Graduate Level Special Topics Courses
Each semester the Department of Statistics and Operations Research offers graduate level special topics courses designed to present in-depth knowledge and cutting edge techniques and to integrate research and teaching. The following are special topics courses from recent semesters.
- Deep Learning (SAMSI)
- High Dimensional Time Series by Vladas Pipiras
- Probability on Trees and Networks by Nicolas Fraiman
- Markov Processes by Sayan Banerjee
- Monte Carol Methods by Chuanshu Ji
- Methods for Precision Medicine (SAMSI)
- Data Driven Decision Models by Vidyadhar Kulkarni
- Integer Programming and Semidefinite Programming by Gabor Pataki
- Model Uncertainty (SAMSI)
- Nonparametric Statistics by Kai Zhang
- Variational Problems in Probability and Statistics by Amarjit Budhiraja
- Design and Control of Queueing Systems with Applications to Manufacturing and Health Care by Nilay Tanık Argon
- Time Series Multivariate Analysis by Vladas Pipiras
- Convex Optimization by Quoc Tran-Dinh
- Concentration inequalities and Combinatorial optimization by Shankar Bhamidi
- Stochastic Analysis by Amarjit Budhiraja
- Numerical Optimization and Applications (SAMSI)
- Introduction to Computational Finance by Chuanshu Ji
- Stochastic Models for Financial Market Dynamics by V. G. Kulkarni
- Stochastic Models In Health care by Vidyadhar Kulkarni
- Point Processes by Ross Leadbetter
- Introduction to Nonlinear Programming by Shu Lu
- Object Oriented Data Analysis by J. S. Marron
- Analytical Methods and Applications to Astrophysics and Astronomy (SAMSI)
- Advanced Probability by Vladas Pipiras
- Long-Range Dependence by Vladas Pipiras
- Multivariate Analysis by Pranab Sen
- Modeling and Analysis of Service Operations by Serhan Ziya
- Statistical and Mathematical Challenges in Molecular Evolution (SAMSI)
- Stochastic Process Modeling for Ecological Processes (SAMSI)