Stanford Graph Learning Workshop 2025 Video Recordings

2 hours ago 1

The event has wrapped up, and you can find timestamps to each talk in the recording down below. Video links for the talks are available in the schedule below.

This year's gathering will spotlight three of the most dynamic frontiers in AI and data science:

Agents - autonomous systems reshaping how we interact with technology.
Relational Foundation Models - unlocking structure and meaning across complex data.
Fast LLM Inference - pushing the boundaries of speed and scalability in large language models.

The event will bring together leading researchers, innovators, and practitioners for a full day of cutting-edge talks, interactive sessions, and collaborative conversations, and opportunities to connect with a vibrant community at the forefront of data science.

The Stanford Graph Learning Workshop will be held on Tuesday, Oct 14 2025, 08:00 - 18:00 Pacific Time.

The event will take place at Stanford University and will be live-streamed online. Free registrations are available. Register here.

Yimin Fan: Prediction of Cellular Morphology Changes Under Perturbations with a Transcriptome-Guided Diffusion Model
Robert Yang: Modular Prompt Patching for Behavioral Drift in Continuously Updated LLMs
Rodrigo da Motta Cabral-Carvalho: AgentMarket: Generative Agents in Dynamic Market Networks
Guiran Liu: SecureBeam-GNN: A Heterogeneous Graph Neural Network for Secrecy Rate Maximization in IRS-Assisted Spectrum Sharing Networks
Binrong Zhu: A GNN-Based Super-Perception Agent for Multimodal Innovative City Planning and Management
Yang Liu: CommFormer-PLS: Multi-Agent Graph Communication Learning for Secure Power Control in Spectrum Sharing Networks
Heming Zhang: GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine
Michael Simon: Aligning Scientific Arguments as Conceptual Knowledge Graphs
Arnav Ramamoorthy: VLMGraph: Vision-Language Models on Graph Reasoning through Algorithmic Traversal Guidance
Tim Xu: OmniCellTOSG: The First Text-Omic Dataset and Foundation Model for Single-Cell Signaling Graph Modeling and Analysis
Viswanath Ganapathy: Exploring Small Language Models as Relational Learners
Arijit Khan: Explainable and Responsible AI with GNNs and GraphRAG
Fabrizio Dimino: FinReflectKG: Agentic Financial Knowledge Graph Construction and Evaluation
Tong Wu: Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations
Jacob Chmura: OpenDG: A Modular Framework for Machine Learning on Dynamic Graphs
Zachary Blumenfeld: GDS Agent for Graph Algorithmic Reasoning
Omid Bazgir: GRASP: Graph Reasoning Agents for Systems Pharmacology with Human-in-the-Loop
Yuan Chiang: Foundation Machine Learning Interatomic Potentials and Benchmarks
Boris Revechkis, Ph.D.: Boosting GraphRAG Accuracy & Reasoning and Reducing Deployment Effort with a Novel “Schema-as-Data” Architecture
Themistoklis Vargiemezis: WindMiL: Equivariant Graph Learning for Wind Loading Prediction
Waqas Ishtiaq: CST-AFNet: A Dual Attention-Based Deep Learning Framework for Intrusion Detection in IoT Networks
Tom Palczewski: RELATE: A Schema-Agnostic Perceiver Encoder for Multimodal Relational Graphs
Joseph Meyer: RGP: A Cross-Attention Based Graph Transformer for Relational Deep Learning
Haoteng Yin: Privately Learning from Graphs with Applications in Fine-Tuning Large Language Models
Fuhai Li: OmniCellAgent: An AI Co-Scientist for Autonomous Single-Cell Omics and Graph-Grounded Precision Medicine Discovery
Zhiyang Wang: Size Generalizable Graph Transformers
Ratna Kandala, Katie Hoemann: Beyond Co-occurrence: Dynamic Emotion Word Graphs from Contextual Embeddings
Shubham Gupta: Running Large Models on Small (Edge) Devices
Moritz Schaefer: Precision Pathology Diagnostics with Multimodal Learning
Parth Sarthi: Optimas: Optimizing Compound AI Systems with Globally Aligned Local Rewards
Arpandeep Khatua: Evolving Real World with Simulated Users' Feedback
Nils Walter: Interpreting & Understanding Predictions of Relational FMs
Yangyi Shen: Zero-Shot Generalization of GNNs over Distinct Attribute Domains
Tianlang Chen: RelGNN: Composite Message Passing for Relational Deep Learning
Yanay Rosen: How to Build the Virtual Cell with AI
Rishabh Ranjan: Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
Vijay Prakash Dwivedi: Relational Graph Transformer
Joy He-Yueya: CollabLLM: From Passive Responders to Active Collaborators
Charilaos Kanatsoulis: Next Generation Architectures for Relational Deep Learning
Kuan Pang: Creating a Donor Foundation Model for the AI Virtual Cell
Zoe Piran: Exposing Spatio-Temporal Cellular Dynamics Within Cells' Continuum
Ali Parviz: Are Large Language Models Good Temporal Graph Learners?
Haoyang Fang: Mlzero: A Multi-Agent System for End-to-End Machine Learning Automation
Subhodip Biswas: AI-powered Network Troubleshooting at Cisco ThousandEyes