Stanford Graph Learning Workshop 2024

Stanford Data Science Affiliates Program

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
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
  • Vijay Prakash Dwivedi: Graph Transformers for Large Graphs
  • Organizing committee

    Charilaos Kanatsoulis, Rok Sosic, Kexin Huang, Rishabh Ranjan, Jure Leskovec

    Prior events

    2023 2022 2021