Shuai Wei
About Shuai Wei
Shuai Wei is a Principal Scientist specializing in machine learning and structural bioinformatics at Bristol Myers Squibb in Cambridge, Massachusetts. He has extensive experience in computational biology and has contributed to advancements in drug discovery and bioinformatics software.
Company
Currently, Shuai Wei is employed at Bristol Myers Squibb as a Principal Scientist, Machine Learning, Structural Bioinformatician. The company is located in Cambridge, Massachusetts, United States.
Previous Employment
Before joining Bristol Myers Squibb, Shuai Wei worked at Sanofi as a Computational Biology Senior Scientist, Machine Learning in Large Molecule Research from 2020 to 2022. Before Sanofi, he was a Senior Scientific Programmer/Analyst at Boston University from 2018 to 2020. Additionally, he held a Post-doc Fellow position at the University of Michigan for five years, from 2013 to 2018. Earlier in his career, Wei served as a Teaching Assistant and Research Assistant at Brigham Young University and as a Research Assistant at South China University of Technology.
Education and Expertise
Shuai Wei holds a Ph.D. in Chemical Engineering (Computational Simulation) from Brigham Young University, completed in 2012. He also earned an M.S. in Chemical Engineering (Computational Simulation) from the same institution in 2010. Wei completed his undergraduate studies in Chemical Engineering at South China University of Technology, earning a B.S. in 2005.
Research and Publications
Wei has published research focused on the application of machine learning in structural bioinformatics. His work includes the development of novel computational methods for analyzing large-scale biological data. He has presented these advancements at international conferences, highlighting the role of machine learning in bioinformatics.
Professional Contributions
Throughout his career, Shuai Wei has collaborated with interdisciplinary teams to integrate machine learning techniques into drug discovery processes. Additionally, he has contributed to several open-source bioinformatics software projects, aiding the scientific community in leveraging computational tools for biological research.