Chaoran Cheng

Chaoran is a fifth-year Ph.D. candidate in the Siebel School of Computing and Data Science. His research interests primarily span multiple aspects of generative modeling, with a particular theoretical focus on manifold learning. His work extends to various generative tasks in AI4Science domains. Actively engaged in the MMLI and CABBI initiatives, Chaoran strives to build versatile machine learning platforms that address complex real-world challenges of protein design in the pharmaceutical industry.