NEWS & EVENTS

By Shawn Hutchins

Guodong Pang joined Rice University as a professor in the Department of Computational and Applied Mathematics in January 2022. 

Pang specializes in applied probability and the application of methods in stochastic processes and control to rigorously explore problems in operations research, service engineering, health care, epidemiology, telecommunications, data centers, financial engineering and econometrics. 

Before coming to Rice, Pang was a professor of industrial and manufacturing engineering at Pennsylvania State University. While at Penn State, he also had a courtesy appointment in the Department of Mathematics and was affiliated with the university’s Institute for Computational and Data Sciences. Pang joined Penn State in 2010 after earning his doctorate in operations research from Columbia University.

Pang is a highly published researcher in applied probability, and queueing and stochastic networks, which is a branch of mathematics and operations research that analyzes workflow systems and complex dynamics.

“Companies that have large-scale service systems have greater opportunities for high efficiency and cost advantages. Queueing and stochastic network models can help optimize workflow processes and decision making,” said Pang, who has served as associate editor of the journal Queueing Systems since 2014 and of the journal Stochastic Systems. He has also published nearly 30 peer-reviewed papers on the subject. 

In this area of work, Pang has used several technical branches of mathematics, including stochastic process limit theory, optimization, stochastic control, and stability analysis. 

Pang has strong interests in quantitative finance and econometrics with an educational background that includes a master’s degree in economics from Queen’s University in Kingston, Canada. 

His recent work in this area introduced a new fractional stochastic process, called the generalized fractional Brownian motion, which can capture long-range dependence in non-stationarity time series in financial data. One of his working papers generalizes a stochastic volatility model in finance with this new process that can generate a more realistic term structure of at-the-money volatility skews. 

Pang teaches a broad range of courses. This past spring, he taught CAAM 382: Stochastic Models and will teach CAAM 485/586: Stochastic Simulation-Algorithms and Analysis in the fall. These courses have great appeal to students interested in quantitative finance. 

Also at Rice, Pang is a member of the Center for Computational Finance and Economic Systems (CoFES) external advisory board and a member of the Ken Kennedy Institute.

On a national level, Pang is a member of INFORMS (Institute for Operations Research and Management Science), Applied Probability Society, Institute of Mathematical Statistics, Bernoulli Society, and Society of Industrial and Applied Mathematicians.