Skip to main navigation Skip to search Skip to main content

Dr. Zihan WU, 吳子晗

20242025

Research activity per year

Personal profile

Author IDs

ORCID iD: 0000-0002-6551-6177
Scopus Author ID: 58550765800

Qualifications/Experiences

Zihan Wu holds dual Bachelor’s degrees in Mathematics and Physics from the University of Science and Technology of China. He has conducted research at CityU Shenzhen Research Institute and the University of Oxford and interned as a Quantitative Researcher at Millennium Management. His work spans tensor decomposition, financial engineering, and real-world measurement systems. He has teaching experience in computer graphics and system design and is a recipient of the Hong Kong PhD Fellowship Scheme.

Biography

Zihan Wu is a Ph.D. candidate in Electrical Engineering at City University of Hong Kong, where he researches machine learning, computer vision, and data mining. He holds dual Bachelor’s degrees in Mathematics and Physics from the University of Science and Technology of China. His research has been published in top-tier IEEE and AI conferences, and he has contributed to advancements in scalable clustering and ellipse detection for real-world measurement systems.

Research Interests/Areas

Zihan Wu’s research interests include machine learning, computer vision, data mining, natural language processing, and financial engineering.

Related Links

Education/Academic qualification

BSc, Bachelor of Natural Science in Physics, University of Science and Technology of China

1 Sept 201518 Jun 2020

Award Date: 18 Jun 2020

BSc, Bachelor of Natural Science in Mathematics and Applied Mathematics(Dual), University of Science and Technology of China

1 Sept 201518 Jun 2020

Award Date: 18 Jun 2020

Fingerprint

Dive into the research topics where Zihan WU is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
  • 1 Similar Profiles

Collaborations from the last five years

Recent external collaboration based on locations. Dive into details by clicking on the dots or