The workshop will bring together leaders from academia and industry to showcase recent advances in Machine Learning and AI in Relational domains, Foundation Models, and Agents. The workshop will discuss methodological advancements, a wide range of applications to different domains, machine learning frameworks and practical challenges for large-scale training and deployment of AI models.
We are excited to announce a call for contributed talks and posters/demos at the upcoming Stanford Graph Learning Workshop. Submit your contribution!
Registration
The Stanford Graph Learning Workshop will be held on Tuesday, Nov 5 2024, 09:00 - 18:00 Pacific Time.
The event will take place at Stanford University and will be live-streamed online. Free registrations are available. Register here.
Schedule
08:00 - 09:00 | Registration & Breakfast | ||
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09:00 - 09:10 | Jure Leskovec, Stanford University | Welcome and Overview | |
09:10 - 09:30 | Joshua Robinson, Isomorphic Labs & Stanford | Relational Deep Learning - Graph Representation Learning on Relational Databases | |
09:30 - 09:50 | Matthias Fey & Akhiro Nitta, PyG & Kumo.AI | What’s New in PyG | |
09:50 - 10:10 | Rishabh Ranjan, Stanford University | RelBench: A Benchmark for Deep Learning on Relational Databases | |
10:10 - 10:30 | Rishi Puri, NVIDIA | GNN+LLM in PyG | |
10:30 - 11:00 | Break | ||
11:00 - 11:20 | Kexin Huang, Stanford University | Small-cohort GWAS discovery with AI over massive functional genomics knowledge graph | |
11:20 - 11:40 | Yusuf Roohani, Arc Institute & Stanford | An AI Agent for Designing Biological Experiments | |
11:40 - 12:00 | Marcel Roed, Stanford University | Lessons from Training Large Foundation Models | |
12:00 - 13:30 | Lunch | ||
13:30 - 13:50 | Vassilis N. Ioannidis, Amazon | Enhancing LLMs with structured data | |
13:50 - 14:10 | Charilaos Kanatsoulis, Stanford University | Towards Next Generation Graph Transformers | |
14:10 - 14:30 | Yiwen Yuan, Kumo.AI | ContextGNN: Beyond Two-Tower Recommendation Systems | |
14:30 - 15:00 | Break | ||
15:00 - 15:20 | Yanay Rosen, Stanford University | Universal Cell Embeddings and towards the AI Virtual Cell | |
15:20 - 15:40 | Minkai Xu, Stanford University | Diffusion Models for Tabular Data Generation | |
15:40 - 16:00 | Swapnil Bembde, Hitachi America Ltd. | Learning Production Functions for Supply Chains with Graph Neural Networks | |
16:00 - 16:15 | Poster slam | ||
16:15 - 18:00 | Happy Hour & Poster Session |
Posters
- Khaled Mohammed Saifuddin: Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation
- Romain Lacombe: Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models
- Mustafa Hajij: TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
- Michael Galkin: GraphAny: Fully-inductive Node Classification on Arbitrary Graphs
- Xiang Song: LM-GNN for Industry Application With GraphStorm
- Francisco Villaescusa-Navarro: Cosmological graphs
- Chang Liu: Enabling PyG Dataloader for Multi-Node GNN Training with Fully Sharded Graph Datasets
- Ronaldo Canizales: Heterogeneity-Aware Software Performance Characterization via Graph Machine Learning
- Zachary Aristei, Rishi Banerjee, Apoorva M. Krishnamurthy: GRACE: GRAph CachE System for RAG Applications
- Carolina Gonzalez-Cavazos: DBR-X: Drug-Based Reasoning Explainer for Interpretable Drug Repositioning
- Brian Shi: GraphRAG with GNN+LLM on Neo4j
- Anish Simhal: Going beyond the curse of infinite data: unsupervised network learning for oncology data using Ollivier-Ricci curvature
- Ying-Chun Lin: Rethinking Node Representation Interpretation through Relation Coherence
- Yorgos Tsitsikas: Multi-Graph Explorer: A Framework for Advanced Multi-Graph Analysis and Method Development
- Maria Shaukat: DeepWiN: Deep Graph Reinforcement Learning for User-Centric Radio Access Networks Automation
- Moritz Rietschel: CAD Graphs: LLMs for Geometric Logic
Organizing committee
Charilaos Kanatsoulis, Rok Sosic, Kexin Huang, Rishabh Ranjan, Jure Leskovec