Congrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!

New paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.

Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological & Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.

An enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow.

New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.

New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.

New paper with NASA in Nature Machine Intelligence on biological research and self-driving labs in deep space supported by artificial intelligence.

New study on zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design. Check out our project website and TxGNN Explorer.

Our approach evaluating explainability of geometric deep learning models is published in Scientific Data. Project website.

A warm welcome to postdoctoral fellows Wanxiang Shen and Ruth Johnson. Congratulations to Ruthie for being named a Berkowitz Fellow.

New preprint on domain adaptation for time series under feature and label shifts. Project website.

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

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

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

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

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

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

Our paper on artificial intelligence foundation for therapeutic science is published in Nature Chemical Biology.

New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.

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.

New preprint! We present the blueprint for graph-centric multimodal learning.

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.

New preprint! We introduce a resource for broad evaluation of the quality and reliability of GNN explanations, addressing challenges and providing solutions for GNN explainability. Project website.

Excited to organize the New Frontiers in Graph Learning workshop at NeurIPS.

We are excited to host the AI4Science meeting at NeurIPS discussing AI-driven scientific discovery, implementation and verification of AI in science, the influence AI has on the conduct of science, and more.

Check out our half-day tutorial with resources on methods and applications in graph representation learning for precision medicine.

Welcoming a research fellow Julia Balla and three Summer students, Nicholas Ho, Satvik Tripathi, and Isuru Herath.

Excited to share a preprint on self-supervised method for pre-training. Project website with evaluation on eight datasets, including electrodiagnostic testing, human daily activity recognition, and health state monitoring.

Excited to welcome George Dasoulas and Huan He, new postdocs joining us this Summer.

Congratulations to George Dasoulas, our incoming postdoctoral fellow, on being named the 2022 Wojcicki Troper HDSI Postdoctoral Fellow. We are delighted to welcome George in our group.

New preprint on building knowledge graphs to enable precision medicine applications.

We created PrimeKG, a precision medicine-oriented knowledge graph to support graph AI analyses in medicine.

Webster is on the cover of April issue of Cell Systems. Webster uses cell viability changes following gene perturbation to automatically learn cellular functions and pathways from data.

Dr. Zitnik will serve on the Science Working Group at NASA Space Biology.

Yasha won the National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations!

We are excited to be selected to organize the AI4Science meeting at ICML 2022. Stay tuned for details. http://www.ai4science.net/icml22

Excited to be organizing a session on Graph Algorithms at PSB 2023. Stay tuned for details.

New preprint! We introduce REMAP, a multimodal AI approach for disease relation extraction and classification. Project website.

Owen has been selected to present our research on explainable biomedical AI to members of the US Congress at the “Posters on the Hill” symposium. Congrats Owen!

Hot off the press at ICLR 2022. Check out Raindrop, our graph neural network with unique predictive capability to learn from irregular time series. Project website.

Excited to present a tutorial at ISMB 2022 on graph representation learning for precision medicine. Congratulations, Michelle!

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

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

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

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

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

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

Submit your papers to our Workshop on Graph Learning Benchmarks organized at The Web Conference (WWW). Call for papers!

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth independence. Delighted to be working with NASA and can share our recommendations!

As space exploration is extended beyond low Earth orbit, experiments must be maximally autonomous and intelligent to guide biological research. Excited to be working with NASA and can share our recommendations!