Guojun Gan

At a broad level, my research interests lie at the intersection of actuarial science and data science. Actuarial science is fundamentally concerned with the modeling and management of risk, utilizing mathematical, statistical, and financial tools to solve problems in insurance, pensions, and other areas involving uncertain future outcomes. Data science is a multidisciplinary field that combines statistics, computer science, and domain expertise to analyze large and complex datasets, uncover patterns, and generate predictive insights.

Papers

A list of my publications can be found at Google Scholar.

Books

Guojun Gan
Data Clustering with Python From Theory to Implementation
Chapman & Hall/CRC Press, 2024
Code: Github

Guojun Gan
Actuarial Loss Models: A Concise Introduction
Chapman & Hall/CRC Press, 2024

Guojun Gan, Chaoqun Ma and Jianhong Wu
Data Clustering: Theory, Algorithms, and Applications (Second Edition)
SIAM, 2021
Code: Github

Guojun Gan and Emiliano A. Valdez
Metamodeling for Variable Annuities
Chapman & Hall/CRC Press, 2019
Code: va2019.zip.

Guojun Gan and Emiliano A. Valdez
Actuarial Statistics with R: Theory and Case Studies
ACTEX, 2018
Code: asr2018.zip.

Guojun Gan
An Introduction to Excel VBA Programming: with Applications in Finance and Insurance
Chapman & Hall/CRC Press, 2017
Code: code.zip.

Guojun Gan, Chaoqun Ma and Hong Xie
Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach
Wiley, 2014

Guojun Gan
Data Clustering in C++: An Object-Oriented Approach
Chapman & Hall/CRC Press, 2011
Code: cluslib-3.141.zip.

Guojun Gan, Chaoqun Ma and Jianhong Wu
Data Clustering: Theory, Algorithms, and Applications
SIAM, 2007

Edited Volume

Guojun Gan, Bohan Li, Xue Li, and Shuliang Wang
Proceedings of the 14th International Conference on Advanced Data Mining and Applications (ADMA 2018), Nanjing, China, November 16 - 18, 2018