Cheng, Kuan

Dr. Kuan Cheng is an assistant professor at the Center on Frontiers of Computing Studies, Peking University, where he is affiliated with the Department of Computer Science. His research interests include computational models and complexity, pseudorandomness and coding, machine learning, and networks. Dr. Cheng joined Peking University in July 2020 and previously served as a postdoc at the University of Texas at Austin. He earned his PhD in computer science from Johns Hopkins University in 2019. His work focuses on coding for edit distance and hamming distance, derandomization for circuits and small space computation, and he plans to extend these results to areas such as machine learning and quantum computation. He has published extensively in top conferences of theoretical computer science, including FOCS, CCC, SODA, ICALP, TCC, and others.

Kuan Cheng is a researcher at Peking University with expertise in Theory of Computation, Pseudorandomness, Coding Theory, and Artificial Intelligence. Their work focuses on advancing efficient algorithms for vision-language models, including techniques like visual token sparsification and knowledge distillation. Cheng's research also explores deterministic document exchange protocols, error-correcting codes for edit errors, and locally decodable codes for Hamming and insertion-deletion errors. They contribute to the development of randomness extraction in AC0 and streaming algorithms for edit distance. Their research bridges theoretical computer science with practical applications in AI and data processing.