Tomorrow Thursday Nov 13 - RiP Meeting - Dr. Yanfu Zhang

[Data science RiP]

Hello All,

Tomorrow, Thursday, November 13th at 3PM (MT), we welcome Dr. Yanfu Zhang from the College of William And Mary, who will be presenting his work on graph representation learning.

🔗 Zoom: https://unm.zoom.us/j/93770730211
📌 Meeting ID: 937 7073 0211
🔒 Passcode: ds-rip

📆 Google Calendar: https://calendar.google.com/calendar/u/0?cid=MzM4YjZkYWQzMmI0NDE4MjU1NDY5Yzk4MGUzZjNjNmRiMDk3NTcyYzEyZmNhY2FmODkxNjkzYmMwMDlmN2U2ZUBncm91cC5jYWxlbmRhci5nb29nbGUuY29t

Abstract

A central question when designing an AI system in the real world is, "how to learn representations of the data that make it easier to extract useful information when building classifiers or other predictors?" Satisfactory answers are attainable for certain data, such as images, texts, or audio. However, various types of data are gathered as graphs, such as social networks, protein interaction networks, brain connectomes, etc. Despite the emerging and powerful graph neural network techniques, researchers are yet unanimous in the answer dedicated to learning embeddings from graphs due to their high irregularity, complexity, and sparsity. I have been focusing on addressing the critical challenges in graph representation learning.

In this talk, I will first introduce my recent research results on solving two key problems in graph representation learning. i) A significant limitation of the famous graph convolutional network is over-smoothed embeddings with deeper networks. ii) The scalability to big data, though facilitated by self-supervised pretraining, loses the focus on local structure. After that, I will expand the horizons beyond the nodes and discuss how they interact with the algorithm design. i) Graph-level embeddings are desired instead of node-level embeddings in some applications. ii) Data distribution implies graph structure---even if it is not explicitly given. Besides addressing the efficacy of representation learning, I also designed fairness-aware machine learning algorithms to tackle the bias in model training and data processing. I applied my new representation learning methods to successfully solve various real-world applications, such as brain disease early diagnosis, drug repositioning, and social media network predictions.