Siu Wing CHENG 鄭紹榮



Telephone Number (852) 2358 6973


Email Email
scheng@ust.hk



Office Room 3551

Links Personal Web

Google Scholar 0nquEkQAAAAJ

ORCID 0000-0002-3557-9935

Scopus ID 7404686026



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First Name (and Middle Name If Any)
Siu Wing
CHENG
鄭紹榮




Highest Degree Acquired (e.g. PhD in Engineering Science)
PhD in Computer Science, University of Minnesota




Related Web Titles
Professor, Department of Computer Science and Engineering




Contact Information Telephone Number (852) 2358 6973


Email Email
scheng@ust.hk



Office Room 3551

Links Personal Web

Google Scholar 0nquEkQAAAAJ

ORCID 0000-0002-3557-9935

Scopus ID 7404686026



Research Interests Research Interests
Algorithms
Data structures
Geometry processing
Theoretical computer science




Biography Biography
Prof Siu-Wing Cheng belongs to the?Theoretical Computer Science Group. His major research areas include computational geometry, algorithms, and data structures. He is currently interested in algorithmic problems in geometric modeling. Recently, he has spent most of his time on problems in mesh generation and manifold reconstruction. Mesh generation is an important task in numerical simulations in scientific and engineering applications. In manifold reconstruction, he studies curve and surface reconstruction from noisy samples, as well as manifold reconstruction in arbitrary dimensions.?



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Research Interests Research Interests
Algorithms
Data structures
Geometry processing
Theoretical computer science




Biography Biography
Prof Siu-Wing Cheng belongs to the?Theoretical Computer Science Group. His major research areas include computational geometry, algorithms, and data structures. He is currently interested in algorithmic problems in geometric modeling. Recently, he has spent most of his time on problems in mesh generation and manifold reconstruction. Mesh generation is an important task in numerical simulations in scientific and engineering applications. In manifold reconstruction, he studies curve and surface reconstruction from noisy samples, as well as manifold reconstruction in arbitrary dimensions.?



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