By popular demand we are releasing lecture videos for Stanford CS224W Machine Learning with Graphs which focuses on graph representation learning. Two new lectures every week. Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

Making tabular machine learning easy with PyToch Frame. Check it out!

🎉 Excited and honored to receive the #SIGKDD Innovation Award for my contributions to #GraphMining, #NetworkScience, and applied #MachineLearning! Looking forward to advancing these fields even further! 🚀 I am grateful for all the amazing students and collaborators. Thank you to all my mentors and everyone who supported me on this journey! 🙌 #KDD2023 @kdd_news

Stanford CS224W Machine Learning with Graphs. Lectures 2 and 3 posted! Feature-based learning (Motifs, Graphlets, Graph Kernels) and Node Embeddings (DeepWalk, Node2Vec, Anonymous Walks). Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

Stanford is proud to bring together leaders from academia&industry to showcase advances in Graph Neural Networks. Program includes applications, frameworks and industry panels on challenges of graph-based machine learning models. Register at: stanford.io/3BUbjjL

Ever wondered how much stay-at-home slows down COVID-19? In our latest @nature paper we use smartphone data to model mobility of 98 million people and detect hotspots, track COVID-19, and guide reopening strategies. Try the model at covid-mobility.stanford.edu

Slides from my Stanford Graph Learning workshop showcasing recent advancements in Graph ML and new developments in PyG: i.stanford.edu/~jure/pub/tal… @PyG_team

How powerful are Graph Neural Networks? Slides from my talk at ITA workshop in San Diego. i.stanford.edu/~jure/pub/tal…

Excited to announce 2nd Stanford Graph Learning Workshop on Wed Sept 28th with leaders from academia and industry to showcase recent advances of Graph Representation Learning across a wide range of applications. Program & free registration: snap.stanford.edu/graphlearn…

Excited to share our latest research: The myth of cosmopolitan cities: Why large urban areas are more segregated Long-standing assumption is that large cities with their diverse population, foster diverse person-to-person interactions. Our @Nature paper shows the opposite is true! A person in a big city encounters less diverse individuals, than a person in a small city. In other words, large cities foster segregation. We show this by analyzing 1.6 billion person-to-person encounters among 10 million people across 382 cities in the U.S. Paper: nature.com/articles/d41586-0… Website: segregation.stanford.edu/

🌟 Excited to announce the Stanford Graph Learning Workshop 2023 on Oct 24 2023!🤝 Bringing together academia & industry leaders to delve into advances in #MachineLearning & #AI in Relational domains, Foundation models, and Multimodal AI. 📢 Free registration. 📢 Calling for talks & poster/demo submissions! Showcase your innovative work in methodological advancements, diverse domain applications, & ML frameworks. snap.stanford.edu/graphlearn…

Hvala lepa @JozeMozina za vabilo v oddajo #Intervju in za prijeten pogovor. Na sporedu danes ob 21:45 na Prvem programu @RTV_Slovenija.

Stanford CS224W Machine Learning with Graphs. Lectures 7 & 8 posted! Design space for Graph Neural Networks, Graph Augmentation, and training of GNNs. Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

Stanford CS224W Machine Learning with Graphs. Lecture 6 posted! Graph Neural Networks and Deep Learning for Graphs. Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

We continue to release Stanford CS224W Machine Learning with Graphs lecture videos. Lecture 9 posted: How Expressive are GNNs and How to design the most powerful GNN. Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

Relational Deep Learning is brings the power of Graph Representation Learning to a Relational Database. Slides from my keynote at @LogConference yesterday: drive.google.com/file/d/1Uk1…

Over the next few days I will be tweeting about amazing graph machine learning projects coming out of the Stanford's CS224W class on Machine Learning with Graphs: cs224w.stanford.edu

📢 Exciting News! Our latest paper is now out in Nature Biotech 🌱🧬 We developed GEARS---an AI method to predict cellular responses to genetic perturbation. 🧪🔬 🔗 Link to the paper: nature.com/articles/s41587-0… 🧬 Unraveling genetic interactions in cancer, regenerative medicine, and more is a complex puzzle. GEARS harnesses the power of deep learning and a gene relationship knowledge graph to predict transcriptional responses to single and multigene perturbations using single-cell RNA-sequencing data. 🧠📊 🧪 What's unique about GEARS? It can predict outcomes for gene combinations that have never been experimentally perturbed before. 🚀 🔍 In a combinatorial perturbation screen, GEARS displayed a 40% higher accuracy compared to existing approaches. It identified distinct genetic interaction subtypes with remarkable accuracy and pinpointed the strongest interactions twice as effectively as previous approaches. 💥 📈 With GEARS, we're opening up new avenues for designing perturbational experiments and gaining deeper insights into the diverse effects of multigene perturbations. 🛠️ Join work with amazing @yusufroohani and @KexinHuang5.

Slides and videos from the Stanford Graph Learning Workshop, which brought together leaders from academia and industry to showcase recent advances of Graph Representation Learning across a wide range of applications. snap.stanford.edu/graphlearn…

Perspective: Netflix: $25.6B Tesla: $23.7B Macy's: $19.7B WhatsApp: $19B Whole Foods: $19B Gap: $18.9B Sony: $17.7B United Airlines: $15.7B

Stanford CS224W Machine Learning with Graphs. Lectures 4 and 5 posted: Message passing, relational and iterative classification, PageRank. Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

Reasoning in Knowledge Graphs using Embeddings. The lecture from Stanford CS224W: Machine Learning with Graphs just released at: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

Stanford Graph Learning Workshop is in 2 days (Wed Sept 28th). We will announce exciting advances in research, tools&platforms and the @PyG_Team ecosystem. We have an exciting program with talks from brightest minds in science and industry. Register at: snap.stanford.edu/graphlearn…

Relational Deep Learning is bringing the power of representation learning to relational databases and data warehouses. This breakthrough technology offers lots of opportunity for impact and raises exciting new research questions. Check out the paper here: relbench.stanford.edu/paper.… and benchmark datasets and code here: relbench.stanford.edu #RelationalDeepLearning #RepresentationLearning #AI #Research

Deep Generative Models for Graphs. Lecture 15 of Stanford CS224W Machine Learning with Graphs course just released! Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

Deep Generative Models for Graphs: Methods & Applications. Slides from my talk at #ICLR2019 workshop on Representation Learning on Graphs and Manifolds. i.stanford.edu/~jure/pub/tal…

Very excited to announce that we have adapted CS224W into a Stanford professional certificate course, XCS224W! The class will begin on September 13, 2021. Thanks @stanfordonline for helping us launch this! Enrollment + more details at: online.stanford.edu/courses/…

Personal update: After 7 amazing years, I will leave my chief scientist role at @PinterestEng and transition to be an advisor. Proud of what we have accomplished with the team: many AI products, solutions, and deployments. I will miss the many wonderful people across the company

🚀 I’m excited to announce Stanford Graph Learning Workshop 2025, happening Tuesday, October 14, 2025 at @Stanford University (with online livestream). Free registration! Submit a talk/poster. 📍 This year’s workshop will spotlight three fast-moving frontiers in AI & data science: -- Agents — Autonomous systems reshaping how we interact with tech -- Relational Foundation Models — Unlocking structure and meaning in complex data -- Fast LLM Inference — Pushing the boundaries of speed & scalability for large language models We’re bringing together researchers, innovators, and practitioners for a full day of cutting-edge talks, interactive sessions, and collaborative discussions. Whether you’re working in industry, academia, or startup land, there will be something to spark your curiosity and drive your work forward. 🔍 Want to share your work? We have a Call for Contributed Talks and Posters/Demos open now. ✅ Register now (free): snap.stanford.edu/graphlearn… 📅 Save the date: Oct 14, 2025

Computer Science is now the most popular major for women at @Stanford.

📢 New research alert! 💡 We've developed PRODIGY: pretraining framework for in-context learning over graphs. Through a novel prompt graph representation and a family of in-context pretraining objectives, our model can adapt to novel tasks on unseen graphs 📈. Outperforming contrastive pretraining baselines by 18% and standard finetuning with limited data by 33% on average, PRODIGY proves its strength in citation networks and knowledge graphs. Read on: arxiv.org/pdf/2305.12600.pdf #AIResearch #GraphLearning #PRODIGY #InContextLearning Joint work with @qhwang3 @ren_hongyu PengChen GregorKrzmanc DanielZheng and @percyliang

We excited to announce OGB-LSC at KDD Cup 2021: A Large-Scale Challenge for Machine Learning on Graphs. Competition ends until June 8. Looking forward to your participation! @kdd_news ogb.stanford.edu/kddcup2021

I am excited to share the knowledge about Machine Learning to Graphs beyond @Stanford. The online course starts on November 6th.

Excited to share our collaboration with @GoogleAI: SMORE is a scalable knowledge graph completion and multi-hop reasoning system that scales to hundreds of millions of entities and relations. @ren_hongyu, @hanjundai, et al. arxiv.org/abs/2110.14890 github.com/google-research/s…

We are excited to announce PyG <PyG.org>, a major new release of Pytorch Geometric-- a collaboration between Stanford and TU Dortmund. To learn more about PyG come to the Stanford Graph Learning workshop: snap.stanford.edu/graphlearn…

The Stanford Graph Learning Workshop 2025 videos are now live! 🎥 Watch all talks! links below 👇 This year’s themes: 🧠 Agents 🔗 Relational Foundation Models ⚡ Fast LLM Inference Explore the frontiers of AI & data science with top researchers and innovators. snap.stanford.edu/graphlearn…

Towards Universal Cell Embeddings. My talk at the @broadinstitute about our recent advancements in ML for biomedicine: i.stanford.edu/~jure/pub/tal… Thank you @CarolineUhler for hosting!

Announcing Biomni — the first general-purpose biomedical AI agent. Biomni is a free web platform where biomedical scientists can immediately delegate their tasks to Biomni, starting today! Biomni automates literature reviews, hypothesis generation, protocol design, bioinformatics analysis, clinical reasoning, and much more — scaling biomedical expertise for 100× the number of discoveries. Key results: ➡️ Designed a cloning experiment with real-world wet-lab validation; on par with 5+ year expert in a blind test ➡️ Ran 458-file wearable bioinformatics analysis in 35 minutes vs. 3 weeks (800x faster) for human expert ➡️ Uncovered novel hypothesis: new TFs regulating skeletal lineages on a large scRNA+scATAC data ➡️ Human-level performance on LAB-bench DbQA and SeqQA, with SOTA at Humanity’s Last Exam and across 8 new biomedical tasks—ranging from GWAS and rare disease diagnosis to microbiology and drug repurposingPowered by: ➡️ Biomni-E1 – the first unified environment designed for a biomedical agent—encompassing 150 tools, 59 databases, 106 software—systematically curated from 2,500+ bioRxiv papers ➡️ Biomni-A1 – a generalist agent with retrieval, planning, and code as action Biomni is an open-source initiative: we invite the community to build on it and advance biomedical research at scale. - Try it now: biomni.stanford.edu - Paper: biomni.stanford.edu/paper.pd… - Code: github.com/snap-stanford/bio… - Join the community: tinyurl.com/biomni-slackWith Amazing team and collaborators @StanfordAILab @StanfordMed @StanfordCancer @genentech @arcinstitute @UCSF @UW @PrincetonAInews @KexinHuang5 @serena2z @hcwww_ @YuanhaoQ @mintaylu @yusufroohani @RyanLi0802 @LinQiu0128 Gavin Junze Di Shruti Jennefer Xin Zhou @MWheelerMD Jon Bernstein @MengdiWang10 @PengHeAtlas @SnyderShot @lecong Aviv Regev

Excited to announce 2nd major release of Open Grpah Benchmark (OGB), a collection of realistic, large-scale, and diverse benchmark datasets for machine learning with graphs. ogb.stanford.edu/ arxiv.org/abs/2005.00687

Fast Subgraph Counting and Matching using Graph Neural Networks. Lecture 12 of Stanford CS224W Machine Learning with Graphs course just released. Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

📣Ready to dive deep into the world of #MachineLearning with Graphs? 🌐 Our new online class explores how diseases & information spread, traffic & weather predictions, & so much more! Join us, & make sense of the world's complex data🚀. online.stanford.edu/courses/… We kick off on June 5th! #DataScience #OnlineCourse @StanfordEng

Scaling up Graph Neural Networks to Large Graphs. Lecture 17 of Stanford CS224W Machine Learning with Graphs course just released! Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

Advancements in Deep Learning for Graphs: Position-aware Graph Neural Networks, Strategies for Pretraining GNNs and the Open Graph Benchmark. Slides from my talk at Deep Learning for Graphs workshop at #TheWebConf: i.stanford.edu/~jure/pub/tal…

Curious about what I’ve been working on over the last two years? Join me next Thursday for the launch of Kumo.AI’s groundbreaking declarative ML platform that will transform the world of predictive AI as we know it. 🚀 More at info.kumo.ai/revolutionizing… #AI #revolution #GNNs #deeplearning

GNNs for heterogeneous graphs and Knowledge graph embedding/completion methods. Lecture 10 of Stanford CS224W Machine Learning with Graphs course just released. Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

It was great to host the Stanford Graph Learning workshop. Humbled to receive an amazing response from the community---over 7000 attendees. Slides and videos are available at: snap.stanford.edu/graphlearn… Make sure to check out pyg.org as well!

#KnowledgeGraph Embeddings for harnessing the full graph context for predictions 🌐📈. @Stanford #CS224W students David Kuo & @riyavsinha have impressively implemented two robust algorithms now ready for use out-of-the-box. medium.com/stanford-cs224w/i… #GNN @PyG_Team 🚀🛠️

Graph Convolutional Neural Networks for Web-Scale Recommender Systems. Our new #KDD2018 paper with @PinterestEng. arxiv.org/abs/1806.01973

Thrilled to share that my lab is looking for postdocs! In partnership with @ChanZuckerberg, we're focusing on developing massive biomedical foundation models to create an AI-powered virtual cell. Dream of harnessing the power of 1,000 H100s? Apply now at: snap.stanford.edu/apply/inde…

How to Build the Virtual Cell with Artificial Intelligence: Excited to share our vision of how to build foundation models for molecules, cells, and entire tissues. Our Universal Cell Embedding (UCE) model is the first in this series and we are going to release more models in the near future. arxiv.org/pdf/2409.11654 biorxiv.org/content/10.1101/… Exciting collaboration with @_bunnech, @yusufroohani, @yanayrosen, @karaletsos, Aviv Regev, @prof_lundberg, @stephenquake

Join us for the Stanford Graph Learning Workshop 2025! 🗓️Oct 14, 2025 📍Stanford University 🧠Topics: Agents, RFMs & LLM Inference. Save your spot to explore the future of #AI, #LLMs and #GraphLearning with leading experts. Register now: snap.stanford.edu/graphlearn…

Med-Flamingo is the first large medical multimodal language-vision model that allows for in-context learning beating benchmarks by 20%. Paper, code and the model released below.

There's a lot of goodness in #GraphNeuralNetworks including better model quality over traditional #MachineLearning, you can learn more here: hubs.ly/Q025lsJg0

Excited to announce the 2nd edition of OGB-LSC (large-scale graph ML challenge) at NeurIPS 2022, following the success of our last OGB-LSC at the KDD Cup 2021! The competition ends on Nov 1st. Looking forward to your participation! ogb.stanford.edu/neurips2022…

This is how machine learning engineers react after using GNNs by @Kumo_ai_team Thanks @TheMilennialDS for making this 😂 @PyG_Team

Reinforcement learning leads to better AI scientist agents! 🚀 By training models end-to-end with multi-turn RL, we’re seeing breakthroughs in reasoning and problem-solving for real biomedical research. Excited to introduce Biomni-R0 — an agentic LLM trained with this approach. On 10 real research tasks, it nearly doubles performance over its open-source base model and even surpasses closed-source frontier models by >10%. A scalable path to expert-level AI in biomedicine. Led by @RyanLi0802 @KexinHuang5 @ProjectBiomni with exciting collaboration with the SkyRL team @shiyi_c98 @NovaSkyAI. Learn more: biomni.stanford.edu/blog/bio… — open sourcing soon!

Making Graph Neural Networks more expressive and more robust to attacks. Lecture 16 of Stanford CS224W Machine Learning with Graphs course just released! Videos: piped.video/playlist?list=PL… Syllabus: cs224w.stanford.edu

🌟 Announcing the speakers for the Stanford Graph Learning Workshop 2023 on Oct 24 2023!🤝 Bringing together academia & industry leaders to delve into advances in #MachineLearning & #AI in Relational domains, Foundation models, and Multimodal AI. 📢 Register at: snap.stanford.edu/graphlearn…

Here is the first CS224W project: @Senem_ashak and Michael Atkin show how GNNs in PyG solve the traveling salesman problem, which can optimize logistics and transportation networks medium.com/stanford-cs224w/t…

Join us for the 3rd Stanford Graph Learning Workshop. Live stream is starting in less than 12 hours -- Tuesday Oct 24 at 9am PST. 📢 Program: snap.stanford.edu/graphlearn… Live stream at: piped.video/watch?v=aLeDg7vl…

📢 In collaboration with @intel and @Kumo_ai_team we are excited to release @PyG_Team v2.5. Big new features: Distributed GNN training, graph tensor representation, RecSys support, PyTorch 2.2 and native compilation support! github.com/pyg-team/pytorch_…

Our ICLR 2023 spotlight paper introduces LAMP, a deep learning-based surrogate model for multi-resolution physics. It optimizes computation cost and resolution for accurate simulation of dynamic regions, with controllable error-computation tradeoff. arxiv.org/abs/2305.01122

Excited to announce that we are founding Kumo.ai with @VanjaJosifovski and @raghavan_hema. We are taking graph learning (pyg.org) to a broad set of use cases in a user-friendly SaaS product. forbes.com/sites/kenrickcai/…

GNNs are highly effective in detecting fraud and malicious activity on transaction, social, and economic graphs. @Stanford #cs224w course rpoject by @AnshKhurana11, @akansal_, @soumyachat medium.com/stanford-cs224w/f…

Winners of the OGB-LSC graph machine learning challenge have just been announced at ogb.stanford.edu/kddcup2021/… Congratulations to the winning teams from @BaiduResearch, @DeepMind, @Synerise, Harbin and Dalian Institutes of Technology and USTC!

Learning Structural Node Embeddings via Diffusion Wavelets. cs.stanford.edu/~jure/pubs/g…

Huge congratulations to my exceptional PhD student, @weihua916 (now @Kumo_ai_team), on earning the #KDD2023 Doctoral Dissertation Award! Your dedication, hard work, and groundbreaking research on GNNs & OGB have truly paid off. A well-deserved honor! 🏆👏

Graph Representation Learning. Slides from my keynote @ieeebigdata. i.stanford.edu/~jure/pub/tal…

A map of over 7000 attendees from all over the world to the the Stanford Graph Learning workshop. Thank you all the speakers and attendees who made this event successful! snap.stanford.edu/graphlearn… Video: piped.video/GYW286H3SKw @PyG_Team

Very excited to announce that our paper "Supporting COVID-19 policy response with large-scale mobility-based modeling" received the #KDD2021 Best Paper Award in the Applied Data Science track! Thanks & congrats to @serinachang5 & all our collaborators! medrxiv.org/content/10.1101/…

🚀 Announcing RelBench: an open benchmark for deep learning on relational databases! RelBench is the foundational infrastructure for research in Relational Deep Learning (RDL), which brings modern AI to structured data. RelBench has databases, tasks, loaders, evaluators, and leaderboards to catalyze research in the field! Key features: 🌍 7 datasets spanning diverse domains: e-commerce, social, medical, and sports. 🧩 30 carefully curated predictive tasks: including entity classification/regression and recommendation. 📊 Wide data size range: ranging from 74K to 41M rows, 15 to 140 columns, 3 to 15 tables. ⏳ Wide time spans: from 2 weeks to 55 years of training data. 🏅 Comprehensive benchmarks: SOTA tabular learning and GNN baselines for every task. 🔥We hired a data scientist with 5 years of industry experience to solve RelBench tasks using traditional machine learning (feature engineering, model training). The RDL outperforms the data scientist in accuracy while reducing the time/code by 20x (12.3 hors -> 0.5 hours) !!! 🤯 Learn more: 🌐 Website: relbench.stanford.edu 📄 Paper: arxiv.org/abs/2407.20060 💻GitHub: github.com/snap-stanford/rel… Follow @RelBench for the latest updates Shoutout to the amazing team: @Josh_d_robinson @_rishabhranjan_ @weihua916 @KexinHuang5 @jiaqihan99 @adobles96 @rusty1s @janericlenssen @yiwenyuan98 @zechengzh @xhe1997 @Kumo_ai_team @PyG_Team @StanfordAILab

Excited for this recognition of our Supervised Random Walks paper, which paved path for Facebook’s people-you-may-know system.

🚀 Excited to open-source our general-purpose biomedical AI agent Biomni. Biomni A1 (agent) + E1 (env) with 150 specialized tools, 59 databases, and 105 software! With just a few lines of code, you can now automate complex biomedical research with AI agent! E1 only scratches the surface of the complex biomedical agent environment — which is why we’re launching Biomni-E2: an open, community-built environment for bio-agent. We welcome new tools, benchmarks, datasets, and beyond. Significant contributors will be invited as co-authors on our upcoming E2 manuscript. Join us in building the future of biomedical agents.More to come! More to come! github.com/snap-stanford/Bio… biomni.stanford.edu/ Great work by @KexinHuang5, @YuanhaoQ and the team!

Announcing Temporal Graph Benchmark, a collection of benchmark datasets for temporal graph machine learning: tgb.complexdatalab.com/ An exciting collaboration between @mila, @Kumo_ai_team, @UniofOxford, @imperialcollege and @Stanford

Really excited to announce Pinterest Labs! medium.com/@Pinterest_Engine… labs.pinterest.com

Can we predict side effects of drug combinations? Our paper gains insights on how different drug combinations interacted with protein networks to create side effects -- before they happen. academic.oup.com/bioinformat… news.stanford.edu/2018/07/10…

Excited to announce a distinguished lecture on Learning and Generalization in Graph Neural Networks by prof. Stefanie Jegelka from @MIT_CSAIL at @Pinterest Labs. Tue 11/16/2021 at 4pm Pacific time. RSVP: pinlabstechtalknov21.splasht…

Gave a keynote at #TheWebConf2025 in Sydney! 🌏 Talked about advancing LLMs from retrieval to reasoning with: 🧠 STaRK: semi-structured QA benchmark 🔧 AvaTaR: tool-using LLM agents 🤝 CollabLLM: models that collaborate 📊 POPPER: auto hypothesis testing Slides: i.stanford.edu/~jure/pub/tal…

The @LogConference, the leading conference dedicated to graph machine learning, is happening! Submission deadline: September 11th, 2024 Final decision: November 13th, 2024 Don't miss out on this opportunity! More details at logconference.org/cfp/#LoG20… #GraphML #MachineLearning #CallForPapers

Sharing slides from my @textgraphs @NAACLHLT workshop keynote: Reasoning with Language and Knowledge Graphs i.stanford.edu/~jure/pub/tal…

Excited to release the complete program of the 30th #thewebconf with paper presentation videos freely available at: videolectures.net/www2021_Lj… Enjoy!

Stanford Graph Learning Workshop starts in less than 1 hour! Live stream at: piped.video/GYW286H3SKw Join @PyG_team Slack #workshop-2022 for Q&A: data.pyg.org/slack.html

Thrilled to share @scverse_team x @ProjectBiomni! Biomni agent now supports 10 scverse core packages including scanpy, squidpy, scirpy, pertpy, etc! You can now use natural language to unlock complex single cell, spatial, and perturbation data analysis to generate novel hypothesis. Learn more at biomni.stanford.edu/blog/scv…

GNNs and atomic coordinates paving the way in protein conformational landscape prediction 🧬📊. @Stanford #CS224W students Leah Reeder & Xun Tang use GNNs to predict protein structures, creating potential impacts on fields of drug discovery. medium.com/stanford-cs224w/g… @PyG_Team

Kumo.AI has just been recognized by @Forbes as one of the top AI companies in the world alongside giants like @OpenAI and @AnthropicAI. I couldn't be prouder to be part of the @Kumo_ai_team and the AI revolution. forbes.com/lists/ai50/?sh=5e…

Mobility network modeling explains higher SARS-CoV-2 infection rates among disadvantaged groups and informs reopening strategies. medrxiv.org/content/10.1101/… Joint work with @serinachang5 @2plus2make5 PangWeiKoh @jalinegerardin @beth_redbird @davidgrusky

🚀 New tutorial on Graph Transformers! We introduce a powerful architecture combining the strengths of GNNs and Transformers for structured data. Learn how GTs open new possibilities for graph learning. kumo.ai/research/introductio… #GraphTransformers #MachineLearning #AI #GNN

📢 Exciting opportunity alert! Join my @StanfordEng research group as a Research Scientist. Manage cutting-edge research in machine learning. Help solve complex problems in graph representation learning, foundation models, and more! 🚀 careersearch.stanford.edu/jo… #JobOpening #ResearchScientist #AI

Reduce LLM hallucinations with RAG over textual as well as structured knowledge bases. Together with @amazon we are releasing 🌟STaRK 🌟, a large-scale LLM retrieval benchmark on semi-structured knowledge bases with dataset from e-commerce, biomedicine, and academic research.

AI for public health workshop at ICLR 2021. 2 weeks left to submit! Both applied and methodological work is welcome, including in-progress, submitted, or recently published papers. aiforpublichealth.github.io/

Our @Nature perspective on flexible and reusable AI models that can revolutionize medicine with Generalist Medical AI (GMAI). GMAI can carry out diverse medical tasks with little to no labeled data and interpret various medical modalities. Free access: nature.com/articles/s41586-0…

How to Reduce Infections Without Closing Businesses nytimes.com/interactive/2020… Excited to see our collaboration with NYT out!

Excited to share that our Open Graph Benchmark Large-Scale Challenge (OGB-LSC) paper has been accepted to NeurIPS! We have updated our datasets and released public leaderboards: ogb.stanford.edu/docs/lsc Paper: openreview.net/pdf?id=qkcLxo…

💠 Stanford Graph Learning Workshop 2024! Join leaders from academia and industry to explore the latest in Machine Learning and AI. Topics include Relational domains, Foundation Models, Agents and more. Save the date: Tuesday, Nov 5, 2024, 09:00 - 18:00 PT. The event will be held at Stanford University and live-streamed online. Register and/or submit a talk/poster: snap.stanford.edu/graphlearn…

#GNNs are highly effective in knowledge tracing, used by online educational systems to make learning effective and relevant. Great work by @Stanford #CS224W students by Anirudhan Badrinath, Jacob Smith, and Zachary Chen: medium.com/stanford-cs224w/g… #gnn #pyg

Join us for the Stanford Graph Learning Workshop (snap.stanford.edu/graphlearn…). Exciting program, panels and demos. Nearly 7000 attendees. We start in 1h! Live stream: piped.video/NKZdqCi5fVE

While generative AI excels at natural language understanding, it struggles with more complex tasks, such as predicting customer behavior or detecting fraudulent transactions. Join me this Thursday to find out how @Kumo_ai_team is bridging this gap and redefining predictive AI in enterprises and beyond. info.kumo.ai/revolutionizing…

Next-generation algorithms for networks. Slides from my keynote at BigNet workshop at #www2017 (today at 11am). i.stanford.edu/~jure/pub/tal…

Z veseljem sem sprejel vabilo bivšega predsednika republike Boruta Pahorja na pogovor o umetni inteligenci, raziskavah @Stanford in podjetništvu @Kumo_ai_team @PinterestEng. metropolitan.si/mastercard-p… @metropolitansi