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.

Data Clustering: Theory, Algorithms, and Applications (Second Edition)

SIAM, 2021

Code: Github

Metamodeling for Variable Annuities

Chapman & Hall/CRC Press, 2019

Code: va2019.zip.

Actuarial Statistics with R: Theory and Case Studies

ACTEX, 2018

Code: asr2018.zip.

An Introduction to Excel VBA Programming: with Applications in Finance and Insurance

Chapman & Hall/CRC Press, 2017

Code: code.zip.

Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach

Wiley, 2014

Data Clustering in C++: An Object-Oriented Approach

Chapman & Hall/CRC Press, 2011

Code: cluslib-3.141.zip.

Data Clustering: Theory, Algorithms, and Applications

SIAM, 2007

Proceedings of the 14th International Conference on Advanced Data Mining and Applications (ADMA 2018), Nanjing, China, November 16 - 18, 2018

Last updated on: October 13, 2020

© Guojun Gan