Sunny Cheng

Dr. Sunny Cheng is a research-focused academic at Durham University specializing in the intersection of astronomy and machine learning. His work centers on developing and applying advanced computational techniques to analyze astronomical data, particularly through convolutional neural networks for galaxy morphology studies and large-scale survey analysis.

Cheng's research spans astrophysics, data science, and computational methods with primary interests in galaxy formation, morphological classification, and metal-poor galaxy detection. He actively contributes to major international collaborations including the Dark Energy Survey and OzDES Reverberation Mapping Program, demonstrating expertise in both theoretical astrophysics and practical machine learning implementation.

Analysis of his 2020-2025 publications reveals a clear trajectory toward increasingly sophisticated machine learning applications in astronomy. Early work included theoretical computer science contributions, but recent efforts focus almost exclusively on deep learning solutions for galaxy classification, intra-cluster light analysis, and metallicity studies within large astronomical datasets.

Scientific awards: None mentioned in available sources.

Advising and grants: No information available regarding student supervision or specific research funding sources.

Labs and teams: Collaborates with major international consortia including the Dark Energy Survey Collaboration and OzDES team, though no dedicated personal laboratory is specified in current documentation.