Bei Chen is a Statistician / Data Scientist with primary interests in time series analysis and forecasting, probability forecasting, unsupervised learning and big data analytics. Bei led the modelling and predictive efforts in various customer projects in energy, retail, transportation and IoT, by inventing and developing novel predictive algorithms; she designed energy forecasting models which achieved predominant accuracy, compared to state-of-art. Currently Bei's research has been focusing on "Deep Learning for Time Series".
Prior to joining IBM, Bei Chen was an Assistant Professor in the Department of Mathematics and Statistics at McMaster University, Canada. Her research was funded by various grants, including NSERC (Project "Modeling, forecasting and diagnostic testing for financial and econometric time series"), ECR and MITACS.
Bei Chen received her PhD in Statistics from the University of Waterloo, Canada. She is the recipient of the 2011 Pierre Robillard Award of the Statistical Society of Canada for her work on "Linearization methods in time series analysis".