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Stanford Graph Learning Workshop 2023

Event Details:

Tuesday, October 24, 2023
8:00am - 6:00pm PDT


Paul Brest Hall
555 Salvatierra Walk
Stanford, CA 94305
United States

Stanford Graph Learning Workshop 2023

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 Multimodal AI. 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.

This event is being held in person & online.


8:00 - 9:00Registration & Breakfast 
9:00 - 9:10Jure Leskovec, Stanford UniversityWelcome and Overview
9:10 - 9:40Matthias Fey, PyG & Kumo.AIWhat’s New in PyG, Torch Frame
9:40 - 10:00Serina Chang, Stanford University & Qi Xiu, HitachiMachine Learning for Supply Chain Management
10:00 - 10:20Michi Yasunaga, Stanford UniversityRetrieving from Knowledge Bases for Large Language Models
10:20 - 10:40Weihua Hu, Kumo.AIGraph Neural Networks for Declarative ML
10:40 - 11:00Break 
11:00 - 11:20Joshua Robinson, Stanford UniversityNext Generation Architectures for Graph ML
11:20 - 11:40Qian Huang, Stanford UniversityLarge Language Models As AI Research Agents
11:40 - 12:00Yusuf Roohani, Stanford UniversityFrom cell engineering to drug discovery: Predicting outcomes of multi-gene cell perturbations
12:00 - 12:20Ravi Motwani,  Hongming Zheng & Michael Galkin, Intel LabsRecSys 2023, Intel-Kumo PyG collaboration and Foundation Models for Knowledge Graphs
12:20 - 12:30Joseph Huang, Stanford UniversityStanford Data Science Institute Affiliate Programs
12:30 - 13:30Lunch 
13:30 - 13:50Mahashweta Das, VISAGraph Neural Network for Financial Data
13:50 - 14:10Karthik Subbian, AmazonGraph Representation Learning at Amazon
14:10 - 15:00

Moderator: Hema Raghavan (Kumo.AI)Panelists:

Industry Panel – Challenges and Opportunities for Graph Learning
15:00 - 15:20Hamed Nilforoshan, Stanford UniversityZero-shot Causal Learning
15:20 - 15:40Minkai Xu, Stanford UniversityGenerative Modeling for Drug Discovery
15:40 - 16:00Rishi Puri & Mohammad Nabian, NVIDIAPyG and Modulus: An open-source framework for building, training, and fine-tuning Physics-ML models
16:00 - 16:15Poster Slam 
16:15 - 18:00Happy Hour & Poster Session 

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