Huijing Jiang
About Huijing Jiang
Huijing Jiang is a Principal Statistician at Bristol Myers Squibb with extensive experience in statistical modeling and research, previously working at IBM for 10 years.
Current Position at Bristol Myers Squibb
Huijing Jiang currently holds the position of Principal Statistician at Bristol Myers Squibb. In this role, Jiang is likely responsible for leading statistical analysis initiatives, supporting clinical trials, and developing innovative methodologies to enhance research and development within the pharmaceutical industry.
Career at IBM
Huijing Jiang worked at IBM for a decade from 2010 to 2020, where Jiang served as a Research Staff Member (RSM). During this tenure, Jiang was involved in developing statistical models for various applications, including but not limited to sensor networks, cognitive pricing, and IT system performance monitoring. Jiang also held a summer intern position at the IBM T.J. Watson Research Center in 2008.
Educational Background
Huijing Jiang achieved a PhD in Statistics from the Georgia Institute of Technology, where Jiang studied from 2005 to 2010. Prior to this, Jiang earned a Master of Science (MS) in Statistics from the same institution between 2003 and 2005. Jiang's undergraduate education was completed at Tsinghua University, where Jiang received a Bachelor of Science (BS) from 1999 to 2003.
Experience in Analytical Projects
Throughout Jiang’s career, they have served as a technical lead on several analytical projects across various industries. Jiang has developed statistical models in areas such as marketing analysis, business software development, building and facility energy management, production forecasting, and air pollution forecasting.
Publications and Patents
Huijing Jiang has published over 10 peer-reviewed academic articles in top journals and conferences. Additionally, Jiang is the inventor of 3 patents, showcasing a significant contribution to the field of statistical research and methodology.
Specialties in Statistical Methods
Huijing Jiang possesses deep expertise in several advanced statistical methods, including change point detection, spatio-temporal analysis, time-series forecasting, clustering analysis, high-dimensional sparse modeling, multilevel models, and robust methods. Jiang's wide-ranging skills contribute to a robust capability in tackling diverse and complex statistical challenges.