Frank Zou
Professor Zou's research focuses on financial time series and spatial statistics with applications to epidemiology, public health and climate change. His most recent research on statistical theory and methodology addressed a wide range of challenges including high dimensionality, complex dependencies, and space and time variations. His research in high-frequency financial data tackled problems with high dimensionality, which is currently a hot topic in statistics. He also works on developing novel spatio-temporal models that provide early and accurate detection of disease outbreaks using syndrome (collection of clinical symptoms) data that are monitored as indicators of potential disease outbreaks. These models can also be applied to threats such as biological weapon attacks.
Zou plans to continue to focus on these two areas of research at WPI. In financial statistics, “I will focus on developing novel methods for large volatility matrix estimation and inference, asset allocation and risk analysis, and evaluate its performance based on high frequency financial data,” Zou said. “I anticipate of my research on models that handle patterns of space-time correlation will be numerous and diverse, because a more accurate statistical model for large data sets has immediate relevance to many problems in finance, epidemiology and public health. I would like to explore opportunities of collaborations with scientists from different areas such as physical sciences, engineering, biological and clinical research. I believe that combining all the talented mind of different expertise, we can make our lives much better.”
Dr. Zou enjoys teaching at all levels and wide ranges of statistics courses. He has been enthusiastically involved in student advising and mentoring. He believes that the objectives of teaching are to give to the students the best possible preparation for their future studies and for their future employments, to make them able to master the concepts they are learning, and to give them the ability to use it creatively in more complex contexts as well as develop it further to suit more general problems.
Frank Zou
Professor Zou's research focuses on financial time series and spatial statistics with applications to epidemiology, public health and climate change. His most recent research on statistical theory and methodology addressed a wide range of challenges including high dimensionality, complex dependencies, and space and time variations. His research in high-frequency financial data tackled problems with high dimensionality, which is currently a hot topic in statistics. He also works on developing novel spatio-temporal models that provide early and accurate detection of disease outbreaks using syndrome (collection of clinical symptoms) data that are monitored as indicators of potential disease outbreaks. These models can also be applied to threats such as biological weapon attacks.
Zou plans to continue to focus on these two areas of research at WPI. In financial statistics, “I will focus on developing novel methods for large volatility matrix estimation and inference, asset allocation and risk analysis, and evaluate its performance based on high frequency financial data,” Zou said. “I anticipate of my research on models that handle patterns of space-time correlation will be numerous and diverse, because a more accurate statistical model for large data sets has immediate relevance to many problems in finance, epidemiology and public health. I would like to explore opportunities of collaborations with scientists from different areas such as physical sciences, engineering, biological and clinical research. I believe that combining all the talented mind of different expertise, we can make our lives much better.”
Dr. Zou enjoys teaching at all levels and wide ranges of statistics courses. He has been enthusiastically involved in student advising and mentoring. He believes that the objectives of teaching are to give to the students the best possible preparation for their future studies and for their future employments, to make them able to master the concepts they are learning, and to give them the ability to use it creatively in more complex contexts as well as develop it further to suit more general problems.
Scholarly Work
A Hybrid Hierarchical Bayesian Model for Spatio-Temporal Surveillance Data. Statistics in Medicine. 2018
Vast Portfolio Allocation and Risk Analysis using
High-Frequency Financial Data. Statistics and Its Interface. 2018
Multiple Day Biclustering of High-Frequency
Financial Time Series. Stat. 2018
An Online Spatio-Temporal Model for Inference and
Predictions of Taxi Demand. IEEE International Conference on Big Data 2017. 2017
Asymptotic theory for large volatility matrix estimation based on high-frequency financial data. Stochastic Processes and their Applications. 2016
Bayesian Methodology for the Analysis of Spatial-Temporal Surveillance Data 2012