Stanford Graph Learning Workshop 2023

Event Details:

Tuesday, October 24, 2023

8:00am - 6:00pm PDT

Location

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.

Agenda

8:00 - 9:00	Registration & Breakfast
9:00 - 9:10	Jure Leskovec, Stanford University	Welcome and Overview
9:10 - 9:40	Matthias Fey, PyG & Kumo.AI	What’s New in PyG, Torch Frame
9:40 - 10:00	Serina Chang, Stanford University & Qi Xiu, Hitachi	Machine Learning for Supply Chain Management
10:00 - 10:20	Michi Yasunaga, Stanford University	Retrieving from Knowledge Bases for Large Language Models
10:20 - 10:40	Weihua Hu, Kumo.AI	Graph Neural Networks for Declarative ML
10:40 - 11:00	Break
11:00 - 11:20	Joshua Robinson, Stanford University	Next Generation Architectures for Graph ML
11:20 - 11:40	Qian Huang, Stanford University	Large Language Models As AI Research Agents
11:40 - 12:00	Yusuf Roohani, Stanford University	From cell engineering to drug discovery: Predicting outcomes of multi-gene cell perturbations
12:00 - 12:20	Ravi Motwani, Hongming Zheng & Michael Galkin, Intel Labs	RecSys 2023, Intel-Kumo PyG collaboration and Foundation Models for Knowledge Graphs
12:20 - 12:30	Joseph Huang, Stanford University	Stanford Data Science Institute Affiliate Programs
12:30 - 13:30	Lunch
13:30 - 13:50	Mahashweta Das, VISA	Graph Neural Network for Financial Data
13:50 - 14:10	Karthik Subbian, Amazon	Graph Representation Learning at Amazon
14:10 - 15:00	Moderator: Hema Raghavan (Kumo.AI)Panelists: Karthik Subbian (Amazon) Mahashweta Das (VISA) Yinglong Xia (META) Luiz Mendes (iFood)	Industry Panel – Challenges and Opportunities for Graph Learning
15:00 - 15:20	Hamed Nilforoshan, Stanford University	Zero-shot Causal Learning
15:20 - 15:40	Minkai Xu, Stanford University	Generative Modeling for Drug Discovery
15:40 - 16:00	Rishi Puri & Mohammad Nabian, NVIDIA	PyG and Modulus: An open-source framework for building, training, and fine-tuning Physics-ML models
16:00 - 16:15	Poster Slam
16:15 - 18:00	Happy Hour & Poster Session