Georgia Institute of Technology College of Sciences

This course is a problem oriented introduction to the basic concepts of probability and statistics, providing a foundation for applications and further study.

Elementary combinatorial techniques used in discrete problem solving: counting methods, solving linear recurrences, graph and network models, related algorithms, and combinatorial designs.

This course includes topics on professional development and responsible conduct of research. The course satisfies the GT RCR Academic Policy for Doctoral Students to complete in-person RCR training.

The second of a two course sequence of faculty-directed independent research culminating in the writing of a senior thesis and its presentation.

The first of a two course sequence of faculty-directed independent research culminating in the writing of a senior thesis and its presentation.

An introduction to the Ito stochastic calculus and stochastic differential equations through a development of continuous-time martingales and Markov processes. (1st of two courses in sequence)

Case studies, visiting lecturers from financial institutions, student group projects of an advanced nature, and student reports, all centered around quantitative and computational finance. Crosslisted with ISYE and MGT 6785.

Introduction to large scale-system design to support computational finance for options, stocks, or other instruments.

An advanced course in Linear Algebra and applications.

Fourier analysis in Euclidean space. Basic topics including L^1 and L^2 theory; advanced topics such as distribution theory, uncertainty, Littlewood-Paley theory