Speaker: Harlin Lee, Ph.D.
University of California Los Angeles
Location: Virtual via Zoom
Time: 11:30 AM – 1:00 PM (PST)
Link to the notebook used in the presentation: https://colab.research.google.com/drive/1B5VXW1pCfoOaPSqjXHmpRlxPExaoCsxZ?usp=sharing
Abstract: The network (or graph) structure in data is increasingly used to improve statistical signal processing and machine learning (ML) methods. In this workshop, I will introduce the popular NetworkX Python package and walk through how it can be used for various tasks in network science, such as graph visualization, degree distribution, ranking, and clustering.
About the speaker: Dr. Harlin Lee is a Hedrick Assistant Adjunct Professor at UCLA Mathematics. She received her Ph.D. in Electrical and Computer Engineering at Carnegie Mellon University in 2021. She also has an MS in Machine Learning from Carnegie Mellon University and a BS + MEng in Electrical Engineering and Computer Science from MIT. Her research is on learning from high-dimensional data supported on structures such as graphs (networks), low-dimensional subspace, or sparsity, motivated by applications in healthcare and social science. Dr. Lee’s lifelong vision is to use data theory to help everyone live physically, mentally, and socially healthier.