Upcoming Events

Viva la Revolución of Open Source Large Language Models: Unleashing the Dark Horse in AI Innovation

Industrial Problems Seminar

Patrick Delaney (BloomBoard)

Viva la Revolución of Open Source Large Language Models: Unleashing the Dark Horse in AI Innovation

Industrial Problems Seminar

Patrick Delaney (BloomBoard)

Advancing Machine-Learned Interatomic Potentials: Enhancing Accuracy and Robustness in Materials Science Applications

Data Science Seminar

Yangshuai Wang (University of British Columbia)

Advancing Machine-Learned Interatomic Potentials: Enhancing Accuracy and Robustness in Materials Science Applications

IMA Data Science Seminar

Yangshuai Wang (University of British Columbia)

Academia, to Industry, to the NBA – Navigating a Non-Academic Career with a PhD

Industrial Problems Seminar 

Daniel Martens (Minnesota Timberwolves)

Academia, to Industry, to the NBA – Navigating a Non-Academic Career with a PhD

Industrial Problems Seminar 

Daniel Martens (Minnesota Timberwolves)

Conditional coalescent and its applications in population genomics

Data Science Seminar

Wai-Tong (Louis) Fan (Indiana University)

Conditional coalescent and its applications in population genomics

IMA Data Science Seminar

Wai-Tong (Louis) Fan (Indiana University)

Graph AI: Science and Industrial Applications

Industrial Problems Seminar 

Jie Chen (IBM Research)

Abstract

Graphs serve as both a mathematical abstraction and a structured framework for organizing data, finding widespread applications across scientific and technological domains. The ascent of graph neural networks underscores their exceptional efficacy in capturing intricate data interactions, leading to a resurgence of traditional applications with elevated solution quality and the emergence of novel uses. This talk delves into several graph-related challenges encountered in industrial contexts and the consequent evolution of graph-based deep learning methodologies. Topics include the learning of graph grammar for advancing material discovery and circuit design, the scaling of graph neural network training for financial forensics, and the unveiling of latent graph structures in power grid analytics. The talk concludes with a discussion on graph-based learning in the era of foundation models and research opportunities.

Graph AI: Science and Industrial Applications

Industrial Problems Seminar 

Jie Chen (IBM Research)

Abstract

Graphs serve as both a mathematical abstraction and a structured framework for organizing data, finding widespread applications across scientific and technological domains. The ascent of graph neural networks underscores their exceptional efficacy in capturing intricate data interactions, leading to a resurgence of traditional applications with elevated solution quality and the emergence of novel uses. This talk delves into several graph-related challenges encountered in industrial contexts and the consequent evolution of graph-based deep learning methodologies. Topics include the learning of graph grammar for advancing material discovery and circuit design, the scaling of graph neural network training for financial forensics, and the unveiling of latent graph structures in power grid analytics. The talk concludes with a discussion on graph-based learning in the era of foundation models and research opportunities.