Professor He Sun
About Me
He Sun is an Associate Director of State Key Laboratory of Biomedical Imaging Science and System in China, and Director of Center for Algorithms and Learning Theory, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). He received his PhD from Fudan University in 2010, and worked at the Max Planck Institute for Informatics, UC Berkeley, University of Bristol, and University of Edinburgh. His research interests include theoretical computer science, and machine learning theory. He has written over 60 papers and 1 book, and supervised 6 PhD students.
He received the President's Medal of Fudan University (2004), Shanghai Outstanding PhD Thesis Award (2010), Simons-Berkeley Research Fellowship (2014), and has so far received research grants of more than 5 million GBP from different research foundations and industrial partners. In 2020, he was awarded an EPSRC Fellowship of 1.5 million GBP for developing advanced spectral algorithms and Spectral Toolkit of Algorithms for Graphs (STAG), which is an C++ based open-source library for spectral graph algorithms; the Fellowship runs from January 2020 to May 2025.
Recent Professional Activities
PC Member of STOC 2026; PC Member of ESA 2026; Area Chair of ICML (2024, 2025, 2026); Senior PC member of AAAI (2025, 2026); Keynote Speaker for COCOA 2024, PDCAT 2024, TAMC 2025
Publication
Signed Laplacians for constrained graph clustering
J. Stewart Fabila Carrasco, and H. Sun. ICML'25, Spotlight
Dynamic similarity graph construction with kernel density estimation
S. Laenen, P. Macgregor, and H. Sun. ICML'25
Online sparsification of bipartite-like clusters in graphs
J. Das, S. De, and H. Sun. ICML'25
Can we measure the impact of a database?
P. Buneman, D. Dosso, M. Lissandrini, G. Silvello, and H. Sun. Communications of the ACM, May 2025
Dynamic spectral clustering with provable approximation guarantee
S. Laenen, and H. Sun. ICML'24
Fast approximation of similarity graphs with kernel density estimation
P. Macgregor, and H. Sun. NeurIPS'23, Spotlight
Is the algorithmic Kadison-Singer problem hard?
B. Jourdan, P. Macgregor, and H. Sun. ISAAC'23
Nearly-optimal hierarchical clustering for well-clustered graphs
B. Manghiuc, S. Laenen, and H. Sun. ICML'23
The support of open versus closed random walks
T. Sauerwald, H. Sun, and D. Vagnozzi. ICALP'23
A tighter analysis of spectral clustering, and beyond
P. Macgregor, and H. Sun. ICML'22
Fully-dynamic graph sparsifiers against an adaptive adversary.
A. Bernstein, J. van den Brand, M. Gutenberg, D. Nanongkai, T. Saranurak, A. Sidford, and H. Sun. ICALP'22
Finding bipartite components in hypergraphs
P. Macgregor, and H. Sun. NeurIPS'21
Hierarchical clustering: O(1)-approximation for well-clustered graphs
B. Manghiuc, and H. Sun. NeurIPS'21
Local algorithms for finding densely connected clusters
P. Macgregor, and H. Sun. ICML'21, Long Talk.
Higher-order spectral clustering of directed graphs.
S. Laenen, and H. Sun. NeurIPS'20
Augmenting the algebraic connectivity of graphs
B. Manghiuc, P. Peng, and H. Sun. ESA'20
Hermitian matrices for clustering directed graphs: insights and applications
M. Cucuringu, H. Li, H. Sun, and L. Zanetti. AISTATS'20
Hermitian Laplacians and a Cheeger inequality for the Max-2-Lin problem
H. Li, H. Sun, and L. Zanetti. ESA'19
Distributed graph clustering and sparsification
H. Sun, and L. Zanetti. ACM Transactions on Parallel Computing, 6(3): 17:1-17:23,2019
Human motion parsing by hierarchical dynamic clustering
Y. Zhang, S. Tang, H. Sun, and H. Neumann. BMVC'18
An SDP-based algorithm for linear-sized spectral sparsification
Y.Lee, and H. Sun. STOC'17
Communication-optimal distributed clustering
J. Chen, D. Woodruff, H. Sun, and Q. Zhang. NeurIPS'16
Partitioning well-clustered graphs: spectral clustering works!
R. Peng, H. Sun, and L. Zanetti. SIAM Journal on Computing'17, COLT'15
Constructing linear-sized spectral sparsification in almost-linear time
Y. Lee, and H. Sun. SIAM Journal on Computing'18, FOCS'15
Balls into bins via local search: cover time and maximum load
K.Bringmann, T.Sauerwald, A.Stauffer, and H. Sun. Random Structures & Algorithms'16, STACS'14
Low randomness rumor spreading via hashing
G. Giakkoupis, T. Sauerwald, H. Sun and P. Woelfel. STACS'12
Tight bounds for randomized load balancing on arbitrary network topologies
T. Sauerwald, and H. Sun. FOCS'12
Counting arbitrary subgraphs in data streams
D.Kane, K.Mehlhorn, T.Sauerwald, and H. Sun. ICALP'12
Approximate counting of cycles in streams
M.Manjunath, K.Mehlhorn, K.Panagiotou, and H. Sun. ESA'11
Minimum Manhattan network is NP-complete
F.Chin, Z.Guo, and H. Sun. Discrete & Computational Geometry'11, SoCG'09