-TDC/index.html
+++ b/postdoc-TDC/index.html
@@ -203,6 +203,118 @@ Advisor
+
+
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+ Sep 2024: Three Papers Accepted to NeurIPS
+
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+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
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+ Sep 2024: TxGNN Published in Nature Medicine
+
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+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
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+ Aug 2024: Graph AI in Medicine
+
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+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
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+ Aug 2024: How Proteins Behave in Context
+
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+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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@@ -657,126 +769,6 @@ Advisor
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
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- Jan 2024: Combinatorial Therapeutic Perturbations
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- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
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- Nov 2023: Next Generation of Therapeutics Commons
-
-
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-
-
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- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
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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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diff --git a/postdoc-biomedicalAI-MGB/index.html b/postdoc-biomedicalAI-MGB/index.html
index 3b557a4b..9d60ca6e 100644
--- a/postdoc-biomedicalAI-MGB/index.html
+++ b/postdoc-biomedicalAI-MGB/index.html
@@ -180,6 +180,118 @@ Application process
+
+
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+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
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+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
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+ Sep 2024: TxGNN Published in Nature Medicine
+
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+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
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+
+
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+ Aug 2024: Graph AI in Medicine
+
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+
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+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
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+ Aug 2024: How Proteins Behave in Context
+
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+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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@@ -634,126 +746,6 @@ Application process
-
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
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- Jan 2024: Combinatorial Therapeutic Perturbations
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- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
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- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
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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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diff --git a/postdoc-cancerTxAI/index.html b/postdoc-cancerTxAI/index.html
index 72e971e8..669b5f13 100644
--- a/postdoc-cancerTxAI/index.html
+++ b/postdoc-cancerTxAI/index.html
@@ -189,6 +189,118 @@ Faculty and mentors
+
+
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+ Sep 2024: Three Papers Accepted to NeurIPS
+
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+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
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+ Sep 2024: TxGNN Published in Nature Medicine
+
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+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
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+ Aug 2024: Graph AI in Medicine
+
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+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
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+ Aug 2024: How Proteins Behave in Context
+
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+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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@@ -643,126 +755,6 @@ Faculty and mentors
-
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
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- Jan 2024: Combinatorial Therapeutic Perturbations
-
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- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
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-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
-
-
-
-
-
-
-
-
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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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diff --git a/postdoc-medicalAI/index.html b/postdoc-medicalAI/index.html
index 0dfd3f92..1fbb8f12 100644
--- a/postdoc-medicalAI/index.html
+++ b/postdoc-medicalAI/index.html
@@ -159,7 +159,7 @@ 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 medical 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 medical AI is a strong plus.
+Candidates must have a Ph.D. or equivalent degree in computer science. Strong programming skills and practical experience with leading machine learning frameworks are required. Experience and/or interest in medical AI is a strong plus.
Application process
@@ -196,6 +196,118 @@ Advisor
+
+
+
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+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
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+
+
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+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
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+ Sep 2024: TxGNN Published in Nature Medicine
+
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+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
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+
+
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+
+
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+ Aug 2024: Graph AI in Medicine
+
+
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+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
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+ Aug 2024: How Proteins Behave in Context
+
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+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
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@@ -650,126 +762,6 @@ Advisor
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
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- Jan 2024: Combinatorial Therapeutic Perturbations
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- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
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- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
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-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
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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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diff --git a/products/ada_fang/index.html b/products/ada_fang/index.html
index 0822d1a5..f9ffe265 100644
--- a/products/ada_fang/index.html
+++ b/products/ada_fang/index.html
@@ -23,14 +23,14 @@
-
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2021-01-07T14:36:27-05:00
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2018-12-24T08:23:20-05:00
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2018-07-15T00:38:37-04:00
diff --git a/software/index.html b/software/index.html
index ec87bd98..f3f0e3ed 100644
--- a/software/index.html
+++ b/software/index.html
@@ -273,7 +273,7 @@
UniTS
- Unified Time Series Model that Can Process Various Tasks Across Multiple Domains with Shared Parameters and Does Not Have any Task-Specific Modules
+ A Unified Multi-Task Time Series Model
@@ -390,7 +390,7 @@
TxGNN
- Zero-Shot Prediction of Therapeutic Use with Geometric Deep Learning and Clinician Centered Design
+ A Foundation Model for Clinician Centered Drug Repurposing
@@ -1743,6 +1743,118 @@
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
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+
+
+
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+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
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@@ -2197,126 +2309,6 @@
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
-
-
-
-
-
-
-
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- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
-
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- Jan 2024: Combinatorial Therapeutic Perturbations
-
-
-
-
-
-
-
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- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
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-
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-
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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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diff --git a/talks/index.html b/talks/index.html
index 52fd1ce3..612803f4 100644
--- a/talks/index.html
+++ b/talks/index.html
@@ -171,6 +171,11 @@
+ - 08/2024: MLHC 2024
Keynote
+ - 08/2024: ACL 2024
Invited Talk on Language + Molecules
+ - 07/2024: Genome Graph Symposium 2024
Keynote
+ - 06/2024: Menarini Conference
Invited Talk at AI and Network Medicine: Path to Precision Medicine
+ - 06/2024: Harvard Medicine
Alumni Day - AI in Medicine
- 06/2024: University of Pennsylvania
Biomedical Data Science Seminar Series
- 05/2024: Broad Institute Models, Inference & Algorithms (MIA)
Broad MIA Seminar
- 05/2024: The Web Conference
Invited Talk at Graph Foundation Models Workshop
+
+
+
+
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+ + +
+
+
+
+
+
+
+
+Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+ + +
+
+
+
+
+
+
+
+Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+ + +
+
+
+
+
+
+
+
+Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+ + + + + +
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+ + +
+
+
+
+
+
+
+
+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+ + + + + +
@@ -713,126 +825,6 @@
Visitors, interns, and short-t
-
-
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-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
- - -
-
-
-
-
-
-
-
-We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
- - - - - - -
-
-
-
-
-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
- - -
-
-
-
-
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-
-
-New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
- - - - - - -
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
- - -
-
-
-
-
-
-
-
-We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
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-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
- - -
-
-
-
-
-
-
-
-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.
- - - - - - -
diff --git a/meetings/index.html b/meetings/index.html
index d27a6356..12b193db 100644
--- a/meetings/index.html
+++ b/meetings/index.html
@@ -594,6 +594,118 @@ Biomedical Data Fusion (EMBC a
Biomedical Data Fusion (EMBC a
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
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+ Sep 2024: TxGNN Published in Nature Medicine
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+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
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+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
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+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
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+
+
+
+
+
+
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+
+
+
+
+
+
+
+
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+ Aug 2024: How Proteins Behave in Context
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+
+
+
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+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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+
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@@ -1048,126 +1160,6 @@ Biomedical Data Fusion (EMBC a
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
-
-
-
-
-
-
-
-
- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
-
-
-
-
-
-
-
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-
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-
- Jan 2024: Combinatorial Therapeutic Perturbations
-
-
-
-
-
-
-
-
- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
-
-
-
-
-
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-
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
-
-
-
-
-
-
-
-
- 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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diff --git a/news/index.html b/news/index.html
index b65ffa90..be051183 100644
--- a/news/index.html
+++ b/news/index.html
@@ -170,6 +170,118 @@
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -1510,118 +1622,6 @@
-
-
-
-
-
- Sep 2022: Self-Supervised Pre-Training at NeurIPS 2022
-
-
-
-
-
-
-
-
- New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Sep 2022: Best Paper Honorable Mention Award at IEEE VIS
-
-
-
-
-
-
-
-
- Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Sep 2022: Multimodal Representation Learning with Graphs
-
-
-
-
-
-
-
-
- New preprint! We present the blueprint for graph-centric multimodal learning.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Aug 2022: On Graph AI for Precision Medicine
-
-
-
-
-
-
-
-
- The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.
-
-
-
-
-
-
-
-
-
-
-
-
+
+
+
+
+
+ Sep 2022: Self-Supervised Pre-Training at NeurIPS 2022
+
+
+
+
+
+
+
+
+ New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2022: Best Paper Honorable Mention Award at IEEE VIS
+
+
+
+
+
+
+
+
+ Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2022: Multimodal Representation Learning with Graphs
+
+
+
+
+
+
+
+
+ New preprint! We present the blueprint for graph-centric multimodal learning.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2022: On Graph AI for Precision Medicine
+
+
+
+
+
+
+
+
+ The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -1469,118 +1581,6 @@
-
-
-
-
-
- Apr 2021: Representation Learning for Biomedical Nets
-
-
-
-
-
-
-
-
- In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Mar 2021: Receiving Amazon Research Award
-
-
-
-
-
-
-
-
- We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Mar 2021: Michelle's Graduate Research Fellowship
-
-
-
-
-
-
-
-
- Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Mar 2021: Hot Off the Press: Multiscale Interactome
-
-
-
-
-
-
-
-
- Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.
-
-
-
-
-
-
-
-
-
-
-
-
+
+
+
+
+
+ Apr 2021: Representation Learning for Biomedical Nets
+
+
+
+
+
+
+
+
+ In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Mar 2021: Receiving Amazon Research Award
+
+
+
+
+
+
+
+
+ We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Mar 2021: Michelle's Graduate Research Fellowship
+
+
+
+
+
+
+
+
+ Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Mar 2021: Hot Off the Press: Multiscale Interactome
+
+
+
+
+
+
+
+
+ Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/people/index.html b/people/index.html
index ebda32f1..c705e47d 100644
--- a/people/index.html
+++ b/people/index.html
@@ -181,50 +181,21 @@
-
-
-
-
-
-
-
- Michelle M. Li" />
-
-
-
-
-
-
- PhD Student
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+
- Robert Calef" />
+ Kevin Li" />
-
- PhD Student
+
+ PhD Student
Harvard-MIT HST
@@ -253,7 +224,7 @@
- PhD Student
+ PhD Student
Harvard CCB
@@ -268,21 +239,21 @@
-
+
- Kevin Li" />
+ Robert Calef" />
-
- PhD Student
+
+ PhD Student
MIT EECS
@@ -311,7 +282,7 @@
- PhD Student
+ PhD Student
Harvard BIG
@@ -369,7 +340,7 @@
- PhD Student
+ PhD Student
Harvard BBS
@@ -500,20 +471,20 @@
-
+
- Ruth Johnson" />
+ Michelle M. Li" />
-
+
Postdoctoral Fellow
Harvard Berkowitz Fellow
@@ -529,21 +500,21 @@
-
+
- Shanghua Gao" />
+ Ruth Johnson" />
-
- Postdoctoral Fellow
+
+ Postdoctoral Fellow
Harvard Berkowitz Fellow
@@ -558,20 +529,20 @@
-
+
- Wanxiang Shen" />
+ Shanghua Gao" />
-
+
Postdoctoral Fellow
@@ -587,20 +558,20 @@
-
+
- Xiang Lin" />
+ Wanxiang Shen" />
-
+
Postdoctoral Fellow
@@ -616,20 +587,20 @@
-
+
- Xiaorui Su" />
+ Xiang Lin" />
-
+
Postdoctoral Fellow
@@ -645,21 +616,21 @@
-
+
- Alejandro Velez Arce" />
+ Xiaorui Su" />
-
- Research Associate
+
+ Postdoctoral Fellow
@@ -674,21 +645,21 @@
-
+
- Pengwei Sui" />
+ Ying Jin" />
-
- Research Associate
+
+ Postdoctoral Fellow
Harvard Data Science Fellow
@@ -703,20 +674,20 @@
-
+
- Owen Queen" />
+ Pengwei Sui" />
-
+
Research Associate
@@ -732,21 +703,21 @@
-
+
- Zaixi Zhang" />
+ Alejandro Velez Arce" />
-
- Visiting PhD Student
+
+ Research Associate
@@ -775,7 +746,7 @@
- Graduate Researcher
+ Research Associate
@@ -819,20 +790,20 @@
-
+
- Ayush Noori" />
+ Richard Zhu" />
-
+
Undergraduate Researcher
Harvard
@@ -848,20 +819,20 @@
-
+
- Richard Zhu" />
+ Ayush Noori" />
-
+
Undergraduate Researcher
Harvard
@@ -938,8 +909,6 @@ Associate members
-
-
@@ -951,6 +920,8 @@ Lab alumni
2024:
+ - Zaixi Zhang (Visiting PhD Student → Postdoctoral Fellow, Princeton University)
+ - Owen Queen (Research Associate → Computer Science PhD Student, Stanford University)
- Varun Ullanat (HMS)
- Ivy Liang (Harvard College)
- Tianlong Chen (Postdoctoral Fellow → Assistant Professor, University of North Carolina at Chapel Hill)
@@ -1012,6 +983,118 @@ Lab alumni
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -1466,126 +1549,6 @@ Lab alumni
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
-
-
-
-
-
-
-
-
- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
-
-
-
-
-
-
-
-
- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
-
-
-
-
-
-
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/postdoc-ML/index.html b/postdoc-ML/index.html
index ee3999c3..e4880c16 100644
--- a/postdoc-ML/index.html
+++ b/postdoc-ML/index.html
@@ -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 strong plus.
+Candidates must have a Ph.D. or equivalent degree in computer science. 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
@@ -191,6 +191,118 @@ Advisor
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -645,126 +757,6 @@ Advisor
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
-
-
-
-
-
-
-
-
- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
-
-
-
-
-
-
-
-
- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
-
-
-
-
-
-
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/postdoc-TDC/index.html b/postdoc-TDC/index.html
index 26741e11..03b660bf 100644
--- a/postdoc-TDC/index.html
+++ b/postdoc-TDC/index.html
@@ -203,6 +203,118 @@ Advisor
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -657,126 +769,6 @@ Advisor
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
-
-
-
-
-
-
-
-
- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
-
-
-
-
-
-
-
-
- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
-
-
-
-
-
-
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/postdoc-biomedicalAI-MGB/index.html b/postdoc-biomedicalAI-MGB/index.html
index 3b557a4b..9d60ca6e 100644
--- a/postdoc-biomedicalAI-MGB/index.html
+++ b/postdoc-biomedicalAI-MGB/index.html
@@ -180,6 +180,118 @@ Application process
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
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- Jan 2024: Combinatorial Therapeutic Perturbations
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- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
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- Nov 2023: Next Generation of Therapeutics Commons
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- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
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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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diff --git a/postdoc-cancerTxAI/index.html b/postdoc-cancerTxAI/index.html
index 72e971e8..669b5f13 100644
--- a/postdoc-cancerTxAI/index.html
+++ b/postdoc-cancerTxAI/index.html
@@ -189,6 +189,118 @@ Faculty and mentors
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+ Sep 2024: Three Papers Accepted to NeurIPS
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+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
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+ Sep 2024: TxGNN Published in Nature Medicine
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+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
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+ Aug 2024: Graph AI in Medicine
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+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
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+ Aug 2024: How Proteins Behave in Context
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+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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@@ -643,126 +755,6 @@ Faculty and mentors
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
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- Jan 2024: Combinatorial Therapeutic Perturbations
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- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
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- Nov 2023: Next Generation of Therapeutics Commons
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- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
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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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diff --git a/postdoc-medicalAI/index.html b/postdoc-medicalAI/index.html
index 0dfd3f92..1fbb8f12 100644
--- a/postdoc-medicalAI/index.html
+++ b/postdoc-medicalAI/index.html
@@ -159,7 +159,7 @@ 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 medical 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 medical AI is a strong plus.
+Candidates must have a Ph.D. or equivalent degree in computer science. Strong programming skills and practical experience with leading machine learning frameworks are required. Experience and/or interest in medical AI is a strong plus.
Application process
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+ Sep 2024: Three Papers Accepted to NeurIPS
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+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
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+ Sep 2024: TxGNN Published in Nature Medicine
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+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
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+ Aug 2024: Graph AI in Medicine
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+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
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+ Aug 2024: How Proteins Behave in Context
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+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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@@ -650,126 +762,6 @@ Advisor
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
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- Jan 2024: Combinatorial Therapeutic Perturbations
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- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
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- Nov 2023: Next Generation of Therapeutics Commons
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- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
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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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diff --git a/products/ada_fang/index.html b/products/ada_fang/index.html
index 0822d1a5..f9ffe265 100644
--- a/products/ada_fang/index.html
+++ b/products/ada_fang/index.html
@@ -23,14 +23,14 @@
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-Ivy Liang | Zitnik Lab
+Ying Jin | Zitnik Lab
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diff --git a/sitemap.xml b/sitemap.xml
index 0b47d371..1b133178 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -545,104 +545,116 @@
2024-07-27T00:00:00-04:00
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+https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine/
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+
+
+https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers/
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https://zitniklab.hms.harvard.edu/products/ada_fang/
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https://zitniklab.hms.harvard.edu/products/michelle_dai/
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https://zitniklab.hms.harvard.edu/products/pengwei_sui/
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https://zitniklab.hms.harvard.edu/products/richard_zhu/
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https://zitniklab.hms.harvard.edu/products/yepeng_huang/
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-https://zitniklab.hms.harvard.edu/products/zaixi_zhang/
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+https://zitniklab.hms.harvard.edu/products/ying_jin/
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https://zitniklab.hms.harvard.edu/products/zhenglun_kong/
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https://zitniklab.hms.harvard.edu/404/
@@ -786,10 +798,6 @@
https://zitniklab.hms.harvard.edu/news/page3/
-https://zitniklab.hms.harvard.edu/docs/marinka_zitnik_CV.pdf
-2022-02-19T16:11:26-05:00
-
-
https://zitniklab.hms.harvard.edu/docs/slides/IJCAI-T27-000-intro.pdf
2021-01-07T14:36:27-05:00
@@ -1018,6 +1026,10 @@
2018-12-24T08:23:20-05:00
+https://zitniklab.hms.harvard.edu/publications/papers/graphAIMedicine24.pdf
+2024-08-29T07:46:20-04:00
+
+
https://zitniklab.hms.harvard.edu/publications/papers/graphwave-kdd18.pdf
2018-07-15T00:38:37-04:00
diff --git a/software/index.html b/software/index.html
index ec87bd98..f3f0e3ed 100644
--- a/software/index.html
+++ b/software/index.html
@@ -273,7 +273,7 @@
UniTS
- Unified Time Series Model that Can Process Various Tasks Across Multiple Domains with Shared Parameters and Does Not Have any Task-Specific Modules
+ A Unified Multi-Task Time Series Model
@@ -390,7 +390,7 @@
TxGNN
- Zero-Shot Prediction of Therapeutic Use with Geometric Deep Learning and Clinician Centered Design
+ A Foundation Model for Clinician Centered Drug Repurposing
@@ -1743,6 +1743,118 @@
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+
+
+
+
+
+
+
+
+ Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+
+
+
+
+
+
+
+
+ Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+
+
+
+
+
+
+
+
+ Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+
+
+
+
+
+
+
+
+ Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+
+
+
+
+
+
+
+
+
+
+
+
@@ -2197,126 +2309,6 @@
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
-
-
-
-
-
-
-
-
- We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
-
-
-
-
-
-
-
-
- New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
-
-
-
-
-
-
-
-
- We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
-
-
-
-
-
-
-
-
- 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.
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/talks/index.html b/talks/index.html
index 52fd1ce3..612803f4 100644
--- a/talks/index.html
+++ b/talks/index.html
@@ -171,6 +171,11 @@
+ - 08/2024: MLHC 2024
Keynote
+ - 08/2024: ACL 2024
Invited Talk on Language + Molecules
+ - 07/2024: Genome Graph Symposium 2024
Keynote
+ - 06/2024: Menarini Conference
Invited Talk at AI and Network Medicine: Path to Precision Medicine
+ - 06/2024: Harvard Medicine
Alumni Day - AI in Medicine
- 06/2024: University of Pennsylvania
Biomedical Data Science Seminar Series
- 05/2024: Broad Institute Models, Inference & Algorithms (MIA)
Broad MIA Seminar
- 05/2024: The Web Conference
Invited Talk at Graph Foundation Models Workshop
+
+
+
+
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+ + +
+
+
+
+
+
+
+
+Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+ + +
+
+
+
+
+
+
+
+Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+ + +
+
+
+
+
+
+
+
+Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+ + + + + +
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+ + +
+
+
+
+
+
+
+
+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+ + + + + +
@@ -1048,126 +1160,6 @@
Biomedical Data Fusion (EMBC a
-
-
-
-
-
-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
- - -
-
-
-
-
-
-
-
-We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
- - - - - - -
-
-
-
-
-
-
-
-
-
- Jan 2024: Combinatorial Therapeutic Perturbations
- - -
-
-
-
-
-
-
-
-New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
- - - - - - -
-
-
-
-
-
-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
- - -
-
-
-
-
-
-
-
-We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
- - - - - - -
-
-
-
-
-
-
-
-
- Oct 2023: Structure-Based Drug Design
- - -
-
-
-
-
-
-
-
-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.
- - - - - - -
diff --git a/news/index.html b/news/index.html
index b65ffa90..be051183 100644
--- a/news/index.html
+++ b/news/index.html
@@ -170,6 +170,118 @@
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+ + +
+
+
+
+
+
+
+
+Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+ + +
+
+
+
+
+
+
+
+Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+ + +
+
+
+
+
+
+
+
+Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+ + + + + +
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+ + +
+
+
+
+
+
+
+
+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+ + + + + +
@@ -1510,118 +1622,6 @@
-
-
-
-
-
-
-
-
-
- Sep 2022: Self-Supervised Pre-Training at NeurIPS 2022
- - -
-
-
-
-
-
-
-
-New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.
- - - - - -
-
-
-
-
-
-
-
-
-
- Sep 2022: Best Paper Honorable Mention Award at IEEE VIS
- - -
-
-
-
-
-
-
-
-Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.
- - - - - -
-
-
-
-
-
-
-
-
-
- Sep 2022: Multimodal Representation Learning with Graphs
- - -
-
-
-
-
-
-
-
-New preprint! We present the blueprint for graph-centric multimodal learning.
- - - - - -
-
-
-
-
-
-
-
-
- Aug 2022: On Graph AI for Precision Medicine
- - -
-
-
-
-
-
-
-
-The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.
- - - - - -
+
+
+
+
+
+
+
+
+
+ Sep 2022: Self-Supervised Pre-Training at NeurIPS 2022
+ + +
+
+
+
+
+
+
+
+New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Sep 2022: Best Paper Honorable Mention Award at IEEE VIS
+ + +
+
+
+
+
+
+
+
+Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Sep 2022: Multimodal Representation Learning with Graphs
+ + +
+
+
+
+
+
+
+
+New preprint! We present the blueprint for graph-centric multimodal learning.
+ + + + + +
+
+
+
+
+
+
+
+
+ Aug 2022: On Graph AI for Precision Medicine
+ + +
+
+
+
+
+
+
+
+The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.
+ + + + + +
@@ -1469,118 +1581,6 @@
-
-
-
-
-
-
-
-
-
- Apr 2021: Representation Learning for Biomedical Nets
- - -
-
-
-
-
-
-
-
-In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.
- - - - - -
-
-
-
-
-
-
-
-
-
- Mar 2021: Receiving Amazon Research Award
- - -
-
-
-
-
-
-
-
-We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.
- - - - - -
-
-
-
-
-
-
-
-
-
- Mar 2021: Michelle's Graduate Research Fellowship
- - -
-
-
-
-
-
-
-
-Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!
- - - - - -
-
-
-
-
-
-
-
-
- Mar 2021: Hot Off the Press: Multiscale Interactome
- - -
-
-
-
-
-
-
-
-Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.
- - - - - -
+
+
+
+
+
+
+
+
+
+ Apr 2021: Representation Learning for Biomedical Nets
+ + +
+
+
+
+
+
+
+
+In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Mar 2021: Receiving Amazon Research Award
+ + +
+
+
+
+
+
+
+
+We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Mar 2021: Michelle's Graduate Research Fellowship
+ + +
+
+
+
+
+
+
+
+Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!
+ + + + + +
+
+
+
+
+
+
+
+
+ Mar 2021: Hot Off the Press: Multiscale Interactome
+ + +
+
+
+
+
+
+
+
+Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.
+ + + + + +
diff --git a/people/index.html b/people/index.html
index ebda32f1..c705e47d 100644
--- a/people/index.html
+++ b/people/index.html
@@ -181,50 +181,21 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Michelle M. Li" />
-
-
-
- Michelle M. Li" />
-
-
-
-
- PhD Student
-
-
+
- Robert Calef" />
+ Kevin Li" />
Robert Calef" />
+ Kevin Li" />
-
-
PhD Student
PhD Student
Harvard-MIT HST
-
PhD Student
PhD Student
Harvard CCB
-
+
- Kevin Li" />
+ Robert Calef" />
Kevin Li" />
+ Robert Calef" />
-
-
PhD Student
PhD Student
MIT EECS
-
PhD Student
PhD Student
Harvard BIG
-
PhD Student
PhD Student
Harvard BBS
-
+
- Ruth Johnson" />
+ Michelle M. Li" />
Ruth Johnson" />
+ Michelle M. Li" />
-
+
Postdoctoral Fellow
Harvard Berkowitz Fellow
-
+
- Shanghua Gao" />
+ Ruth Johnson" />
Shanghua Gao" />
+ Ruth Johnson" />
-
-
Postdoctoral Fellow
Postdoctoral Fellow
Harvard Berkowitz Fellow
-
+
- Wanxiang Shen" />
+ Shanghua Gao" />
Wanxiang Shen" />
+ Shanghua Gao" />
-
+
Postdoctoral Fellow
-
+
- Xiang Lin" />
+ Wanxiang Shen" />
Xiang Lin" />
+ Wanxiang Shen" />
-
+
Postdoctoral Fellow
-
+
- Xiaorui Su" />
+ Xiang Lin" />
Xiaorui Su" />
+ Xiang Lin" />
-
+
Postdoctoral Fellow
-
+
- Alejandro Velez Arce" />
+ Xiaorui Su" />
Alejandro Velez Arce" />
+ Xiaorui Su" />
-
-
Research Associate
Postdoctoral Fellow
-
+
- Pengwei Sui" />
+ Ying Jin" />
Pengwei Sui" />
+ Ying Jin" />
-
-
Research Associate
Postdoctoral Fellow
Harvard Data Science Fellow
-
+
- Owen Queen" />
+ Pengwei Sui" />
Owen Queen" />
+ Pengwei Sui" />
-
+
Research Associate
-
+
- Zaixi Zhang" />
+ Alejandro Velez Arce" />
Zaixi Zhang" />
+ Alejandro Velez Arce" />
-
-
Visiting PhD Student
Research Associate
-
Graduate Researcher
Research Associate
-
+
- Ayush Noori" />
+ Richard Zhu" />
Ayush Noori" />
+ Richard Zhu" />
-
+
Undergraduate Researcher
Harvard
-
+
- Richard Zhu" />
+ Ayush Noori" />
Richard Zhu" />
+ Ayush Noori" />
-
+
Undergraduate Researcher
Harvard
Associate members
- - @@ -951,6 +920,8 @@Lab alumni
2024:-
+
- Zaixi Zhang (Visiting PhD Student → Postdoctoral Fellow, Princeton University) +
- Owen Queen (Research Associate → Computer Science PhD Student, Stanford University)
- Varun Ullanat (HMS)
- Ivy Liang (Harvard College)
- Tianlong Chen (Postdoctoral Fellow → Assistant Professor, University of North Carolina at Chapel Hill) @@ -1012,6 +983,118 @@
- 08/2024: MLHC 2024
Keynote
+ - 08/2024: ACL 2024
Invited Talk on Language + Molecules
+ - 07/2024: Genome Graph Symposium 2024
Keynote
+ - 06/2024: Menarini Conference
Invited Talk at AI and Network Medicine: Path to Precision Medicine
+ - 06/2024: Harvard Medicine
Alumni Day - AI in Medicine - 06/2024: University of Pennsylvania
Biomedical Data Science Seminar Series - 05/2024: Broad Institute Models, Inference & Algorithms (MIA)
Broad MIA Seminar - 05/2024: The Web Conference
Invited Talk at Graph Foundation Models Workshop
Lab alumni
+
-
+
+
+
+
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+ + +
+
+
+
+
+
+
+
+Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+ + +
+
+
+
+
+
+
+
+Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+ + +
+
+
+
+
+
+
+
+Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+ + + + + +
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+ + +
+
+
+
+
+
+
+
+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+ + + + + +
@@ -1466,126 +1549,6 @@
Lab alumni
-
-
-
-
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-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
- - -
-
-
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-
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-We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
- - - - - - -
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-
-
-
-
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-
-
-
-
-
-
-
-New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
- - - - - - -
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-
-
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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.
- - - - - - -
diff --git a/postdoc-ML/index.html b/postdoc-ML/index.html
index ee3999c3..e4880c16 100644
--- a/postdoc-ML/index.html
+++ b/postdoc-ML/index.html
@@ -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 strong plus.
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Application process
@@ -191,6 +191,118 @@Advisor
+
-
+
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+
+
+
+
+
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+ Sep 2024: Three Papers Accepted to NeurIPS
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+
+
+
+
+
+Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+ + + + + +
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+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
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+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
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+
+
+
+
+
+
+
+Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
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+
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+ Aug 2024: How Proteins Behave in Context
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Advisor
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-
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index 26741e11..03b660bf 100644
--- a/postdoc-TDC/index.html
+++ b/postdoc-TDC/index.html
@@ -203,6 +203,118 @@
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+
+
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+
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+
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+
+
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+
+
+ Aug 2024: How Proteins Behave in Context
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+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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@@ -657,126 +769,6 @@
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-
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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@@ -180,6 +180,118 @@
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+
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+
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+
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+ Sep 2024: TxGNN Published in Nature Medicine
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+Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
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+ Aug 2024: Graph AI in Medicine
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+
+
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+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+ + +
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+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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@@ -634,126 +746,6 @@
Application process
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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-
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-
-
-
-
- Nov 2023: Next Generation of Therapeutics Commons
- - -
-
-
-
-
-
-
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-We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
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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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index 72e971e8..669b5f13 100644
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2018-07-15T00:38:37-04:00
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index ec87bd98..f3f0e3ed 100644
--- a/software/index.html
+++ b/software/index.html
@@ -273,7 +273,7 @@
Faculty and mentors
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-
diff --git a/sitemap.xml b/sitemap.xml
index 0b47d371..1b133178 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -545,104 +545,116 @@
+
+
+
+
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+ + +
+
+
+
+
+
+
+
+Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+ + +
+
+
+
+
+
+
+
+Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+ + + + + +
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+
+
+
+
+
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+
+
+ Aug 2024: Graph AI in Medicine
+ + +
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+
+
+
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+
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+Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+ + + + + +
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
+ + +
+
+
+
+
+
+
+
+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
+ + + + + +
@@ -643,126 +755,6 @@
Faculty and mentors
-
-
-
-
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-
-
-
-
- Jan 2024: AI's Prospects in Nature Machine Intelligence
- - -
-
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-
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- - - - - - -
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-
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-New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
- - - - - - -
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-
-
-
-
-
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- Nov 2023: Next Generation of Therapeutics Commons
- - -
-
-
-
-
-
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-We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
- - - - - - -
-
-
-
-
-
-
-
-
- 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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diff --git a/postdoc-medicalAI/index.html b/postdoc-medicalAI/index.html
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--- a/postdoc-medicalAI/index.html
+++ b/postdoc-medicalAI/index.html
@@ -159,7 +159,7 @@
+
+
+
+
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 medical 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 medical AI is a strong plus.
+Candidates must have a Ph.D. or equivalent degree in computer science. Strong programming skills and practical experience with leading machine learning frameworks are required. Experience and/or interest in medical AI is a strong plus.
Application process
@@ -196,6 +196,118 @@Advisor
+
-
+
+
+
+
+
+
+
+
+
+
+
+ Sep 2024: Three Papers Accepted to NeurIPS
+ + +
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+
+
+
+
+
+
+Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Sep 2024: TxGNN Published in Nature Medicine
+ + +
+
+
+
+
+
+
+
+Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
+ + + + + +
+
+
+
+
+
+
+
+
+
+ Aug 2024: Graph AI in Medicine
+ + +
+
+
+
+
+
+
+
+Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
+ + + + + +
+
+
+
+
+
+
+
+
+ Aug 2024: How Proteins Behave in Context
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+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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Advisor
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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-New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
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- Nov 2023: Next Generation of Therapeutics Commons
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-We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
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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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diff --git a/products/ada_fang/index.html b/products/ada_fang/index.html
index 0822d1a5..f9ffe265 100644
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-Ivy Liang | Zitnik Lab
+Ying Jin | Zitnik Lab
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+{"image":"https://zitniklab.hms.harvard.edu/img/ying_jin.png","url":"https://zitniklab.hms.harvard.edu/products/ying_jin/","mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/ying_jin/"},"author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Ying Jin","dateModified":"2024-09-27T23:26:33-04:00","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","datePublished":"2024-09-27T23:26:33-04:00","@type":"BlogPosting","@context":"https://schema.org"}
+{"image":"https://zitniklab.hms.harvard.edu/img/zaixi_zhang.png","url":"https://zitniklab.hms.harvard.edu/products/zaixi_zhang/","mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/zaixi_zhang/"},"author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Zaixi Zhang","dateModified":"2024-08-29T08:06:14-04:00","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","datePublished":"2024-08-29T08:06:14-04:00","@type":"BlogPosting","@context":"https://schema.org"}
+{"image":"https://zitniklab.hms.harvard.edu/img/zhenglun_kong.png","url":"https://zitniklab.hms.harvard.edu/products/zhenglun_kong/","mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/zhenglun_kong/"},"author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Zhenglun Kong","dateModified":"2024-09-27T23:26:33-04:00","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","datePublished":"2024-09-27T23:26:33-04:00","@type":"BlogPosting","@context":"https://schema.org"}
+
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+ UniTS
-Unified Time Series Model that Can Process Various Tasks Across Multiple Domains with Shared Parameters and Does Not Have any Task-Specific Modules
+A Unified Multi-Task Time Series Model
@@ -390,7 +390,7 @@TxGNN
-Zero-Shot Prediction of Therapeutic Use with Geometric Deep Learning and Clinician Centered Design
+A Foundation Model for Clinician Centered Drug Repurposing
@@ -1743,6 +1743,118 @@
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+ Sep 2024: Three Papers Accepted to NeurIPS
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+Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.
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+ Sep 2024: TxGNN Published in Nature Medicine
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+Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes]
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+ Aug 2024: Graph AI in Medicine
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+Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.
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+ Aug 2024: How Proteins Behave in Context
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+Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.
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@@ -2197,126 +2309,6 @@
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- Jan 2024: AI's Prospects in Nature Machine Intelligence
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-We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.
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- Jan 2024: Combinatorial Therapeutic Perturbations
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-New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.
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- Nov 2023: Next Generation of Therapeutics Commons
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-We are building the next generation of Therapeutics Commons! We are seeking outstanding fellows who will lead AI research to advance molecular drug design and clinical drug development.
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-
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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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diff --git a/talks/index.html b/talks/index.html
index 52fd1ce3..612803f4 100644
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