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Courses

Graduate Courses


STOR 612 – Foundations of Optimization
Credits: 3
Description: STOR 612 consists of three major parts: linear programming, quadratic programming, and unconstrained optimization. Topics: Modeling, theory and algorithms for linear programming; modeling, theory and algorithms for quadratic programming; convex sets and functions; first-order and second-order methods such as stochastic gradient methods, accelerated gradient methods and quasi-Newton methods for unconstrained optimization.
Prerequisite: MATH 347, MATH 521
Permission: Permission of the instructor for students lacking the prerequisites.
Grading Status: Letter Grade


STOR 614 – Advanced Optimization
Credits: 3
Description: STOR 614 consists of three major parts: Integer programming, conic programming, and nonlinear optimization. Topics: modeling, theory and algorithms for integer programming; second-order cone and semidefinite programming; theory and algorithms for constrained optimization; dynamic programming; networks.
Prerequisite: STOR 612
Permission: Permission of the instructor for students lacking the prerequisites.
Grading Status: Letter Grade


STOR 634 – Probability I
Credits: 3
Description: Required preparation, advanced calculus. Lebesgue and abstract measure and integration, convergence theorems, differentiation. Radon-Nikodym theorem, product measures. Fubini theorems. Lp spaces.
Grading Status: Letter Grade


STOR 635 – Probability II
Credits: 3
Description: Foundations of probability. Basic classical theorems. Modes of probabilistic convergence. Central limit problem. Generating functions, characteristic functions. Conditional probability and expectation.
Prerequisite: STOR 634
Permission: Permission of the instructor for students lacking the prerequisite.
Same As: MATH 635
Grading Status: Letter Grade


STOR 641 – Stochastic Models in Operations Research I
Credits: 3
Description: Review of probability, conditional probability, expectations, transforms, generating functions, special distributions, and functions of random variables. Introduction to stochastic processes. Discrete-time Markov chains. Transient and limiting behavior. First passage times.
Prerequisite: STOR 435
Grading Status: Letter Grade


STOR 642 – Stochastic Models in Operations Research II
Credits: 3
Description: Exponential distribution and Poisson process. Birth-death processes, continuous-time Markov chains. Transient and limiting behavior. Applications to elementary queueing theory. Renewal processes and regenerative processes.
Prerequisite: STOR 461
Grading Status: Letter Grade


STOR 654 – Statistical Theory I
Credits: 3
Description: Required preparation, two semesters of advanced calculus. Probability spaces. Random variables, distributions, expectation. Conditioning. Generating functions. Limit theorems: LLN, CLT, Slutsky, delta-method, big-O in probability. Inequalities. Distribution theory: normal, chi-squared, beta, gamma, Cauchy, other multivariate distributions. Distribution theory for linear models.
Grading Status: Letter Grade


STOR 655 – Statistical Theory II
Credits: 3
Description: Point estimation. Hypothesis testing and confidence sets. Contingency tables, nonparametric goodness-of-fit. Linear model optimality theory: BLUE, MVU, MLE. Multivariate tests. Introduction to decision theory and Bayesian inference.
Prerequisite: STOR654
Grading Status: Letter Grade


STOR 664 – Applied Statistics I
Credits: 3
Description: Permission of the instructor. Basics of linear models: matrix formulation, least squares, tests. Computing environments: SAS, MATLAB, S+. Visualization: histograms, scatterplots, smoothing, QQ plots. Transformations: log, Box-Cox, etc. Diagnostics and model selection.
Grading Status: Letter Grade


STOR 665 – Applied Statistics II
Credits: 3
Description: ANOVA (including nested and crossed models, multiple comparisons). GLM basics: exponential families, link functions, likelihood, quasi-likelihood, conditional likelihood. Numerical analysis: numerical linear algebra, optimization; GLM diagnostics. Simulation: transformation, rejection, Gibbs sampler.
Prerequisite: STOR 664
Permission: Permission of the instructor for students lacking the prerequisite.
Grading Status: Letter Grade


STOR 672 – Simulation Modeling and Analysis
Credits: 3
Description: Introduces students to modeling, programming, and statistical analysis applicable to computer simulations. Emphasizes statistical analysis of simulation output for decision-making. Focuses on discrete-event simulations and discusses other simulation methodologies such as Monte Carlo and agent-based simulations. Students model, program, and run simulations using specialized software. Familiarity with computer programming recommended.
Prerequisite: STOR 555, STOR 641
Same As: COMP 672
Grading Status: Letter Grade


STOR 712 – Optimization for Machine Learning and Data Science
Credits: 3
Description: This course will provide a detailed and deep treatment for commonly used methods in continuous optimization, with applications in machine learning, statistics, data science, operations research, among others. The focus of this course is on continuous optimization algorithms, and it will also cover some core optimization theory as a foundation for the development of these algorithms. The discussions of algorithms will be accompanied with representative applications.
Prerequisite: STOR 612 or equivalent
Grading Status: Letter Grade


STOR 713 – Mathematical Programming II
Credits: 3
Description: Advanced theory for nonlinear optimization. Algorithms for unconstrained and constrained problems.
Prerequisite: STOR 712
Permission: Permission of the instructor for students lacking the prerequisite.
Grading Status: Letter Grade


STOR 722 – Integer Programming
Credits: 3
Description: Techniques for formulating and solving discrete valued and combinatorial optimization problems. Topics include enumerative and cutting plane methods, Lagrangian relaxation, Benders' decomposition, knapsack problems, and matching and covering problems.
Prerequisite: STOR 614
Permission: Permission of the instructor for students lacking the prerequisite.
Grading Status: Letter Grade


STOR 724 – Networks
Credits: 3
Description: Network flow problems and solution algorithms; maximum flow, shortest route, assignment, and minimum cost flow problems; Hungarian and out-of-kilter algorithms; combinatorial and scheduling applications.
Prerequisite: STOR 614
Permission: Permission of the instructor for students lacking the prerequisite.
Grading Status: Letter Grade


STOR 734 – Stochastic Processes
Credits: 3
Description: Discrete and continuous parameter Markov chains, Brownian motion, stationary processes.
Prerequisite: STOR 435
Grading Status: Letter Grade


STOR 743 – Stochastic Models in Operations Research III
Credits: 3
Description: Intermediate queueing theory, queueing networks. Reliability. Diffusion processes and applications. Markov decision processes (stochastic dynamic programming): finite horizon, infinite horizon, discounted and average-cost criteria.
Prerequisite: STOR 642
Grading Status: Letter Grade


STOR 744 – Queueing Networks
Credits: 3
Description: Jackson networks; open and closed. Reversibility and quasi-reversibility. Product form networks. Nonproduct form networks. Approximations. Applications to computer performance evaluations and telecommunication networks.
Prerequisite: STOR 642
Permission: Permission of the instructor for students lacking the prerequisite.
Grading Status: Letter Grade


STOR 754 – Time Series and Multivariate Analysis
Credits: 3
Description: Introduction to time series: exploratory analysis, time-domain analysis and ARMA models, Fourier analysis, state space analysis. Introduction to multivariate analysis: principal components, canonical correlation, classification and clustering, dimension reduction.
Prerequisite: STOR 435, STOR 555
Grading Status: Letter Grade


STOR 755 – Estimation, Hypothesis Testing, and Statistical Decision
Credits: 3
Description: Bayes procedures for estimation and testing. Minimax procedures. Unbiased estimators. Unbiased tests and similar tests. Invariant procedures. Sufficient statistics. Confidence sets. Large sample theory. Statistical decision theory.
Prerequisite: STOR 635, STOR 655
Grading Status: Letter Grade


STOR 756 – Design and Robustness
Credits: 3
Description: Introduction to experimental design, including classical designs, industrial designs, optimality, and sequential designs. Introduction to robust statistical methods; bootstrap, cross-validation, and resampling.
Prerequisite: STOR 555
Grading Status: Letter Grade


STOR 757 – Bayesian Statistics and Generalized Linear Models
Credits: 3
Description: Bayes factors, empirical Bayes theory, applications of generalized linear models.
Prerequisite: STOR 555
Grading Status: Letter Grade


STOR 763 – Statistical Quality Improvement
Credits: 3
Description: Methods for quality improvement through process control, graphical methods, designed experimentation. Shewhart charts, cusum schemes, methods for autocorrelated multivariate process data, process capability analysis, factorial and response surface designs, attribute sampling.
Prerequisite: STOR 655, STOR 664
Grading Status: Letter Grade


STOR 767 – Advanced Statistical Machine Learning
Credits: 2
Description: This is a graduate course on statistical machine learning.
Prerequisite: STOR 654, STOR 655, STOR 664, and STOR 665
Permission: Permission of the instructor for students lacking the prerequisite.
Grading Status: Letter Grade


STOR 772 – Introduction to Inventory Theory
Credits: 3
Description: Permission of the instructor. Introduction to the techniques of constructing and analyzing mathematical models of inventory systems.
Grading Status: Letter Grade


STOR 831 – Advanced Probability
Credits: 3
Description: Advanced theoretic course, covering topics selected from weak convergence theory, central limit theorems, laws of large numbers, stable laws, infinitely divisible laws, random walks, martingales.
Prerequisite: STOR 634, STOR 635
Grading Status: Letter Grade


STOR 832 – Stochastic Processes
Credits: 3
Description: Advanced theoretic course including topics selected from foundations of stochastic processes, renewal processes, Markov processes, martingales, point processes.
Prerequisite: STOR 634, STOR 635
Grading Status: Letter Grade


STOR 833 – Time Series Analysis
Credits: 3
Description: Analysis of time series data by means of particular models such as autoregressive and moving average schemes. Spectral theory for stationary processes and associated methods for inference. Stationarity testing.
Prerequisite: STOR 634, STOR 635
Grading Status: Letter Grade


STOR 834 – Extreme Value Theory
Credits: 3
Description: This course covers both mathematical theory and statistical methodology concerned with extreme values in sequences of random variables. IID theory: the three types of extreme value distributions, statistical methods by block maxima and threshold exceedances. Extensions to dependent stochastic sequences: the extremal index and related concepts. Multivariate and spatial extremes, max-stable process. Applications in: engineering and strength of materials; finance and insurance; environment and climate.
Prerequisite: STOR 635, STOR 654
Grading Status: Letter Grade


STOR 835 – Point Processes
Credits: 3
Description: Random measures and point processes on general spaces, Poisson and related processes, regularity, compounding. Point processes on the real line stationarity, Palm distributions, Palm-Khintchine formulae. Convergence and related topics.
Prerequisite: STOR 635
Grading Status: Letter Grade


STOR 836 – Stochastic Analysis
Credits: 3
Description: Brownian motion, semimartingale theory, stochastic integrals, stochastic differential equations, diffusions, Girsanov's theorem, connections with elliptic PDE, Feynman-Kac formula. Applications: mathematical finance, stochastic networks, biological modeling.
Prerequisite: STOR 634, STOR 635
Grading Status: Letter Grade


STOR 842 – Control of Stochastic Systems in Operations Research
Credits: 3
Description: Review of Markov decision processes. Monotone control policies. Algorithms. Examples: control of admission, service, routing and scheduling in queues and networks of queues. Applications: manufacturing systems, computer/communication systems.
Prerequisite: STOR 641, STOR 642
Grading Status: Letter Grade


STOR 851 – Sequential Analysis
Credits: 3
Description: Hypothesis testing and estimation when sample size depends on the observations. Sequential probability ratio tests. Sequential design of experiments. Optimal stopping. Stochastic approximation.
Prerequisite: STOR 635, STOR 655
Grading Status: Letter Grade


STOR 852 – Nonparametric Inference: Rank-Based Methods
Credits: 3
Description: Estimation and testing when the functional form of the population distribution is unknown. Rank, sign, and permutation tests. Optimum nonparametric tests and estimators including simple multivariate problems.
Prerequisite: STOR 635, STOR 655
Grading Status: Letter Grade


STOR 822 – Topics in Discrete Optimization
Credits: 3
Description: Topics may include polynomial algorithms, computational complexity, matching and matroid problems, and the traveling salesman problem.
Prerequisite: STOR 712
Permission: Permission of the instructor for students lacking the prerequisite.
Same As: COMP 822
Grading Status: Letter Grade


STOR 853 – Nonparametric Inference: Smoothing Methods
Credits: 3
Description: Density and regression estimation when no parametric model is assumed. Kernel, spline, and orthogonal series methods. Emphasis on analysis of the smoothing problem and data based smoothing parameter selectors.
Prerequisite: STOR 635, STOR 655
Grading Status: Letter Grade


STOR 854 – Statistical Large Sample Theory
Credits: 3
Description: Asymptotically efficient estimators; maximum likelihood estimators. Asymptotically optimal tests; likelihood ratio tests.
Prerequisite: STOR 635, STOR 655
Grading Status: Letter Grade


STOR 855 – Subsampling Techniques
Credits: 3
Description: Basic subsampling concepts: replicates, empirical c.d.f., U-statistics. Subsampling for i.i.d. data: jackknife, typical-values, bootstrap. Subsampling for dependent or nonidentically distributed data: blockwise and other methods.
Prerequisite: STOR 655
Grading Status: Letter Grade


STOR 856 – Multivariate Analysis
Credits: 3
Description: Required preparation, matrix theory, multivariate normal distributions. Related distributions. Tests and confidence intervals. Multivariate analysis of variance, covariance and regression. Association between subsets of a multivariate normal set. Theory of discriminant, canonical, and factor analysis.
Prerequisite: STOR 655
Grading Status: Letter Grade


STOR 857 – Nonparametric Multivariate Analysis
Credits: 3
Description: Nonparametric MANOVA. Large sample properties of the tests and estimates. Robust procedures in general linear models, including the growth curves. Nonparametric classification problems.
Prerequisite: STOR 852
Grading Status: Letter Grade


STOR 881 – Object Oriented Data Analysis
Credits: 1-3
Description: Object Oriented Data Analysis (OODA) is the statistical analysis of populations of complex objects. Examples include data sets where the data points could be curves, images, shapes, movies, or tree structured objects.
Grading Status: Letter Grade


STOR 890 – Special Problems
Credits: 1-3
Description: Permission of the instructor.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit.


STOR 891 – Special Problems
Credits: 1-3
Description: Permission of the instructor.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit; may be repeated in the same term for different topics.


STOR 892 – Special Topics in Operations Research and Systems Analysis
Credits: 1-3
Description: Permission of the instructor.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit.


STOR 893 – Special Topics
Credits: 1-3
Description: Advance topics in current research in statistics and operations research.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit.


STOR 701 – Statistics and Operations Research Colloquium
Credits: 1
Description: This seminar course is intended to give Ph.D. students exposure to cutting edge research topics in statistics and operations research and assist them in their choice of a dissertation topic. The course also provides a forum for students to meet and learn from major researchers in the field.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit. 10 total credits. 10 total completions.


STOR 702 – Seminar in Teaching
Credits: 1
Description: This seminar course is intended to give Ph.D. students exposure to various issues and pedagogy in teaching statistics and operations research. The course also provides a forum for students to observe and learn from current teaching faculty. Students should register for one credit only. STOR Ph.D. students only.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit. 3 total credits. 3 total completions.


STOR 705 – Operations Research Practice
Credits: 3
Description: Gives students an opportunity to work on an actual operations research project from start to finish under the supervision of a faculty member. Intended exclusively for operations research students.
Prerequisite: STOR 614, STOR 641, STOR 672
Permission: Permission of the instructor for students lacking the prerequisite.
Grading Status: Letter Grade


STOR 765 – Statistical Consulting
Credits: 3
Description: Application of statistics to real problems presented by researchers from the University and local companies and institutes. (Taught over two semesters for a total of 3 credits.)
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit. 3 total credits. 2 total completions.


STOR 790 – Operations Research and Systems Analysis Student Seminar
Credits: 1
Description: Survey of literature in operations research and systems analysis.
Grading Status: Letter Grade


STOR 910 – Directed Reading in Statistics and Operations Research
Credits: 1-3
Description: Students will read selected works under supervision of instructor, and attend discussion meetings. Permission of the instructor.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit. 12 total credits. 12 total completions.


STOR 930 – Advanced Research
Credits: 1-3
Description: Permission of the instructor.
Grading Status: Letter Grade


STOR 940 – Seminar in Theoretical Statistics
Credits: 1-3
Prerequisite: STOR 655
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit.


STOR 950 – Advanced Research
Credits: 0.5-21
Description: Permission of the instructor.
Grading Status: Letter Grade


STOR 960 – Seminar in Theoretical Statistics
Credits: 0.5-21
Prerequisite: STOR 655
Grading Status: Letter Grade


STOR 970 – Practicum
Credits: 1-3
Description: Students work with other organizations (Industrial/Governmental) to gain practical experience in Statistics and Operations Research.
Grading Status: Letter Grade
Repeat Rules: May be repeated for credit.


STOR 992 – Master's (Non-Thesis)
Credits: 3
Description: Permission of instructor.
Grading Status:
Repeat Rules: May be repeated for credit.


STOR 994 – Doctoral Research and Dissertation
Credits: 3
Description: Permission of instructor.
Grading Status:
Repeat Rules: May be repeated for credit.