CSCE768 Pattern Recognition and Classification,

CSCE822 Data Mining and Warehousing,

CSCE883 Machine Learning,

CSCE240 Introduction to Software Engineering (C++ programming),

CSCE350 Data structures and algorithmS,

CSCE569 Parallel computing,

CSCE555 Algorithms in bioinformatics,

High-throughput experimental techniques have generated huge amount of biological data such as genome sequences, microarray expression datasets, protein-protein interaction networks, and disease-gene mapping. Data mining techniques such as classification, clustering, and model-based prediction are routinely applied to these data for pattern recognition and knowledge discovery. It is also widely applied in science, engineering, business intelligence organizations, and financial analysis. This course will focus on techniques that have been successfully applied to bioinformatics and other engineering problems and will emphasize the capability of formulating and solving problems using either existing tools or self-made programs. Real-world (bioinformatics) problems from the literature will be used to challenge students; skills of data mining learned in the course.

Teaching Statement

I believe teaching and learning are both an exploratory process during which the instructor and students interact to achieve maximum productivity. I expect the students in my class to be active explorer in an uncharted land, reading as much as they can and putting the maximum effort in hands-on experience to achieve conceptual and intuitive understanding.