Feng Cui

Feng Cui is an Associate Professor in the Thomas H. Gosnell School of Life Sciences at Rochester Institute of Technology (RIT), where he serves as Graduate Director of the Bioinformatics MS Program and is an affiliated faculty member of the Golisano College for Computing and Information Sciences. He holds the position of Faculty Senator (Alternate) and maintains an active research laboratory focused on computational biology and bioinformatics.

His educational background includes an MS from Truman State University, a Ph.D. in Bioinformatics and Computational Biology from Iowa State University, and an MD from Hunan Medical University in China. Following his doctoral studies, he completed postdoctoral training at the National Cancer Institute (NCI).

Dr. Cui's research spans three primary areas: exploring nucleosomal DNA diversity, developing machine learning approaches to predict nucleosome-binding proteins, and applying artificial intelligence to medical and systems biology challenges. His work combines computational approaches with biological insights to address fundamental questions in chromatin structure and develop practical applications for disease diagnosis and treatment.

His publication record from 2012-2023 demonstrates consistent output in high-impact journals including Nucleic Acids Research, BMC Bioinformatics, and Frontiers in Bioinformatics. His recent work shows a clear progression toward increasingly sophisticated AI applications in biology, with a growing emphasis on deep learning techniques for medical applications, particularly in cancer research and virology.

Dr. Cui actively mentors students at multiple levels, with numerous undergraduate and graduate researchers contributing to his projects. His laboratory has been supported by funding from the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health under award number R15GM149587.

The Cui Research Group maintains a strong focus on the interface between computer science and biology, with current projects investigating nucleosomal DNA patterns, machine learning prediction of protein-DNA interactions, and AI applications for medical diagnostics. The lab has developed several computational tools including nuMap for nucleosome positioning prediction and ProtGauss for predicting nucleosome binding modes.