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.
Papers
A list of my publications can be found at Google Scholar or this page.
Books
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
Edited Volume
Proceedings of the 14th International Conference on Advanced Data Mining and Applications (ADMA 2018), Nanjing, China, November 16 - 18, 2018