Abstract

Spectral clustering is one of the most fundamental clustering algorithms in machine learning and has comprehensive applications in many fields of computer science. In this talk I will introduce the basics of spectral clustering, starting with its roots in spectral graph theory and its connection to eigenvalues and eigenvectors of graph Laplacians. I will present a spectral clustering algorithm in dynamic settings and discuss techniques for analyzing its performance. Several open problems will be discussed at the end of the talk. This is based on joint work with Steinar Laenen from Google Zurich, and the work appeared at ICML 2024.

About the speaker

He Sun is a Professor, and Director of Center for Algorithms and Learning Theory, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. He received his PhD from Fudan University in 2010 and worked at the Max Planck Institute for Informatics (2010 - 2015), UC Berkeley (2014, 2023), University of Bristol (2015 - 2017), and University of Edinburgh (2017 – 2025). His research areas include algorithms, machine learning, spectral graph theory, and applied probability. He has written over 60 papers and has solved several long-standing open problems in algorithms. He received the President Medal of Fudan University (2004), Shanghai Outstanding PhD Thesis Award (2010), Simons-Berkeley Research Fellowship (2014), Turing Fellowship (2018), and EPSRC Fellowship (2020). He is a recipient of the Chinese High-Level Talent Recruitment Program for Overseas Experts (2024). He has received research grants of more than 40 million CNY, and has served as an area chair and PC member of several leading conferences in ML and TCS, including ICML 2025 and STOC 2026.