Guixiang Ma of Intel Labs

Graphs are pervasive in various fields, including social networks, biological interactomes, transportation networks, and more. Understanding these graphs through structured representations is crucial for grasping their complex topologies and interdependencies, which are vital for numerous applications within these domains. In recent years, Graph Neural Networks (GNNs) have attracted considerable attention and shown promising results in many graph learning tasks. Despite this progress, learning long-range dependencies within graphs continues to be a significant challenge. In this talk, Dr. Ma will discuss their recent advancements in enhancing the expressive power of GNNs to capture long-range information in graphs effectively, addressing real-world challenges in diverse interdisciplinary applications, such as medical image, brain network analysis, social network mining, and program analysis. She will also introduce innovative work in using graph machine learning for efficient workload partitioning in system optimization. This approach not only improves system performance but also supports the scalable distributed training of large-scale GNNs.

Biography:

Dr. Guixiang Ma is currently an AI Research Scientist at Intel Research Labs. She received her Ph.D. in Computer Science from the University of Illinois at Chicago in 2019. Her research interests lie in the field of machine learning and data mining, with a particular focus on graph machine learning. She is devoted to developing foundational machine learning algorithms and data-driven solutions to tackle challenges for real-world applications in various domains, such as medicine, neuroscience, social sciences, and computer systems. She has published more than 30 technical papers in top-tier conferences and journals in the fields of data mining, machine learning, systems, and biomedical informatics. She also holds two US patents.

Address:United States