At a broad level, my research interests lie in data mining and actuarial science. Data mining is an analytic process designed to explore large amounts of data (also known as “big data”) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. Actuarial science is the discipline that applies mathematical and statistical methods to assess risk in insurance, finance and other industries and professions. In the area of data mining, I am especially interested in developing efficient algorithms for data clustering, which is the most basic form of unsupervised learning that aims to divide a collection of data items into homogeneous groups or clusters. I am also interested in applying data clustering and other data mining techniques to solve problems in bioinformatics, actuarial science, computational finance, etc. In the area of actuarial science, I am especially interested in developing efficient algorithms and models to solve the problems related to variable annuity valuation.

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

Guojun Gan and Emiliano A. ValdezMetamodeling for Variable Annuities Chapman & Hall/CRC Press, 2019 |
Guojun Gan and Emiliano A. ValdezActuarial Statistics with R: Theory and Case Studies ACTEX, 2018 |
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Guojun GanAn Introduction to Excel VBA Programming: with Applications in Finance and Insurance Chapman & Hall/CRC Press, 2017 |
Guojun Gan, Chaoqun Ma and Hong XieMeasure, Probability, and Mathematical Finance: A Problem-Oriented Approach Wiley, 2014 |
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Guojun GanData Clustering in C++: An Object-Oriented Approach Chapman & Hall/CRC Press, 2011 |
Guojun Gan, Chaoqun Ma and Jianhong WuData Clustering: Theory, Algorithms, and Applications SIAM, 2007 |

Last updated on: September 22, 2017