About me

Welcome! I am currently a data and applied scientist at Microsoft.

Prior to join Microsoft, I spent two and half years at BlackRock as a quantitative researcher. Before that, I was a postdoctoral research fellow in the H. Milton Stewart School of Industrial & Systems Engineering (ISyE) at Georgia Institute of Technology. I received my Ph.D. from the department of statistics at the University of Florida in April 2021, advised by Professor George Michailidis. Before coming to the University of Florida, I obtained B.Sc. in mathematics and statistics in School of Mathematical Sciences at Peking University in July 2015.

My main research spans in change points detection, high dimensional time series analysis, spatio-temporal data analysis, and machine learning.

Professional Experience

• Applied Scientist, Microsoft, New York City, NY, Aug. 2024 - Present
• Quantitative Researcher, BlackRock, New York City, NY, Mar. 2022 - Aug. 2024
• Postdoctoral Research Fellow, Georgia Institute of Technology, Atlanta, GA, May 2021 - Mar. 2022

Education

• Ph.D. in Statistics, University of Florida, FL, USA, Aug. 2015 - Apr. 2021
• B.Sc. in Mathematics and Statistics, Peking University, Beijing, China, Sep. 2011 - Jul. 2015

Research Publication

• Sepideh Mosaferi, Abolfazl Safikhani, Peiliang Bai, Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models, under review, 2025+.
• Che-Yi Liao, Peiliang Bai, Lance Waller, Kamran Paynabar, Estimating Hidden Epidemic: A Bayesian Spatiotemporal Compartmental Modelling Approach, Journal on Data Science, to appear, 2025.
• Peiliang Bai, Abolfazl Safikhani, George Michaailidis, Cohort Multiple Change Point Detection in Multivariate Vector Autoregressive Models, in submission, 2024+.
• Peiliang Bai, Abolfazl Safikhani, George Michailidis, Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models, Journal of the American Statistical Association (Theory and Methods).
• Peiliang Bai, Abolfazl Safikhani, George Michailidis, A Fast Detection Method of Break Points in Effective Connectivity Networks, IEEE Transactions on Medical Imaging, doi:10.1109/TMI.2021.3131142.
• Peiliang Bai, Yue Bai, Abolfazl Safikhani, George Michailidis, Multiple Change Point Detection in Structural VAR Models: the VARDetect R Package, Journal of Statistical Software, under review, 2021+, R package VARDetect is available at R CRAN.
• Peiliang Bai, Abolfazl Safikhani, George Michailidis, Multiple Change Points Detection in Low Rank and Sparse High Dimensional Vector Autoregressive Models, IEEE Transactions on Signal Processing, volume. 68, pp.3074-3089, 2020, doi:10.1109/TSP.2020.2993145.
• Peiliang Bai, Multiple Change Points Detection in Vector Autoregressive Models, PhD Thesis.

Softwares

• VARDetect: Multiple Change Point Detection in Structural VAR Models
• LSVAR: Estimation of Low Rank Plus Sparse Structured VAR Model R package