There are a number of classes across the affiliated departments that can count towards the Operations Research Program requirements, detailed below. The classes that are currently active can be found in the University Course Bulletin in LionPath . Students planning on taking any of the classes listed in this page should check schedule and frequency with the corresponding departments, as these are continuously being updated.
Colloquium requirements: students must enroll in OR 590 Colloquium for 1 credit in each year enrolled in the major graduate program and in residence. The maximum number of OR 590 credits required for a Ph.D dual title or Ph.D minor in OR is 4, and the maximum for a Master’s dual title or minor is 2. Any particular course may satisfy both the graduate major program and those in the Operations Research Program.
Note: some classes are considered equivalent within and across departments. When classes are equivalent, only one can count towards the credit requirements of a specific area and sub-area. Equivalent classes are detailed in the Application Forms.
OR Courses
Statistical Methods:
- ECON 501 – Econometrics
Description: Applications of Statistical Techniques to Economics. - EEFE/ECON 510 – Econometrics I
Description: General linear model, multicolinearity, specification error, autocorrelation, heteroskedasticity, restricted least squares, functional form, dummy variables, limited dependent variables.
Prerequisite: ECON 490 or STAT 462 or STAT 501 - IE 511 – Experimental Design in Engineering
Description: Statistical design and analysis of experiments in engineering; experimental models and experimental designs using the analysis of variance.
Prerequisite: IE 323 - IE 532 – Reliability Engineering
Description: Mathematical definition of concepts in reliability engineering; methods of system reliability calculation; reliability modeling, estimation, and acceptance testing procedures.
Prerequisite: IE 323 or 3 credits in probability and statistics with a prerequisite of calculus - IE 583 – Response Surface Methodology and Process Optimization
Description: Surface Methodologies used for sequential experimentation and optimization of production processes. Statistical design and analysis of such experiments.
Prerequisite: IE 511 or STAT 501 - IE 584 – Time Series Control and Process Adjustment
Description: Design of Time Series-based process controllers for Quality Engineering. Study of the effect of autocorrelation on control chart performance.
Prerequisite: IE 423 - MATH/STAT 414 – Introduction to Probability Theory
Description: probability spaces, discrete and continuous random variables, transformations, expectations, generating functions, conditional distributions, law of large numbers, central limit theorems. Students may take only one course from MATH(STAT) 414 and 418 for credit.
Prerequisite: MATH 230 or MATH 231 - MATH/STAT 415 – Introduction to Mathematical Statistics
Description: A theoretical treatment of statistical inference, including sufficiency, estimation, testing, regression, analysis of variance, and chi-square tests.
Prerequisite: MATH 414 - MATH/STAT 418 – Introduction to Probability and Stochastic Processing for Engineering
Description: Fundamentals and axioms, combinatorial probability, conditional probability and independence, probability laws, random variables, expectation; Chebyshev’s inequality. Students may take only one course from MATH(STAT) 414 and 418 for credit.
Prerequisite: MATH 230 or MATH 231 - SC&IS 535 – Statistical Research Methods for Supply Chain and Information Systems
Description: Current statistical research methods for modeling and analysis of supply chain and information systems.
Prerequisite: 3 credits each in undergraduate accounting, economics, and statistics - STAT 460 – Intermediate Applied Statistics
Description: Review of hypothesis testing, goodness-of-fit tests, regression, correlation analysis, completely randomized designs, randomized complete block designs, latin squares.
Prerequisite: STAT 200, STAT 240, STAT 250, STAT 301, or STAT 401 - STAT 501 – Regression Methods
Description: Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.
Prerequisite: 6 credits in statistics or STAT 451; matrix algebra - STAT 502 – Analysis of Variance and Design of Experiments
Description: Analysis of variance and design concepts; factorial, nested, and unbalanced data; ANCOVA; blocked, Latin square, split-plot, repeated measures designs.
Prerequisite: STAT 462 or STAT 501 - STAT 503 – Design of Experiments
Description: Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs.
Prerequisite: STAT 462 or STAT 501; STAT 502 - STAT 553 – Asymptotic tools
Description: A rigorous but non-measure-theoretic introduction to statistical large-sample theory for Ph.D. students. STAT 553 Asymptotic Tools (3) STAT 553 covers most standard statistical asymptotics theory but does not require any knowledge of measure theory (it does not define convergence with probability one, for example). It covers convergence of random variables in both the univariate and multivariate settings, Slutsky’s theorem(s) and the delta method, the Lindeberg-Feller central limit theorem, power and sample size, likelihood-based estimation and testing, and U-statistics. Although there is no measure theory in the course, it is a mathematically rigorous course and major results are proved. Many common applications of the theory in mathematical statistics are discussed, and most assignments require the use of a computer.
Prerequisite: STAT 513 and STAT 514 - STAT 561 – Statistical Inference I
Description: Classical optimal hypothesis test and confidence regions, Bayesian inference, Bayesian computation, large sample relationship between Bayesian and classical procedures.
Prerequisite: STAT 514; Concurrent: STAT 517 - STAT 562 – Statistical Inference II
Description: Basic limit theorems; asymptotically efficient estimators and tests; local asymptotic analysis; estimating equations and generalized linear models.
Prerequisite: STAT 561
Stochastic Processes:
- EE 560 – Probability, Random Variables, and Stochastic Processes
Description: Review of probability theory and random variables; mathematical description of random signals; linear system response; Wiener, Kalman, and other filtering.
Prerequisite: EE 350; STAT 418 - IE/SC&IS 516 – Applied Stochastic Processes
Description: Study of stochastic processes and their applications to engineering and supply chain and information systems.
Prerequisite: IE 322 or STAT 318 - MATH/STAT 416 – Stochastic Modeling
Description: Review of distribution models, probability generating functions, transforms, convolutions, Markov chains, equilibrium distributions, Poisson process, birth and death processes, estimation.
Prerequisite: MATH 318 OR MATH 414; MATH 230 - MATH/STAT 516 – Stochastic Processes
Description: Markov chains; generating functions; limit theorems; continuous time and renewal processes; martingales, submartingales, and supermartingales; diffusion processes; applications.
Prerequisite: MATH 416 - MATH/STAT 519 – Topics in Stochastic Processes
Description: Selected topics in stochastic processes, including Markov and Wiener processes; stochastic integrals, optimization, and control; optimal filtering.
Prerequisite: STAT 516, STAT 517 - ME 577 – Stochastic Systems for Science and Engineering
Description: The course develops the theory of stochastic processes and linear and nonlinear stochastic differential equations for applications to science and engineering.
Prerequisite: MATH 414 or MATH 418; ME 550 or MATH 501 - METEO 527 – Data Assimilation
Description: Data assimilation is the process of finding the best estimate of the state by statistically combining model forecasts, observations, and their respective uncertainties.
Prerequisite: Basic knowledge of probability theory, calculus, linear algebra/matrices, and computer programming is expected. - STAT 515 – Stochastic Processes and Monte Carlo Methods
Description: Conditional probability and expectation, Markov chains, the exponential distribution and Poisson processes.
Prerequisite: MATH 414, STAT 414, or STAT 513
Linear Optimization:
- EEFE 527 – Quantitative Methods I
Description: The first part of the course reviews the foundations of the mathematical analysis with the goal of modeling feasibility; i.e., the set of possible choices. This prepares us to next move to modeling the optimal choice with an extended presentation on optimization theory and application in the static setting. The final part of the course moves on to the methods for engaging in dynamic optimization.
Prerequisite: EEFE 512, ECON 502 - IE 405 – Deterministic Models in Operations Research
Description: Deterministic models in operation research including linear programming, flows in networks, project management, transportation and assignment models and integer programming.
Prerequisite: MATH 220 - IE 505 – Linear Programming
Description: An accelerated treatment of the main theorems of linear programming and duality structures plus introduction to numerical and computational aspects of solving large-scale problems.
Prerequisite: IE 405 - MATH 484 – Linear Programs and Related Problems
Description: Introduction to theory and applications of linear programming; the simplex algorithm and newer methods of solution; duality theory.
Prerequisite: MATH 220; MATH 230 or MATH 231 - CHE 512 – Optimization and Biological Networks
Description: Mathematical optimization, formulation and solution techniques for linear, nonlinear, and mixed-integer problems; optimization-based tools for reconstruction, analysis, and redesign of biological networks.
Deterministic Optimization:
- ECON 534 – Game Theory
Description: Foundations of current research in game theory. This is an advanced graduate course in game theory and its applications to economics. The course content is mathematical in nature and emphasizes formal statements of key propositions and their proofs. It begins by presenting two alternative ways in which a game may be represented: the extensive (or tree) form and the strategic (or normal) form. The relationship between these two representations is studied and the key idea of a strategy is introduced. Pre-equilibrium notions of dominance, iterated dominance and rationalizability are studied. Nash’s fundamental theorem on the existence of equilibrium in finite games is proved. Strategic form based refinements of Nash equilibrium, including perfect, proper and stable equilibria are considered. Extensive form based refinements, including subgame perfection and sequential equilibrium are also considered and compared. Harsanyi’s conception of a game of incomplete information is introduced. Other subjects covered include repeated games and the folk theorem, bargaining, common knowledge. Additional topics of current interests may also be covered.
Prerequisite: ECON 521 or permission of program - IE 468 – Optimization Modeling and Methods
Description: Mathematical modeling of linear, integer, and nonlinear programming problems and computational methods for solving these classes of problems.
Prerequisite: IE 405, MATH 231 - IE 510 – Integer Programming
Description: Study of advanced topics in mathematical programming; emphasis on large-scale systems involving integer variables.
Prerequisite: IE 512 - IE 512 – Graph Theory and Networks in Management
Description: Graph and network theory; application to problems of flows in networks, transportation and assignment problems, pert/CPM, facilities planning.
Prerequisite: IE 425 - IE 520 – Multiple Criteria Optimization
Description: Study of concepts and methods in analysis of systems involving multiple objectives with applications to engineering, economic, and environmental systems.
Prerequisite: IE 405 or INS 427 - IE 521 – Nonlinear Programming
Description: Fundamental theory of optimization including classical optimization, convex analysis, optimality conditions and duality, algorithmic solution strategies, variational methods in optimization.
Prerequisite: IE 505 - IE/EE 585 – Convex Optimization
Description: This course is designed to provide students with necessary skills to recognize or build convex optimization problems coming from diverse application areas and to solve them efficiently. It consists of five parts: 1) convex sets, 2) convex functions, 3) convex optimization, 4) algorithms and 5) real life applications.
Prerequisite: IE 505 - IE 588 – Nonlinear Networks
Description: Foundation in congestion games, including elements of non-cooperative game theory, equilibrium network flows, Braess paradox, and the price of anarchy. This course examines the theory of congestion games, developed originally to describe flows on congested transport networks but recently embraced to model data networks.
Prerequisite: IE 505 - IE 589 – Dynamic Optimization and Differential Games
Description: Dynamic optimization and dynamic non-cooperative games emphasizing industrial applications. This course provides an introduction to dynamic optimization and dynamic noncooperative games from the perspective of infinite dimensional mathematical programming and differential variational inequalities in topological vector spaces.
Prerequisite: IE 425; IE 505; IE 521 (can be taken concurrently) - MATH 486 – Mathematical Theory of Games
Description: Basic theorems, concepts, and methods in the mathematical study of games of strategy; determination of optimal play when possible.
Prerequisite: MATH 220 - MATH/CSE 555 – Numerical Optimization Techniques
Description: Unconstrained and constrained optimization methods, linear and quadratic programming, software issues, ellipsoid and Karmarkar’s algorithm, global optimization, parallelism in optimization.
Prerequisite: CMPSC 456 - ME 444 – Engineering Optimization
Description: Problem formulation, algorithms and computer solution of various engineering optimization problems.
Prerequisite: MATH 220; MATH 230 or MATH 231; CMPSC 201 or CMPSC 202 or CMPSC 200 - SC&IS 525- Supply Chain Optimization
Description: Introduction to theory and practice of optimization methods and models for analyzing and improving the performance of supply chain environments.
Prerequisite: prior coursework in linear algebra and calculus
Stochastic Optimization:
- EME 523 – Stochastic Optimization Methods of Energy and Environmental Systems
Description: This course covers the theory and implementation of computational methods for stochastic simulation and stochastic optimization, with an emphasis on algorithms and implementation. The course emphasizes the quantitative analysis or numerical modeling of complex systems in fields such as civil, environmental, energy, mechanical, and industrial engineering or energy, environmental, and natural resource economics. Topics include Monte Carlo simulation, quasi-random and pseudorandom sampling methods, Markov Chains, Dynamic Programming, Approximate Dynamic Programming, and Stochastic Programming decomposition techniques.
Prerequisite: EME 501 or IE 505 - IE/SC&IS 519 – Dynamic Programming
Description: This course presents the basic theory and applications of dynamic programming. The focus of the course will be on the theory of Markov decision processes (MDP), which provides an analytical tool to optimally control the behavior of a Markov Chain. The students will learn fundamental MDP models, computational methods and applications in supply chain and information systems, including production and inventory control, quality control, logistics, scheduling, queueing network, and economic problem.
Prerequisite: IE 516 or SC&IS 516 or equivalent
Numerical Methods:
- CE 597 – Computational Analysis of Randomness in Engineering
Description: Probability theory, simulation methods (mote carlo, MCMC), fragility estimation, reliability, simulation of random processes and fields, Bayesian methods and updating. - GEOG 485 – GIS Programming and Software Development
Description: The course focuses on solving geographic problems by modifying and automating generic Geographic Information System (GIS) software through programming. In GEOG 485, students use the Python programming language to write and modify scripts that add functionality to desktop GIS tools and to automate geospatial analysis processes. No previous programming experience is assumed. Core topics covered in this class include object-oriented programming, component object model technologies, object model diagrams, loops, if-then constructs, and modular code design, and situates these topics in the geospatial workflow through their integration with maps, layers, spatial data tables, and spatial analysis methods. Students who successfully complete the course can automate repetitive GIS tasks, customize GIS interfaces, and share their geospatial software development work with others. - MATH/CMPSC 451 – Numerical Computations
Description: Algorithms for interpolation, approximation, integration, nonlinear equations, linear systems, fast FOURIER transform, and differential equations emphasizing computational properties and implementation. Students may take only one course for credit from MATH 451 and 455.
Prerequisite: CMPSC 201C, CMPSC 201, or CSE 103; MATH 230 or MATH 231 - MATH 455/CMPSC – Introduction to Numerical Analysis I
Description: Floating point computation, numerical rootfinding, interpolation, numerical quadrature, direct methods for linear systems. Students may take only one course for credit from MATH 451 and MATH 455.
Prerequisite: CMPSC 201C, CMPSC 201F, or CSE 103; MATH 220; MATH 230 or MATH 231 - MATH/CMPSC 456 – Introduction to Numerical Analysis II
Description: Polynomial and piecewise polynomial approximation, matrix least squares problems, numerical solution of eigenvalue problems, numerical solution of ordinary differential equations.
Prerequisite: MATH 455 - CSE/MATH 550 – Numerical Linear Algebra
Description: Solution of linear systems, sparse matrix techniques, linear least squares, singular value decomposition, numerical computation of eigenvalues and eigenvectors.
Prerequisite: MATH 441 or MATH 456 - MATH 553 – Introduction to Approximation Theory
Description: Interpolation; remainder theory; approximation of functions; error analysis; orthogonal polynomials; approximation of linear functionals; functional analysis applied to numerical analysis.
Prerequisite: MATH 401, 3 credits in Computer Science and Engineering
Simulation Methods:
- IE 453 – Simulation Modeling for Decision Support
Description: Introduction of concepts of simulation modeling and analysis, with application to manufacturing and production systems.
Prerequisite: CMPSC 201C or CMPSC 201F ;IE 323, IE 425 - IE 522 – Discrete Event Systems Simulation
Description: Fundamentals of discrete event simulation, including event scheduling, time advance mechanisms, random variate generation, and output analysis.
Prerequisite: IE 425 - SC&IS 545 – Supply Chain Systems Simulation
Description: Application of computer simulation to analysis and design of supply chain and information systems design; simulation experiments in SC&IS research.
Prerequisite: 3 credits of computer programming
Data Science / Data Analytics:
- CMPSC 410 – Programming Models for Big Data
Description: This course introduces modern programming models and related software stacks for performing scalable data analytics and discovery tasks over massive and/or high dimensional datasets. The learning objectives of the course are that the students are able to choose appropriate programming models for a big data application, understand the tradeoff of such choice, and be able to leverage state-of-the art cyber infrastructures to develop scalable data analytics or discovery tasks..
Enforced Prerequisite: CMPSC 122 and DS 220. Recommended Preparation: DS 310 or CMPSC 448 - CMPSC 448 – Machine Learning and Algorithmic AI
Description: Evaluation and use of machine learning models; algorithmic elements of artificial intelligence.
Prerequisite: IE 453 - CSE/STAT 584 – Machine Learning: Tools and Algorithms
Description: Computational methods for modern machine learning models, including applications to big data and non-differentiable objective functions. - EE 456 – Introduction to Neural Networks
Description: Artificial Neural Networks as a solving tool for difficult problems for which conventional methods are not applicable.
Prerequisite: CMPSC201 or CMPSC202; MATH 220 - EE 556 – Graphs, Algorithms, and Neural Networks
Description: Examine neural networks by exploiting graph theory for offering alternate solutions to classical problems in signal processing and control. - EE 582 – Adaptive and Learning Systems
Description: Adaptive and learning control systems; system identification; performance indices; gradient, stochastic approximation, controlled random search methods; introduction to pattern recognition.
Prerequisite: EE 580 - EME 524 – Machine Learning for Energy and Mineral Engineering Problems
Description: This course provides an overview of the application of machine learning algorithms to problems in energy and mineral engineering. The course addresses the strengths and weaknesses of various machine learning approaches, as well as appropriate testing and validation techniques for these complex models. Topics include machine learning applications in regression, classification, design optimization, and risk analysis. An emphasis of this course is for students to apply these methods to specific research problems of interest. Students with some background in statistics, but no previous formal training in machine learning algorithms will find this course most useful. - GEOG 463 – Geospatial Information Management
Description: This course examines geospatial data representations and algorithmic techniques that apply to spatially-organized data in digital form.
Prerequisite: GEOG 363 - GEOG 465 – Advanced Geographic Information Systems Modeling
Description: Before taking GEOG 465, students will have learned the fundamentals and principles of GIS. This course extends such knowledge to modeling geospatial scenarios. A GIS model simulates real-world phenomena, including environmental, physical and natural features, as well as social features such as demographic, transportation and origin-destination data. We will model raster and vector data types with an emphasis on multi-criteria GIS operations, using ArcGIS, R and potential other software packages. Upon completion of the course, successful students will have achieved the following objectives and learning outcomes: Students will be able to: a) discuss basic GIS modeling principles; b) find, use, store, retrieve and evaluate GIS datasets; c) describe capabilities and limitations of GIS methods and models; e) implement capabilities, tools and packages in ArcMap GIS and R environments; f) use R for programming tasks such as looping and branching; g) evaluate an external software program and create a model using this software; h) exhibit ability to design and carry out spatial analyses using GIS; i) communicate the results of geographic analyses to others, both in oral and in written form; j) analyze spatial data sets in terms of predictability and uncertainty; and k) calibrate models based on real-world datasets.
Prerequisite: GEOG 363 - GEOG 580 – Geovisual Analytics
Description: Traces of geographic information reflected in digital data continuously increase in volume, variety, velocity, and variability. New geographic data sources, together with advancements in high-performance computing, present opportunities to dissect complex real-world problems in unprecedented ways. Human cognitive capability, however, remains constant, thus limiting our ability to extract value and insight from this data deluge. Geovisual analytics is the emerging science of analytical reasoning supported by interactive geovisualization, computational methods, and user-centered design. Geovisual analytics constitutes an essential subdomain in the broader field of Spatial Data Science. This seminar investigates the role of geovisual analytics in facilitating the human sensemaking process through (a) a survey and synthesis of research challenges surrounding the design, use, and evaluation of geovisual analytics systems and (b) exercises to design geovisual analytics approaches to tackle complex problem contexts such as those found in crisis management, epidemiology, and transportation domains.
Prerequisite: GEOG 486 - GEOG 586 – Geographical Information Analysis
Description: Choosing and applying analytical methods for geospatial data, including point pattern analysis, interpolation, surface analysis, overlay analysis, and spatial autocorrelation
Prerequisite: GEOG 485 or GEOG 486 or GEOG 487 - IE/CSE/EDSGN/IST 561 – Data Mining Driven Design
Description: The study and application of data mining/machine learning (DM/ML) techniques in multidisciplinary design. - IE 562 – Computational Foundations of Smart Systems
Description: Intelligent computational techniques for the design and implementation of smart systems. - IE 575 – Foundations of Predictive Analytics
Description: Survey course on the key topics in predictive analytics.
Prerequisite: IE 323, STAT 500 or equivalent - IE 582 – Engineering Analytics
Description: Students will learn advanced information technology, network science, big data, descriptive and predictive analytics, for manufacturing and service systems. - STAT 508 – Applied Data Mining and Statistical Learning
Description: With rapid advances in information technology, the field of Applied Statistics and Data Science has witnessed an explosive growth in the capabilities to generate and collect data. In the business world, very large databases on commercial transactions are generated by retailers. Huge amounts of scientific data are generated in various fields as well using a wide assortment of high throughput technologies. The internet provides another example of billions of web pages consisting of textual and multimedia information that is used by millions of people. Analyzing large complex bodies of data systematically and efficiently remains a challenging problem. This course addresses this problem by covering techniques and new software that automate the analysis and exploration of large complex data sets. Data Mining methods are introduced by using examples to demonstrate the power of the statistical methods for exploring structure in data sets, discovering patterns in data, making predictions, and reducing the dimensionality by Principal Component Analysis (PCA) and other tools for visualization of high dimensional data. Exploratory data analysis, classification methods, clustering methods, and other statistical and algorithmic tools are presented and applied to actual data. In particular, the course investigates classification methods (supervised learning), and clustering methods (unsupervised learning), and other statistical and algorithmic tools as they are applied to actual data. In addition, data mining and learning techniques developed in fields other than statistics, e.g., machine learning and signal processing, will also be reviewed. The Statistics graduate program also offers more in-depth courses on data mining, STAT 557 and STAT 558. This course focuses on how to use software to investigate and analyze large data sets, whereas STAT 557 and STAT 558 focus more on writing data mining algorithms and the computational aspects of algorithm implementation.
Prerequisite: STAT 501 ; STAT 462 - IST/STAT 557 – Data mining I
Description: This course on data mining will cover methodology, major software tools and applications in this field. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining.
Prerequisite: STAT 318 or STAT 416 and basic programming skills - IST/STAT 558 – Data mining II
Description: Advanced data mining techniques: temporal pattern mining, network mining, boosting, discriminative models, generative models, data warehouse, and choosing mining algorithms.
Prerequisite: STAT 557 or IST 557
- ABE 559 – Biological and Agricultural Systems Simulation
Description: Continuous simulation modeling of biological and physical systems, numerical simulation techniques, validation and verification, difference measures, sensitivity analysis.
Prerequisite: MATH 111 or MATH 141 - BRS 429W – Biorenewable Systems Analysis and Management
Description: Systems analysis and optimization techniques including an introduction to systems theory, qualitative and quantitative analysis, linear programming, waiting line models, PERT/CPM, minimal spanning tree, calculus methods, simulation modeling for decision making, inventory, and energy audits. All topics are presented in the form of case studies that require the students to solve problems in realistic production and processing scenarios. The course also provides a writing-intensive structure.
Prerequisite: BRS 422 - CE 521 – Transportation Networks and System Analysis
Description: Will cover basic concepts of transportation network analysis, as well as explore some applications. Network analysis answers questions such as, “where will people change their routes if I build a new freeway to Pittsburg?”, “where will the congestion hotspots be 30 years from now?”, or “how will traffic patterns change if a bypass toll road is built for I-95 around Philadelphia?” Basically, any problem which requires a “big-picture” view of what routes people will take relies on a network model. The focus is on a large area, such as a city or metropolitan region, rather than on a specific intersection or roadway. By the end of this course, you will have the tools to answer these questions. You will be able to formulate a variety of transportation planning problems as network models, and have the practical knowledge needed to solve them. Furthermore, you will have a conceptual understanding of these models which allows you to understand and critically evaluate model results which others may present to you.
Prerequisite: This course has no formal prerequisites but does require a significant amount of calculus – if your calculus is a bit rusty, it may not be a bad idea to review. This course may require Python for homework assignments, so past programming experience (MATLAB, C, Python, Java, etc.) is helpful. - CE 525 – Transportation Operations
Description: Tools for analyzing transportation operations, including: properties of traffic streams, queuing, traffic dynamics, networks, probability and estimation of traffic properties.
Prerequisite: CE 423 - CE 529 – Infrastructure Systems Analysis and Decision Making
Description: Focuses on thephysical infrustructure systems that provide essential public services, including transportation, energy, water, communication, etc. These complex, large-scale, expensive systems must be planned for and managed. This course emphasizes different tools (including basic economic theory, mathematical modeling, and optimization techniques) that can be used to study these complex theory, drawing examples from transportation. This includes the evaluation of infrastructure investments; better informing the data collection and inspection process; modeling deterioration of infrastructure components such as pavement; and maintenance and repair decision-making at both the single facility and system level.
Prerequisite: none - CMPEN 431 – Introduction to Computer Architecture
Description: Introduction to Computer Architecture (3). This course will introduce students to the architecture-level design issues of a computer system. They will apply their knowledge of digital logic design to explore the high-level interaction of the individual computer system hardware components. Concepts of sequential and parallel architecture including the interaction of different memory components, their layout and placement, communication among multiple processors, effects of pipelining, and performance issues, will be covered.
Prerequisite: CMPEN331 or CMPEN371m - CMPSC 431W – Database Management Systems
Description: Topics include: conceptual data modeling, relational data model, relational query languages, schema normalization, database/Internet applications, and database system issues. - CMPSC 442 – Artificial Intelligence
Description: Introduction to the theory, research paradigms, implementation techniques, and philosophies of artificial intelligence.
Prerequisite: CMPSC122 or equivalent; Concurrent: CMPSC465 - CMPSC 465 – Data Structures and Algorithms
Description: Fundamental concepts of computer science: data structures, analysis of algorithms, recursion, trees, sets, graphs, sorting.
Prerequisite: CMPSC122; CMPSC360 or MATH 311W - CSE 556 – Finite Element Methods
Description: Sobolev spaces, variational formulations of boundary value problems; piecewise polynomial approximation theory, convergence and stability, special methods and applications.
Prerequisite: MATH 502 , MATH 552 - CSE 562 – Probabilistic Algorithms
Description: Design and analysis of probabilistic algorithms, reliability problems, probabilistic complexity classes, lower bounds.
Prerequisite: CSE 565 - CSE 564 – Complexity of Combinatorial Problems
Description: NP-completeness theory; approximation and heuristic techniques; discrete scheduling; additional complexity classes.
Prerequisite: CSE 565 - CSE 565 – Algorithm Design and Analysis
Description: An introduction to algorithmic design and analysis.
Prerequisite: CMPSC 465 - ECON 402 – Decision Making and Strategy in Economics
Description: Development and application of the tools for decision making under uncertainty and for game theoretic analysis of economic problems.
Prerequisite: ECON 302 and (ECON 106 or SCM 200 or STAT 200) - ECON 500 – Introduction to Mathematical Economics
Description: Mathematical Economics: Applications of Mathematical Techniques to Economics. - ECON 521 – Advanced Microeconomic Theory
Description: Theory of consumer behavior; theory of the firm; price determination in product and factor markets; introduction to welfare economics. - ECON 589 – Seminar in Econometric Theory
Description: Theories and methods relevant to the application of statistical methods to economics.
Prerequisite: ECON 510 - EE 580 – Linear Control Systems
Description: This course provides a mathematical foundation that will enable students to understand and apply linear state space concepts to the analysis and synthesis of control laws.
Prerequisite: EE 380 - EE 581 – Optimal Control
Description: Variational methods in control system design; classical calculus of variations, dynamic programming, maximum principle; optimal digital control systems; state estimation.
Prerequisite: EE 580 - EE 597 – Reinforcement Learning
Description: [Need to ask Dr. Ventura for the syllabus]
Prerequisite: [Do not know yet] - EEFE 530 – Applied Microeconomics II
Description: This course is designed to: (1) expose students to the most common econometric and statistical techniques used in applied microeconomic research and (2) give students an overview of the different types of micro data and the most common methods used to manipulate these data to create additional data sets and variables.
Prerequisite: EEFE 512, EEFE 510 - EEFE 531 – Applied Microeconomics I
Description: In this course, we will study microeconometrics, a subfield that encompasses specification as well as a variety of estimation, computational, and simulation methods that allow us to pursue specification and parameterization of econometric models suitable for analyzing micro-level data. We will see that these methods support an enriched basis for examining the validity of microeconomic theory, and also extend the analytics feasibly tackled by microeconomics. At the micro-level of empirical analysis, we will see our theory predicts high frequencies of corner solutions, abrupt switching, and discontinuities.
Prerequisite: EEFE 512 or ECON 502, EEFE 510 or ECON 510, EEFE 511 or ECON 510 - EEFE 532 – Applied Computational Economics
Description: Economists often find themselves in situations where closed-form solutions do not exist or econometric estimation is inappropriate due to data limitations or the nature of the problem. In these cases, numerical approaches, using computer-based methods, may be an economist’s best option. In this course, we will explore four topics in the field of computational economics: computable general equilibrium modeling, growth modeling, uncertainty and formal monte carlo analysis, and agent-based modeling.
Prerequisite: EEFE 512 - EME 522 – Computational Methods for Electric Power Systems Analysis
Description: This course covers the formulation of and solution methods for a full range of economic-engineering investment and operations problems for electric power systems. Application problems include economic dispatch, unit commitment, optimal power flow, generation capacity expansion, transmission expansion, and modeling of competitive electricity markets. Solution methods include linear programming, mixed integer programming, decomposition methods for stochastic programming (e.g., Lagrangian Relaxation, Benders Decomposition), and mixed complementarity problems, with an emphasis on numerical implementation.
Prerequisite: EME 501 or IE 505 - EMSC 460 – Environmental Data Analytics
Description: This course introduces various data analystics methods focused on machine learning for Earth and environmental sciences. A range of supervised and unsupervised methods for regression, classification, and clustering problems will be discussed with real-world examples, including but not limited to climatological data, biodiversity data, remote sensing imagery classification, and geomorphological analysis.
Prerequisite: (GEOG 365 or GEOG 485 or GEOG 489 or GEOGSC 210 or GEOSC 44 or METEO 273 or EME 210 or MATSE 219 or CMPSC 101 or CMPSC 200 or CMPSC 201) and (MAT 110 or MATH 140 or MATH 140A or MATH 140B or MATH 140E or MATH 140G or MATH 140H) - EMSE 497 – Environmental Data Analytics
Description: This course will focus on the basic principles and environmental and earth science applications of machine learning algorithms, including regression (e.g., OLS, Ridge, LASSO, PCR), gradient descent optimization, classification (e.g., logistic regression, naïve Bayes, decision trees, random forest, support vector machine), neural networks and deep learning, and clustering (e.g., k-means, hierarchical, self-organizing map) techniques, etc. The class projects will include practical programming exercises in using these techniques for various real-world datasets.
Prerequisite: (GEOG 365 or GEOG 485 or GEOG 489 or GEOSC 210 or GEOSC 444 or METEO 273 or EME 210 or MATSE 219 or CMPSC 101 or CMPSC 200 or CMPSC 201) and (MATH 110 or MATH 140 or MATH 140A or MATH 140B or MATH 140E or MATH 140G or MATH 140H) - ERM 412 – Resource Systems Analysis
Description: The concept of systems; techniques of analysis, including input/output, mathematical programming, and simulation; application to resource systems.
Prerequisite: BIOL 220W, ERM 151, ERM 300, and STAT 240; MATH 111 or MATH 141 - GEOG 464 – Advanced Spatial Analysis
Description: Skills and knowledge for applying quantitative methods to analyze information with spatial distributions. GEOG 464GEOG 464 Analysis and GIS (3)(BA) This course meets the Bachelor of Arts degree requirements. Geography 464 is a course in methods for analyzing spatial data–methods that can and should be used when the geographic arrangement of a set of measured observations is thought to be of significance in explaining the values of those measurements. The methods of spatial analysis looked at in this course can be distinguished from conventional statistical analysis techniques, and also from many of the analysis functions programmed into many GIS packages. In fact several spatial analysis methods considered in this course the result of attempts to alter and extend conventional statistical techniques to take account of locational similarity and distance measurements (which is why Geography 364 or an equivalent primer in introductory statistical methods is a prerequisite). This means that the techniques that will be introduced in the course are often quite complex mathematically or statistically. Having said this, the overall goal of the course is to provide sufficient conceptual understanding and practical experience so that students become competent in selecting and applying methods appropriate to a variety of frequently-encountered analytical situations.
Prerequisite: GEOG 364 - GEOG 479 – Spatial Data Science for Cyber and Human Social Networks
Description: This course examines the nexus of geospatial intelligence analysis with cyberspace, the geopolitics of cyber threats, the politics of censorship and hacking, public safety, disaster response, and humanitarian relief. Students will utilize a range of cyber data, systems, and spatial sciences to examine human social networks of the Internet. The course will be centered on geospatial intelligence with emphases on technology, information theory, and cyber and human networks.
Prerequisite: GEOG 160 or GEOG 482 - GEOG 560 – Seminar in Geographic Information Science
Description: Geographic information science problems/theory, e.g. GIS, cartography, remote sensing, spatial analysis, modeling. - GEOG 850 – Location Intelligence for Business
Description: In business, the application of maps and mapping technology ranges from a long-standing presence (commercial real estate, retail, and logistics) to nascent analytical applications across different industries. The momentum for commercial applications that encompass GIS, geospatial intelligence (GEOINT) technologies, and geospatial intelligence analysis is growing. In businesses, geospatial attributes are being combined with enterprise-wide databases. GIS and GEOINT tools and methodologies can now be folded into the more mainstream information technology (IT) applications of business intelligence (BI) to formulate location intelligence (LI) applications, products, and services. This course explores and applies the key geospatial intelligence principles involved in site selection, market analysis, risk and crisis management, and logistics, providing opportunities for students to solve those problems with contemporary geospatial tools and datasets. This course provides a foundation for spatial thinking and analysis in commercial settings, and experience with contemporary mapping and analysis tools for professional applications of location intelligence.
Prerequisite: GEOG 482 - GEOG 855 – Spatial Data Analytics for Transportation
Description: This course explores the spatial data science and technology associated with the transportation industry. This interdisciplinary field is often referred to as GIS-T. There is a natural synergy between GIS and transportation, which has resulted in a number of specialized techniques and a wide variety of GIS-T applications. To appreciate the value GIS brings to the transportation industry, students need to have some understanding of the business of transportation and the challenges and problems those in the industry face. Consequently, they will learn about a number of subdisciplines within transportation and examine how GIS has been applied to each. Students will also explore some of the key organizations in the transportation industry who use GIS and learn firsthand from transportation professionals, representing a variety of specialized fields, about the role GIS plays for them. Throughout the course, students will study GIS concepts and techniques which are fundamental to transportation, such as transportation networks and linear referencing systems. In addition, they will have the opportunity to explore a number of GIS applications and tools related to transportation. Due to the overall breadth of the transportation industry, the course will focus primarily on the largest application areas: highway and mass transit. We will, however, examine other significant modes, including aviation, maritime, pedestrian, and bike transit. Furthermore, while much of the course content is oriented around the U.S. transportation industry, students will also look at GIS-T applications and trends in other parts of the world.
Prerequisite: GEOG 482 - GEOG 858 – Spatial Data Science for Emergency Management
Description: Geospatial perspectives and technologies have a major role to play in planning for and responding to emergencies. As is true with other analytical paradigms, geospatial systems and technologies – from aerial mapping techniques to data acquisition – are changing rapidly. Emergency management is also changing quickly as the frequency and magnitude of crises and disasters are increasing, and more and more people and places are being impacted. GEOG 858 helps students develop proficiency in the theoretical, analytical, and technical perspectives required to support all stages of emergency (crisis or disaster) management activities with geospatial solutions, ranging from small-scale emergency management efforts to large-scale disasters and humanitarian crises. Topics covered in GEOG 858 will include advancements in geospatial data collection, geospatial data processing and analysis capabilities, unmanned aerial systems (UAS), geospatial artificial intelligence (geoAI), volunteered geographic information (VGI), geospatially-oriented social media, and others.
Prerequisite: GEOG 483 - GEOSC 450 – Risk Analysis
Description: An introduction to concepts and methods of quantitative risk analysis with focus on water, climate, and energy related risks. Key concepts include probability, impacts, risk, uncertainty, statistical estimation, and decision-making under uncertainty,
Prerequisite: MATH 140 or MATH 110, Introductory Earth Science or Geoscience class, Introductory Statistics class (e.g. STAT 200, or STAT 301, or ENNEC 473), or permission of program. - IE 402 – Advanced Engineering Economy
Description: Concepts and techniques of analyses useful in evaluating engineering projects under deterministic and uncertain conditions.
Prerequisite: IE 302, IE 322, IE 405 - IE 425 – Stochastic Models in Operations Research
Description: An introduction to the method and techniques of mathematical decision making, including inventory, replacement, allocation, and waiting line problems.
Prerequisite: IE 405 - IE 454 – Applied Decision Analysis
Description: Theory and practice of decision analysis applied to engineering problems.
Prerequisite: IE 322 - IE 478 – Retail Services Engineering
Description: To understand modern retail industry with focus on their operations and information technologies.
Prerequisite: IE 322 - IE 507 – Operations Research: Scheduling Models
Description: Scheduling models with simultaneous job arrival and probabilistic job arrival, network scheduling, and scheduling simulation techniques.
Prerequisite: IE 425 - IE 509 – Operations Research: Waiting Line Models
Description: Waiting line models including models with infinite queues, finite queues, single and multiple servers under various priorities and disciplines.
Prerequisite: IE 516 - IE 517 – Models and Technologies for Financial Services
Description: The objective of this course is to study current and emerging electronic financial services used in enterprise and global supply chain operations. The emphasis will be on technologies used in these services and how they can be used for improving operations.
Prerequisite: IE 505 or IE 516 - IE 530 – Financial Engineering
Description: Financial option pricing and portfolio design relevant to investment decision making.
Prerequisite: IE 516 - IE 566 – Quality Control
Description: Advanced quality assurance and control topics, including multivariate methods, economic design for control and acceptance, dimensioning, tolerancing, and error analysis.
Prerequisite: IE 423 - IE 567 – Distributed Systems and Control
Description: The objective of this course is to study current research and engineering challenges in distributed systems and control in the context of manufacturing and service enterprises, and supply chains. Emphasis will be placed on understanding the dynamics and computational aspects of decision making and control algorithms in integrated enterprises. This course deals with the multidisciplinary aspects of controls, computing, and communication in this rapidly evolving area.
Prerequisite: Familiarity with high-level programming. Students are expected to be proficient in undergraduate level analysis pertaining to differential equations. - IE 568 – Healthcare Systems Engineering
Description: Quantitative methods to analyze and improve healthcare systems.
Prerequisite: IE 405, IE 425 and IE 433 - IE/SC&IS 570 – Supply Chain Engineering
Description: Use of operations research models and methods for solving problems in supply chain systems.
Prerequisite: IE 405, IE 425 or SC&IS 510 - MATH 485 – Graph Theory
Description: Introduction to the theory and applications of graphs and directed graphs. Emphasis on the fundamental theorems and their proofs.
Prerequisite: MATH 311W - MATH 486 – Mathematical Theory of Games
Description: Basic theorems, concepts, and methods in the mathematical study of games of strategy; determination of optimal play when possible.
Prerequisite: MATH 220 - ME/EE 550 – Foundations of Engineering Systems Analysis
Description: Analytical methods are developed using the vector space approach for solving control and estimation problems; examples from different engineering applications.
Prerequisite: MATH 436 - ME 565 – Optimal Design of Mechanical and Structural Systems
Description: Application of numerical optimization techniques to design mechanical and structural systems; design sensitivity analysis. - MKTG 555 – Marketing Models
Description: Topics in the model building approach to marketing decision making, focusing on current research issues. - PHYS 580 – Elements of Network Science and Its Applications
Description: Introduction to elements of network theory used to describe and model complex networks; applictions in social, biological, and technological networks. PHYS 580 Elements of Network Science and Its Applications (3) Network Science is the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena. This class will focus on four main questions asked by network science: (i) How do we use data analysis methods to determine or infer the interaction graphs underlying complex systems? (ii) How can we characterize the organizational features of large-scale networks? (iii) What are the mechanisms that determine the common topological features of a wide variety of networks? (iv) To what extent does the organization of the interaction network underlying a complex system determine the dynamical behavior (e.g. steady state or oscillations) of the system? Applications in social, biological and technological networks will be examined. As Network Science is an interdisciplinary field of research, the course is open and should be of interest to a wide range of graduate students in degree programs in physics, social sciences, life sciences, mathematics, engineering, and computer science. - PNG 430 – Reservoir Modeling
Description: The numerical simulation of petroleum reservoir processes by the use of models; scaling criteria and network flow.
Prerequisite: MATH 251, PNG 410; CMPSC 201C or CMPSC 201F - PNG 511 – Numerical Solution of the Partial Differential Equations of Flow in Porous Media
Description: Differencing schemes for the partial differential equations of single-phase flow; application to flow of gas and mixing in porous media - PNG 512 – Numerical Reservoir Simulation
Description: Mathematical analysis of complex reservoir behavior and combination drives; numerical methods for the solution of behavior equations; recent developments. - SC&IS 505 – Management Information Systems Research
Description: Research problems and issues in supply chain and information systems. - SC&IS 510 – Introduction to Supply Chain and Information Systems
Description: Introduction to the strategic framework, issues, and methods for integrating supply and demand management within and across companies. - SC&IS 520 – Principles of SC&IS I
Description: Initial course on principles of supply chain and information systems with special emphasis on potential research topics.
Prerequisite: SC&IS 510 - SC&IS 530 – Principles of SC&IS II
Description: Sequel on principles of supply chain and information systems with special emphasis on potential research topics.
Prerequisite: SC&IS 510 - SC&IS 546 – Procurement and Supply Management
Description: Analysis, planning, and management of domestic and international procurement and supply activities. SCIS 546 Procurement and Supply Management (3) SCIS 546 provides an overview of procurement and supply management in the context of domestic and global supply-chain networks. Special emphasis is given to strategic sourcing relationships, supply management “best practices,” and E-perspectives on supply management. The course uses problem-based learning and emphasizes the case method. The goal is to learn through the application of course materials to relevant supply management case problems and scenarios. Collaboration in case preparation is required. Student evaluations are based on class participation, individual and group assignments, and exams. This course will be offered during the Fall semester with resident enrollment limits set at 20 students. - SC&IS 565 – Supply Chain Strategy
Description: Strategies, issues and best practices in technology adoption, change management, financial/capability assessments, critical aspects of relationship management in supply-chain networks. SC&IS 565 Supply Chain Strategy (3) The course focuses on the strategic design and the effective operation of supply chains. It specifically seeks to integrate topics foundation course and to engage students in the critical analysis and in probing discussions of specific supply chain leadership issues. Special emphasis is given to supply chain technology adoption, change management, shareholder value assessment, capability assessment, relationship management, and performance metrics.
Prerequisite: SC&IS 510 - STAT 510 – Applied Time Series Analysis
Description: Identification of models for empirical data collected over time. Use of models in forecasting.
Prerequisite: STAT 462 or STAT 501 or STAT 511 - STAT 513 – Theory of Statistics I
Description: Probability models, random variables, expectation, generating functions, distribution theory, limit theorems, parametric families, exponential families, sampling distributions.
Prerequisite: MATH 230 - STAT 514 – Theory of Statistics II
Description: Sufficiency, completeness, likelihood, estimation, testing, decision theory, Bayesian inference, sequential procedures, multivariate distributions and inference, nonparametric inference.
Prerequisite: STAT 513 - STAT 540 – Statistical Computing
Description: Computational foundations of statistics; algorithms for linear and nonlinear models, discrete algorithms in statistics, graphics, missing data, Monte Carlo techniques.
Prerequisite: STAT 501 or STAT 511; STAT 415; matrix algebra - STAT 551 – Linear Models I / Applied Statistics for Engineers and Scientists I
Description: A coordinate-free treatment of the theory of univariate linear models, including multiple regression and analysis of variance models.
Prerequisite: MATH 415 or STAT 415 or STAT 514 ; STAT 512 ; MATH 436 or MATH 441 - STAT 552 – Linear Models II / Applied Statistics for Engineers and Scientists II
Description: Treatment of other normal models, including generalized linear, repeated measures, random effects, mixed, correlation, and some multivariate models.
Prerequisite: STAT 552
- ABE 559 – Biological and Agricultural Systems Simulation
Description: Continuous simulation modeling of biological and physical systems, numerical simulation techniques, validation and verification, difference measures, sensitivity analysis.
Prerequisite: MATH 111 or MATH 141 - BRS 429W – Biorenewable Systems Analysis and Management
Description: Systems analysis and optimization techniques including an introduction to systems theory, qualitative and quantitative analysis, linear programming, waiting line models, PERT/CPM, minimal spanning tree, calculus methods, simulation modeling for decision making, inventory, and energy audits. All topics are presented in the form of case studies that require the students to solve problems in realistic production and processing scenarios. The course also provides a writing-intensive structure.
Prerequisite: BRS 422 - CE 521 – Transportation Networks and System Analysis
Description: Will cover basic concepts of transportation network analysis, as well as explore some applications. Network analysis answers questions such as, “where will people change their routes if I build a new freeway to Pittsburg?”, “where will the congestion hotspots be 30 years from now?”, or “how will traffic patterns change if a bypass toll road is built for I-95 around Philadelphia?” Basically, any problem which requires a “big-picture” view of what routes people will take relies on a network model. The focus is on a large area, such as a city or metropolitan region, rather than on a specific intersection or roadway. By the end of this course, you will have the tools to answer these questions. You will be able to formulate a variety of transportation planning problems as network models, and have the practical knowledge needed to solve them. Furthermore, you will have a conceptual understanding of these models which allows you to understand and critically evaluate model results which others may present to you.
Prerequisite: This course has no formal prerequisites but does require a significant amount of calculus – if your calculus is a bit rusty, it may not be a bad idea to review. This course may require Python for homework assignments, so past programming experience (MATLAB, C, Python, Java, etc.) is helpful. - CE 525 – Transportation Operations
Description: Tools for analyzing transportation operations, including: properties of traffic streams, queuing, traffic dynamics, networks, probability and estimation of traffic properties.
Prerequisite: CE 423 - CE 529 – Infrastructure Systems Analysis and Decision Making
Description: Focuses on thephysical infrustructure systems that provide essential public services, including transportation, energy, water, communication, etc. These complex, large-scale, expensive systems must be planned for and managed. This course emphasizes different tools (including basic economic theory, mathematical modeling, and optimization techniques) that can be used to study these complex theory, drawing examples from transportation. This includes the evaluation of infrastructure investments; better informing the data collection and inspection process; modeling deterioration of infrastructure components such as pavement; and maintenance and repair decision-making at both the single facility and system level.
Prerequisite: none - CE 597 – Computational Analysis of Randomness in Engineering
Description: Probability theory, simulation methods (mote carlo, MCMC), fragility estimation, reliability, simulation of random processes and fields, Bayesian methods and updating. - CHE 512 – Optimization and Biological Networks
Description: Mathematical optimization, formulation and solution techniques for linear, nonlinear, and mixed-integer problems; optimization-based tools for reconstruction, analysis, and redesign of biological networks. - CMPEN 431 – Introduction to Computer Architecture
Description: ntroduction to Computer Architecture (3) This course will introduce students to the architecture-level design issues of a computer system. They will apply their knowledge of digital logic design to explore the high-level interaction of the individual computer system hardware components. Concepts of sequential and parallel architecture including the interaction of different memory components, their layout and placement, communication among multiple processors, effects of pipelining, and performance issues, will be covered.
Prerequisite: CMPEN331 or CMPEN371m - CMPSC 410 – Programming Models for Big Data
Description: This course introduces modern programming models and related software stacks for performing scalable data analytics and discovery tasks over massive and/or high dimensional datasets. The learning objectives of the course are that the students are able to choose appropriate programming models for a big data application, understand the tradeoff of such choice, and be able to leverage state-of-the art cyber infrastructures to develop scalable data analytics or discovery tasks..
Enforced Prerequisite: CMPSC 122 and DS 220. Recommended Preparation: DS 310 or CMPSC 448 - CMPSC 431W – Database Management Systems
Description: Topics include: conceptual data modeling, relational data model, relational query languages, schema normalization, database/Internet applications, and database system issues. - CMPSC 442 – Artificial Intelligence
Description: Introduction to the theory, research paradigms, implementation techniques, and philosophies of artificial intelligence.
Prerequisite: CMPSC122 or equivalent; Concurrent: CMPSC465 - CMPSC 448 – Machine Learning and Algorithmic AI
Description: Evaluation and use of machine learning models; algorithmic elements of artificial intelligence.
Prerequisite: IE 453 - CMPSC 465 – Data Structures and Algorithms
Description: Fundamental concepts of computer science: data structures, analysis of algorithms, recursion, trees, sets, graphs, sorting.
Prerequisite: CMPSC122; CMPSC360 or MATH 311W - CSE/MATH 550 – Numerical Linear Algebra
Description: Solution of linear systems, sparse matrix techniques, linear least squares, singular value decomposition, numerical computation of eigenvalues and eigenvectors.
Prerequisite: MATH 441 or MATH 456 - CSE 556 – Finite Element Methods
Description: Sobolev spaces, variational formulations of boundary value problems; piecewise polynomial approximation theory, convergence and stability, special methods and applications.
Prerequisite: MATH 502 , MATH 552 - CSE 562 – Probabilistic Algorithms
Description: Design and analysis of probabilistic algorithms, reliability problems, probabilistic complexity classes, lower bounds.
Prerequisite: CSE 565 - CSE 564 – Complexity of Combinatorial Problems
Description: NP-completeness theory; approximation and heuristic techniques; discrete scheduling; additional complexity classes.
Prerequisite: CSE 565 - CSE 565 – Algorithm Design and Analysis
Description: An introduction to algorithmic design and analysis.
Prerequisite: CMPSC 465 - CSE/STAT 584 – Machine Learning: Tools and Algorithms
Description: Computational methods for modern machine learning models, including applications to big data and non-differentiable objective functions. - ECON 402 – Decision Making and Strategy in Economics
Description: Development and application of the tools for decision making under uncertainty and for game theoretic analysis of economic problems.
Prerequisite: ECON 302 and (ECON 106 or SCM 200 or STAT 200) - ECON 500 – Introduction to Mathematical Economics
Description: Mathematical Economics: Applications of Mathematical Techniques to Economics. - ECON 501 – Econometrics
Description: Applications of Statistical Techniques to Economics. - ECON 521 – Advanced Microeconomic Theory
Description: Theory of consumer behavior; theory of the firm; price determination in product and factor markets; introduction to welfare economics. - ECON 534 – Game Theory
Description: Foundations of current research in game theory. This is an advanced graduate course in game theory and its applications to economics. The course content is mathematical in nature and emphasizes formal statements of key propositions and their proofs. It begins by presenting two alternative ways in which a game may be represented: the extensive (or tree) form and the strategic (or normal) form. The relationship between these two representations is studied and the key idea of a strategy is introduced. Pre-equilibrium notions of dominance, iterated dominance and rationalizability are studied. Nash’s fundamental theorem on the existence of equilibrium in finite games is proved. Strategic form based refinements of Nash equilibrium, including perfect, proper and stable equilibria are considered. Extensive form based refinements, including subgame perfection and sequential equilibrium are also considered and compared. Harsanyi’s conception of a game of incomplete information is introduced. Other subjects covered include repeated games and the folk theorem, bargaining, common knowledge. Additional topics of current interests may also be covered.
Prerequisite: ECON 521 or permission of program - ECON 589 – Seminar in Econometric Theory
Description: Theories and methods relevant to the application of statistical methods to economics.
Prerequisite: ECON 510 - EE 456 – Introduction to Neural Networks
Description: Artificial Neural Networks as a solving tool for difficult problems for which conventional methods are not applicable.
Prerequisite: CMPSC201 or CMPSC202; MATH 220 - EE 556 – Graphs, Algorithms, and Neural Networks
Description: Examine neural networks by exploiting graph theory for offering alternate solutions to classical problems in signal processing and control. - EE 560 – Probability, Random Variables, and Stochastic Processes
Description: Review of probability theory and random variables; mathematical description of random signals; linear system response; Wiener, Kalman, and other filtering.
Prerequisite: EE 350; STAT 418 - EE 580 – Linear Control Systems
Description: This course provides a mathematical foundation that will enable students to understand and apply linear state space concepts to the analysis and synthesis of control laws.
Prerequisite: EE 380 - EE 581 – Optimal Control
Description: Variational methods in control system design; classical calculus of variations, dynamic programming, maximum principle; optimal digital control systems; state estimation.
Prerequisite: EE 580 - EE 582 – Adaptive and Learning Systems
Description: Adaptive and learning control systems; system identification; performance indices; gradient, stochastic approximation, controlled random search methods; introduction to pattern recognition.
Prerequisite: EE 580 - EE 597 – Reinforcement Learning
Description: [Need to ask Dr. Ventura for the syllabus]
Prerequisite: [Do not know yet] - EEFE/ECON 510 – Econometrics I
Description: General linear model, multicolinearity, specification error, autocorrelation, heteroskedasticity, restricted least squares, functional form, dummy variables, limited dependent variables.
Prerequisite: ECON 490 or STAT 462 or STAT 501 - EEFE 527 – Quantitative Methods I
Description: The first part of the course reviews the foundations of the mathematical analysis with the goal of modeling feasibility; i.e., the set of possible choices. This prepares us to next move to modeling the optimal choice with an extended presentation on optimization theory and application in the static setting. The final part of the course moves on to the methods for engaging in dynamic optimization.
Prerequisite: EEFE 512, ECON 502 - EEFE 530 – Applied Microeconomics II
Description: This course is designed to: (1) expose students to the most common econometric and statistical techniques used in applied microeconomic research and (2) give students an overview of the different types of micro data and the most common methods used to manipulate these data to create additional data sets and variables.
Prerequisite: EEFE 512, EEFE 510 - EEFE 531 – Applied Microeconomics I
Description: In this course, we will study microeconometrics, a subfield that encompasses specification as well as a variety of estimation, computational, and simulation methods that allow us to pursue specification and parameterization of econometric models suitable for analyzing micro-level data. We will see that these methods support an enriched basis for examining the validity of microeconomic theory, and also extend the analytics feasibly tackled by microeconomics. At the micro-level of empirical analysis, we will see our theory predicts high frequencies of corner solutions, abrupt switching, and discontinuities.
Prerequisite: EEFE 512 or ECON 502, EEFE 510 or ECON 510, EEFE 511 or ECON 510 - EEFE 532 – Applied Computational Economics
Description: Economists often find themselves in situations where closed-form solutions do not exist or econometric estimation is inappropriate due to data limitations or the nature of the problem. In these cases, numerical approaches, using computer-based methods, may be an economist’s best option. In this course, we will explore four topics in the field of computational economics: computable general equilibrium modeling, growth modeling, uncertainty and formal monte carlo analysis, and agent-based modeling.
Prerequisite: EEFE 512 - EME 522 – Computational Methods for Electric Power Systems Analysis
Description: This course covers the formulation of and solution methods for a full range of economic-engineering investment and operations problems for electric power systems. Application problems include economic dispatch, unit commitment, optimal power flow, generation capacity expansion, transmission expansion, and modeling of competitive electricity markets. Solution methods include linear programming, mixed integer programming, decomposition methods for stochastic programming (e.g., Lagrangian Relaxation, Benders Decomposition), and mixed complementarity problems, with an emphasis on numerical implementation.
Prerequisite: EME 501 or IE 505 - EME 523 – Stochastic Optimization Methods of Energy and Environmental Systems
Description: This course covers the theory and implementation of computational methods for stochastic simulation and stochastic optimization, with an emphasis on algorithms and implementation. The course emphasizes the quantitative analysis or numerical modeling of complex systems in fields such as civil, environmental, energy, mechanical, and industrial engineering or energy, environmental, and natural resource economics. Topics include Monte Carlo simulation, quasi-random and pseudorandom sampling methods, Markov Chains, Dynamic Programming, Approximate Dynamic Programming, and Stochastic Programming decomposition techniques.
Prerequisite: EME 501 or IE 505 - EME 524 – Machine Learning for Energy and Mineral Engineering Problems
Description: This course provides an overview of the application of machine learning algorithms to problems in energy and mineral engineering. The course addresses the strengths and weaknesses of various machine learning approaches, as well as appropriate testing and validation techniques for these complex models. Topics include machine learning applications in regression, classification, design optimization, and risk analysis. An emphasis of this course is for students to apply these methods to specific research problems of interest. Students with some background in statistics, but no previous formal training in machine learning algorithms will find this course most useful. - EMSC 460 – Environmental Data Analytics
Description: This course introduces various data analystics methods focused on machine learning for Earth and environmental sciences. A range of supervised and unsupervised methods for regression, classification, and clustering problems will be discussed with real-world examples, including but not limited to climatological data, biodiversity data, remote sensing imagery classification, and geomorphological analysis.
Prerequisite: (GEOG 365 or GEOG 485 or GEOG 489 or GEOGSC 210 or GEOSC 44 or METEO 273 or EME 210 or MATSE 219 or CMPSC 101 or CMPSC 200 or CMPSC 201) and (MAT 110 or MATH 140 or MATH 140A or MATH 140B or MATH 140E or MATH 140G or MATH 140H) - EMSC 497 – Environmental Data Analytics (need to update title)
Description: This course will focus on the basic principles and environmental and earth science applications of machine learning algorithms, including regression (e.g., OLS, Ridge, LASSO, PCR), gradient descent optimization, classification (e.g., logistic regression, naïve Bayes, decision trees, random forest, support vector machine), neural networks and deep learning, and clustering (e.g., k-means, hierarchical, self-organizing map) techniques, etc. The class projects will include practical programming exercises in using these techniques for various real-world datasets.
Prerequisite: (GEOG 365 or GEOG 485 or GEOG 489 or GEOSC 210 or GEOSC 444 or METEO 273 or EME 210 or MATSE 219 or CMPSC 101 or CMPSC 200 or CMPSC 201) and (MATH 110 or MATH 140 or MATH 140A or MATH 140B or MATH 140E or MATH 140G or MATH 140H) - ERM 412 – Resource Systems Analysis
Description: The concept of systems; techniques of analysis, including input/output, mathematical programming, and simulation; application to resource systems.
Prerequisite: BIOL 220W, ERM 151, ERM 300, and STAT 240; MATH 111 or MATH 141 - GEOG 463 – Geospatial Information Management
Description: This course examines geospatial data representations and algorithmic techniques that apply to spatially-organized data in digital form.
Prerequisite: GEOG 363 - GEOG 464 – Advanced Spatial Analysis
Description: Skills and knowledge for applying quantitative methods to analyze information with spatial distributions. GEOG 464GEOG 464 Analysis and GIS (3)(BA) This course meets the Bachelor of Arts degree requirements. Geography 464 is a course in methods for analyzing spatial data–methods that can and should be used when the geographic arrangement of a set of measured observations is thought to be of significance in explaining the values of those measurements. The methods of spatial analysis looked at in this course can be distinguished from conventional statistical analysis techniques, and also from many of the analysis functions programmed into many GIS packages. In fact several spatial analysis methods considered in this course the result of attempts to alter and extend conventional statistical techniques to take account of locational similarity and distance measurements (which is why Geography 364 or an equivalent primer in introductory statistical methods is a prerequisite). This means that the techniques that will be introduced in the course are often quite complex mathematically or statistically. Having said this, the overall goal of the course is to provide sufficient conceptual understanding and practical experience so that students become competent in selecting and applying methods appropriate to a variety of frequently-encountered analytical situations.
Prerequisite: GEOG 364 - GEOG 465 – Advanced Geographic Information Systems Modeling
Description: Before taking GEOG 465, students will have learned the fundamentals and principles of GIS. This course extends such knowledge to modeling geospatial scenarios. A GIS model simulates real-world phenomena, including environmental, physical and natural features, as well as social features such as demographic, transportation and origin-destination data. We will model raster and vector data types with an emphasis on multi-criteria GIS operations, using ArcGIS, R and potential other software packages. Upon completion of the course, successful students will have achieved the following objectives and learning outcomes: Students will be able to: a) discuss basic GIS modeling principles; b) find, use, store, retrieve and evaluate GIS datasets; c) describe capabilities and limitations of GIS methods and models; e) implement capabilities, tools and packages in ArcMap GIS and R environments; f) use R for programming tasks such as looping and branching; g) evaluate an external software program and create a model using this software; h) exhibit ability to design and carry out spatial analyses using GIS; i) communicate the results of geographic analyses to others, both in oral and in written form; j) analyze spatial data sets in terms of predictability and uncertainty; and k) calibrate models based on real-world datasets.
Prerequisite: GEOG 363 - GEOG 479 – Spatial Data Science for Cyber and Human Social Networks
Description: This course examines the nexus of geospatial intelligence analysis with cyberspace, the geopolitics of cyber threats, the politics of censorship and hacking, public safety, disaster response, and humanitarian relief. Students will utilize a range of cyber data, systems, and spatial sciences to examine human social networks of the Internet. The course will be centered on geospatial intelligence with emphases on technology, information theory, and cyber and human networks.
Prerequisite: GEOG 160 or GEOG 482 - GEOG 485 – GIS Programming and Software Development
Description: The course focuses on solving geographic problems by modifying and automating generic Geographic Information System (GIS) software through programming. In GEOG 485, students use the Python programming language to write and modify scripts that add functionality to desktop GIS tools and to automate geospatial analysis processes. No previous programming experience is assumed. Core topics covered in this class include object-oriented programming, component object model technologies, object model diagrams, loops, if-then constructs, and modular code design, and situates these topics in the geospatial workflow through their integration with maps, layers, spatial data tables, and spatial analysis methods. Students who successfully complete the course can automate repetitive GIS tasks, customize GIS interfaces, and share their geospatial software development work with others. - GEOG 560 – Seminar in Geographic Information Science
Description: Geographic information science problems/theory, e.g. GIS, cartography, remote sensing, spatial analysis, modeling. - GEOG 580 – Geovisual Analytics
Description: Traces of geographic information reflected in digital data continuously increase in volume, variety, velocity, and variability. New geographic data sources, together with advancements in high-performance computing, present opportunities to dissect complex real-world problems in unprecedented ways. Human cognitive capability, however, remains constant, thus limiting our ability to extract value and insight from this data deluge. Geovisual analytics is the emerging science of analytical reasoning supported by interactive geovisualization, computational methods, and user-centered design. Geovisual analytics constitutes an essential subdomain in the broader field of Spatial Data Science. This seminar investigates the role of geovisual analytics in facilitating the human sensemaking process through (a) a survey and synthesis of research challenges surrounding the design, use, and evaluation of geovisual analytics systems and (b) exercises to design geovisual analytics approaches to tackle complex problem contexts such as those found in crisis management, epidemiology, and transportation domains.
Prerequisite: GEOG 486 - GEOG 586 – Geographical Information Analysis
Description: Choosing and applying analytical methods for geospatial data, including point pattern analysis, interpolation, surface analysis, overlay analysis, and spatial autocorrelation
Prerequisite: GEOG 485 or GEOG 486 or GEOG 487 - GEOG 850 – Location Intelligence for Business
Description: In business, the application of maps and mapping technology ranges from a long-standing presence (commercial real estate, retail, and logistics) to nascent analytical applications across different industries. The momentum for commercial applications that encompass GIS, geospatial intelligence (GEOINT) technologies, and geospatial intelligence analysis is growing. In businesses, geospatial attributes are being combined with enterprise-wide databases. GIS and GEOINT tools and methodologies can now be folded into the more mainstream information technology (IT) applications of business intelligence (BI) to formulate location intelligence (LI) applications, products, and services. This course explores and applies the key geospatial intelligence principles involved in site selection, market analysis, risk and crisis management, and logistics, providing opportunities for students to solve those problems with contemporary geospatial tools and datasets. This course provides a foundation for spatial thinking and analysis in commercial settings, and experience with contemporary mapping and analysis tools for professional applications of location intelligence.
Prerequisite: GEOG 482 - GEOG 855 – Spatial Data Analytics for Transportation
Description: This course explores the spatial data science and technology associated with the transportation industry. This interdisciplinary field is often referred to as GIS-T. There is a natural synergy between GIS and transportation, which has resulted in a number of specialized techniques and a wide variety of GIS-T applications. To appreciate the value GIS brings to the transportation industry, students need to have some understanding of the business of transportation and the challenges and problems those in the industry face. Consequently, they will learn about a number of subdisciplines within transportation and examine how GIS has been applied to each. Students will also explore some of the key organizations in the transportation industry who use GIS and learn firsthand from transportation professionals, representing a variety of specialized fields, about the role GIS plays for them. Throughout the course, students will study GIS concepts and techniques which are fundamental to transportation, such as transportation networks and linear referencing systems. In addition, they will have the opportunity to explore a number of GIS applications and tools related to transportation. Due to the overall breadth of the transportation industry, the course will focus primarily on the largest application areas: highway and mass transit. We will, however, examine other significant modes, including aviation, maritime, pedestrian, and bike transit. Furthermore, while much of the course content is oriented around the U.S. transportation industry, students will also look at GIS-T applications and trends in other parts of the world.
Prerequisite: GEOG 482 - GEOG 858 – Spatial Data Science for Emergency Management
Description: Geospatial perspectives and technologies have a major role to play in planning for and responding to emergencies. As is true with other analytical paradigms, geospatial systems and technologies – from aerial mapping techniques to data acquisition – are changing rapidly. Emergency management is also changing quickly as the frequency and magnitude of crises and disasters are increasing, and more and more people and places are being impacted. GEOG 858 helps students develop proficiency in the theoretical, analytical, and technical perspectives required to support all stages of emergency (crisis or disaster) management activities with geospatial solutions, ranging from small-scale emergency management efforts to large-scale disasters and humanitarian crises. Topics covered in GEOG 858 will include advancements in geospatial data collection, geospatial data processing and analysis capabilities, unmanned aerial systems (UAS), geospatial artificial intelligence (geoAI), volunteered geographic information (VGI), geospatially-oriented social media, and others.
Prerequisite: GEOG 483 - GEOSC 450 – Risk Analysis
Description: An introduction to concepts and methods of quantitative risk analysis with focus on water, climate, and energy related risks. Key concepts include probability, impacts, risk, uncertainty, statistical estimation, and decision-making under uncertainty,
Prerequisite: MATH 140 or MATH 110, Introductory Earth Science or Geoscience class, Introductory Statistics class (e.g. STAT 200, or STAT 301, or ENNEC 473), or permission of program. - IE 402 – Advanced Engineering Economy
Description: Concepts and techniques of analyses useful in evaluating engineering projects under deterministic and uncertain conditions.
Prerequisite: IE 302, IE 322, IE 405 - IE 405 – Deterministic Models in Operations Research
Description: Deterministic models in operation research including linear programming, flows in networks, project management, transportation and assignment models and integer programming.
Prerequisite: MATH 220 - IE 425 – Stochastic Models in Operations Research
Description: An introduction to the method and techniques of mathematical decision making, including inventory, replacement, allocation, and waiting line problems.
Prerequisite: IE 405 - IE 453 – Simulation Modeling for Decision Support
Description: Introduction of concepts of simulation modeling and analysis, with application to manufacturing and production systems.
Prerequisite: CMPSC 201C or CMPSC 201F ;IE 323, IE 425 - IE 454 – Applied Decision Analysis
Description: Theory and practice of decision analysis applied to engineering problems.
Prerequisite: IE 322 - IE 468 – Optimization Modeling and Methods
Description: Mathematical modeling of linear, integer, and nonlinear programming problems and computational methods for solving these classes of problems.
Prerequisite: IE 405, MATH 231 - IE 478 – Retail Services Engineering
Description: To understand modern retail industry with focus on their operations and information technologies.
Prerequisite: IE 322 - IE 505 – Linear Programming
Description: An accelerated treatment of the main theorems of linear programming and duality structures plus introduction to numerical and computational aspects of solving large-scale problems.
Prerequisite: IE 405 - IE 507 – Operations Research: Scheduling Models
Description: Scheduling models with simultaneous job arrival and probabilistic job arrival, network scheduling, and scheduling simulation techniques.
Prerequisite: IE 425 - IE 509 – Operations Research: Waiting Line Models
Description: Waiting line models including models with infinite queues, finite queues, single and multiple servers under various priorities and disciplines.
Prerequisite: IE 516 - IE 510 – Integer Programming
Description: Study of advanced topics in mathematical programming; emphasis on large-scale systems involving integer variables.
Prerequisite: IE 512 - IE 511 – Experimental Design in Engineering
Description: Statistical design and analysis of experiments in engineering; experimental models and experimental designs using the analysis of variance.
Prerequisite: IE 323 - IE 512 – Graph Theory and Networks in Management
Description: Graph and network theory; application to problems of flows in networks, transportation and assignment problems, pert/CPM, facilities planning.
Prerequisite: IE 425 - IE/SC&IS 516 – Applied Stochastic Processes
Description: Study of stochastic processes and their applications to engineering and supply chain and information systems.
Prerequisite: IE 322 or STAT 318 - IE 517 – Models and Technologies for Financial Services
Description: The objective of this course is to study current and emerging electronic financial services used in enterprise and global supply chain operations. The emphasis will be on technologies used in these services and how they can be used for improving operations.
Prerequisite: IE 505 or IE 516 - IE/SC&IS 519 – Dynamic Programming
Description: This course presents the basic theory and applications of dynamic programming. The focus of the course will be on the theory of Markov decision processes (MDP), which provides an analytical tool to optimally control the behavior of a Markov Chain. The students will learn fundamental MDP models, computational methods and applications in supply chain and information systems, including production and inventory control, quality control, logistics, scheduling, queueing network, and economic problem.
Prerequisite: IE 516 or SC&IS 516 or equivalent - IE 520 – Multiple Criteria Optimization
Description: Study of concepts and methods in analysis of systems involving multiple objectives with applications to engineering, economic, and environmental systems.
Prerequisite: IE 405 or INS 427 - IE 521 – Nonlinear Programming
Description: Fundamental theory of optimization including classical optimization, convex analysis, optimality conditions and duality, algorithmic solution strategies, variational methods in optimization.
Prerequisite: IE 505 - IE 522 – Discrete Event Systems Simulation
Description: Fundamentals of discrete event simulation, including event scheduling, time advance mechanisms, random variate generation, and output analysis.
Prerequisite: IE 425 - IE 530 – Financial Engineering
Description: Financial option pricing and portfolio design relevant to investment decision making.
Prerequisite: IE 516 - IE 532 – Reliability Engineering
Description: Mathematical definition of concepts in reliability engineering; methods of system reliability calculation; reliability modeling, estimation, and acceptance testing procedures.
Prerequisite: IE 323 or 3 credits in probability and statistics with a prerequisite of calculus - IE/CSE/EDSGN/IST 561 – Data Mining Driven Design
Description: The study and application of data mining/machine learning (DM/ML) techniques in multidisciplinary design. - IE 562 – Computational Foundations of Smart Systems
Description: Intelligent computational techniques for the design and implementation of smart systems. - IE 566 – Quality Control
Description: Advanced quality assurance and control topics, including multivariate methods, economic design for control and acceptance, dimensioning, tolerancing, and error analysis.
Prerequisite: IE 423 - IE 567 – Distributed Systems and Control
Description: The objective of this course is to study current research and engineering challenges in distributed systems and control in the context of manufacturing and service enterprises, and supply chains. Emphasis will be placed on understanding the dynamics and computational aspects of decision making and control algorithms in integrated enterprises. This course deals with the multidisciplinary aspects of controls, computing, and communication in this rapidly evolving area.
Prerequisite: Familiarity with high-level programming. Students are expected to be proficient in undergraduate level analysis pertaining to differential equations. - IE 568 – Healthcare Systems Engineering
Description: Quantitative methods to analyze and improve healthcare systems.
Prerequisite: IE 405, IE 425 and IE 433 - IE/SC&IS 570 – Supply Chain Engineering
Description: Use of operations research models and methods for solving problems in supply chain systems.
Prerequisite: IE 405, IE 425 or SC&IS 510 - IE 575 – Foundations of Predictive Analytics
Description: Survey course on the key topics in predictive analytics.
Prerequisite: IE 323, STAT 500 or equivalent - IE 582 – Engineering Analytics
Description: Students will learn advanced information technology, network science, big data, descriptive and predictive analytics, for manufacturing and service systems. - IE 583 – Response Surface Methodology and Process Optimization
Description: Surface Methodologies used for sequential experimentation and optimization of production processes. Statistical design and analysis of such experiments.
Prerequisite: IE 511 or STAT 501 - IE 584 – Time Series Control and Process Adjustment
Description: Design of Time Series-based process controllers for Quality Engineering. Study of the effect of autocorrelation on control chart performance.
Prerequisite: IE 423 - IE/EE 585 – Convex Optimization
Description: This course is designed to provide students with necessary skills to recognize or build convex optimization problems coming from diverse application areas and to solve them efficiently. It consists of five parts: 1) convex sets, 2) convex functions, 3) convex optimization, 4) algorithms and 5) real life applications.
Prerequisite: IE 505 - IE 588 – Nonlinear Networks
Description: Foundation in congestion games, including elements of non-cooperative game theory, equilibrium network flows, Braess paradox, and the price of anarchy. This course examines the theory of congestion games, developed originally to describe flows on congested transport networks but recently embraced to model data networks.
Prerequisite: IE 505 - IE 589 – Dynamic Optimization and Differential Games
Description: Dynamic optimization and dynamic non-cooperative games emphasizing industrial applications. This course provides an introduction to dynamic optimization and dynamic noncooperative games from the perspective of infinite dimensional mathematical programming and differential variational inequalities in topological vector spaces.
Prerequisite: IE 425; IE 505; IE 521 (can be taken concurrently) - MATH/STAT 414 – Introduction to Probability Theory
Description: probability spaces, discrete and continuous random variables, transformations, expectations, generating functions, conditional distributions, law of large numbers, central limit theorems. Students may take only one course from MATH(STAT) 414 and 418 for credit.
Prerequisite: MATH 230 or MATH 231 - MATH/STAT 415 – Introduction to Mathematical Statistics
Description: A theoretical treatment of statistical inference, including sufficiency, estimation, testing, regression, analysis of variance, and chi-square tests.
Prerequisite: MATH 414 - MATH/STAT 416 – Stochastic Modeling
Description: Review of distribution models, probability generating functions, transforms, convolutions, Markov chains, equilibrium distributions, Poisson process, birth and death processes, estimation.
Prerequisite: MATH 318 OR MATH 414; MATH 230 - MATH/STAT 418 – Introduction to Probability and Stochastic Processing for Engineering
Description: Fundamentals and axioms, combinatorial probability, conditional probability and independence, probability laws, random variables, expectation; Chebyshev’s inequality. Students may take only one course from MATH(STAT) 414 and 418 for credit.
Prerequisite: MATH 230 or MATH 231 - MATH/CMPSC 451 – Numerical Computations
Description: Algorithms for interpolation, approximation, integration, nonlinear equations, linear systems, fast FOURIER transform, and differential equations emphasizing computational properties and implementation. Students may take only one course for credit from MATH 451 and 455.
Prerequisite: CMPSC 201C, CMPSC 201, or CSE 103; MATH 230 or MATH 231 - MATH 455/CMPSC – Introduction to Numerical Analysis I
Description: Floating point computation, numerical rootfinding, interpolation, numerical quadrature, direct methods for linear systems. Students may take only one course for credit from MATH 451 and MATH 455.
Prerequisite: CMPSC 201C, CMPSC 201F, or CSE 103; MATH 220; MATH 230 or MATH 231 - MATH/CMPSC 456 – Introduction to Numerical Analysis II
Description: Polynomial and piecewise polynomial approximation, matrix least squares problems, numerical solution of eigenvalue problems, numerical solution of ordinary differential equations.
Prerequisite: MATH 455 - MATH 484 – Linear Programs and Related Problems
Description: Introduction to theory and applications of linear programming; the simplex algorithm and newer methods of solution; duality theory.
Prerequisite: MATH 220; MATH 230 or MATH 231 - MATH 485 – Graph Theory
Description: Introduction to the theory and applications of graphs and directed graphs. Emphasis on the fundamental theorems and their proofs.
Prerequisite: MATH 311W - MATH 486 – Mathematical Theory of Games
Description: Basic theorems, concepts, and methods in the mathematical study of games of strategy; determination of optimal play when possible.
Prerequisite: MATH 220 - MATH/CSE 555 – Numerical Optimization Techniques
Description: Unconstrained and constrained optimization methods, linear and quadratic programming, software issues, ellipsoid and Karmarkar’s algorithm, global optimization, parallelism in optimization.
Prerequisite: CMPSC 456 - MATH/STAT 516 – Stochastic Processes
Description: Markov chains; generating functions; limit theorems; continuous time and renewal processes; martingales, submartingales, and supermartingales; diffusion processes; applications.
Prerequisite: MATH 416 - MATH/STAT 519 – Topics in Stochastic Processes
Description: Selected topics in stochastic processes, including Markov and Wiener processes; stochastic integrals, optimization, and control; optimal filtering.
Prerequisite: STAT 516, STAT 517 - MATH/CSE 550 – Numerical Linear Algebra
Description: Solution of linear systems, sparse matrix techniques, linear least squares, singular value decomposition, numerical computation of eigenvalues and eigenvectors.
Prerequisite: MATH 441 or MATH 456 - MATH 553 – Introduction to Approximation Theory
Description: Interpolation; remainder theory; approximation of functions; error analysis; orthogonal polynomials; approximation of linear functionals; functional analysis applied to numerical analysis.
Prerequisite: MATH 401, 3 credits in Computer Science and Engineering - METEO 527 – Data Assimilation
Description: Data assimilation is the process of finding the best estimate of the state by statistically combining model forecasts, observations, and their respective uncertainties.
Prerequisite: Basic knowledge of probability theory, calculus, linear algebra/matrices, and computer programming is expected. - ME 444 – Engineering Optimization
Description: Problem formulation, algorithms and computer solution of various engineering optimization problems.
Prerequisite: MATH 220; MATH 230 or MATH 231; CMPSC 201 or CMPSC 202 or CMPSC 200 - ME/EE 550 – Foundations of Engineering Systems Analysis
Description: Analytical methods are developed using the vector space approach for solving control and estimation problems; examples from different engineering applications.
Prerequisite: MATH 436 - ME 565 – Optimal Design of Mechanical and Structural Systems
Description: Application of numerical optimization techniques to design mechanical and structural systems; design sensitivity analysis. - ME 577 – Stochastic Systems for Science and Engineering
Description: The course develops the theory of stochastic processes and linear and nonlinear stochastic differential equations for applications to science and engineering.
Prerequisite: MATH 414 or MATH 418; ME 550 or MATH 501 - MKTG 555 – Marketing Models
Description: Topics in the model building approach to marketing decision making, focusing on current research issues. - PHYS 580 – Elements of Network Science and Its Applications
Description: Introduction to elements of network theory used to describe and model complex networks; applictions in social, biological, and technological networks. PHYS 580 Elements of Network Science and Its Applications (3) Network Science is the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena. This class will focus on four main questions asked by network science: (i) How do we use data analysis methods to determine or infer the interaction graphs underlying complex systems? (ii) How can we characterize the organizational features of large-scale networks? (iii) What are the mechanisms that determine the common topological features of a wide variety of networks? (iv) To what extent does the organization of the interaction network underlying a complex system determine the dynamical behavior (e.g. steady state or oscillations) of the system? Applications in social, biological and technological networks will be examined. As Network Science is an interdisciplinary field of research, the course is open and should be of interest to a wide range of graduate students in degree programs in physics, social sciences, life sciences, mathematics, engineering, and computer science. - PNG 430 – Reservoir Modeling
Description: The numerical simulation of petroleum reservoir processes by the use of models; scaling criteria and network flow.
Prerequisite: MATH 251, PNG 410; CMPSC 201C or CMPSC 201F - PNG 511 – Numerical Solution of the Partial Differential Equations of Flow in Porous Media
Description: Differencing schemes for the partial differential equations of single-phase flow; application to flow of gas and mixing in porous media - PNG 512 – Numerical Reservoir Simulation
Description: Mathematical analysis of complex reservoir behavior and combination drives; numerical methods for the solution of behavior equations; recent developments. - SC&IS 505 – Management Information Systems Research
Description: Research problems and issues in supply chain and information systems. - SC&IS 510 – Introduction to Supply Chain and Information Systems
Description: Introduction to the strategic framework, issues, and methods for integrating supply and demand management within and across companies. - SC&IS 520 – Principles of SC&IS I
Description: Initial course on principles of supply chain and information systems with special emphasis on potential research topics.
Prerequisite: SC&IS 510 - SC&IS 525 – Supply Chain Optimization
Description: Introduction to theory and practice of optimization methods and models for analyzing and improving the performance of supply chain environments.
Prerequisite: prior coursework in linear algebra and calculus - SC&IS 530 – Principles of SC&IS II
Description: Sequel on principles of supply chain and information systems with special emphasis on potential research topics.
Prerequisite: SC&IS 510 - SC&IS 535 – Statistical Research Methods for Supply Chain and Information Systems
Description: Current statistical research methods for modeling and analysis of supply chain and information systems.
Prerequisite: 3 credits each in undergraduate accounting, economics, and statistics - SC&IS 545 – Supply Chain Systems Simulation
Description: Application of computer simulation to analysis and design of supply chain and information systems design; simulation experiments in SC&IS research.
Prerequisite: 3 credits of computer programming - SC&IS 546 – Procurement and Supply Management
Description: Analysis, planning, and management of domestic and international procurement and supply activities. SCIS 546 Procurement and Supply Management (3) SCIS 546 provides an overview of procurement and supply management in the context of domestic and global supply-chain networks. Special emphasis is given to strategic sourcing relationships, supply management “best practices,” and E-perspectives on supply management. The course uses problem-based learning and emphasizes the case method. The goal is to learn through the application of course materials to relevant supply management case problems and scenarios. Collaboration in case preparation is required. Student evaluations are based on class participation, individual and group assignments, and exams. This course will be offered during the Fall semester with resident enrollment limits set at 20 students. - SC&IS 565 – Supply Chain Strategy
Description: Strategies, issues and best practices in technology adoption, change management, financial/capability assessments, critical aspects of relationship management in supply-chain networks. SC&IS 565 Supply Chain Strategy (3) The course focuses on the strategic design and the effective operation of supply chains. It specifically seeks to integrate topics foundation course and to engage students in the critical analysis and in probing discussions of specific supply chain leadership issues. Special emphasis is given to supply chain technology adoption, change management, shareholder value assessment, capability assessment, relationship management, and performance metrics.
Prerequisite: SC&IS 510 - STAT 460 – Intermediate Applied Statistics
Description: Review of hypothesis testing, goodness-of-fit tests, regression, correlation analysis, completely randomized designs, randomized complete block designs, latin squares.
Prerequisite: STAT 200, STAT 240, STAT 250, STAT 301, or STAT 401 - STAT 501 – Regression Methods
Description: Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.
Prerequisite: 6 credits in statistics or STAT 451; matrix algebra - STAT 502 – Analysis of Variance and Design of Experiments
Description: Analysis of variance and design concepts; factorial, nested, and unbalanced data; ANCOVA; blocked, Latin square, split-plot, repeated measures designs.
Prerequisite: STAT 462 or STAT 501 - STAT 503 – Design of Experiments
Description: Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs.
Prerequisite: STAT 462 or STAT 501; STAT 502 - STAT 508 – Applied Data Mining and Statistical Learning
Description: With rapid advances in information technology, the field of Applied Statistics and Data Science has witnessed an explosive growth in the capabilities to generate and collect data. In the business world, very large databases on commercial transactions are generated by retailers. Huge amounts of scientific data are generated in various fields as well using a wide assortment of high throughput technologies. The internet provides another example of billions of web pages consisting of textual and multimedia information that is used by millions of people. Analyzing large complex bodies of data systematically and efficiently remains a challenging problem. This course addresses this problem by covering techniques and new software that automate the analysis and exploration of large complex data sets. Data Mining methods are introduced by using examples to demonstrate the power of the statistical methods for exploring structure in data sets, discovering patterns in data, making predictions, and reducing the dimensionality by Principal Component Analysis (PCA) and other tools for visualization of high dimensional data. Exploratory data analysis, classification methods, clustering methods, and other statistical and algorithmic tools are presented and applied to actual data. In particular, the course investigates classification methods (supervised learning), and clustering methods (unsupervised learning), and other statistical and algorithmic tools as they are applied to actual data. In addition, data mining and learning techniques developed in fields other than statistics, e.g., machine learning and signal processing, will also be reviewed. The Statistics graduate program also offers more in-depth courses on data mining, STAT 557 and STAT 558. This course focuses on how to use software to investigate and analyze large data sets, whereas STAT 557 and STAT 558 focus more on writing data mining algorithms and the computational aspects of algorithm implementation.
Prerequisite: STAT 501 ; STAT 462 - STAT 510 – Applied Time Series Analysis
Description: Identification of models for empirical data collected over time. Use of models in forecasting.
Prerequisite: STAT 462 or STAT 501 or STAT 511 - STAT 513 – Theory of Statistics I
Description: Probability models, random variables, expectation, generating functions, distribution theory, limit theorems, parametric families, exponential families, sampling distributions.
Prerequisite: MATH 230 - STAT 514 – Theory of Statistics II
Description: Sufficiency, completeness, likelihood, estimation, testing, decision theory, Bayesian inference, sequential procedures, multivariate distributions and inference, nonparametric inference.
Prerequisite: STAT 513 - STAT 515 – Stochastic Processes and Monte Carlo Methods
Description: Conditional probability and expectation, Markov chains, the exponential distribution and Poisson processes.
Prerequisite: MATH 414, STAT 414, or STAT 513 - STAT 540 – Statistical Computing
Description: Computational foundations of statistics; algorithms for linear and nonlinear models, discrete algorithms in statistics, graphics, missing data, Monte Carlo techniques.
Prerequisite: STAT 501 or STAT 511; STAT 415; matrix algebra - STAT 551 – Linear Models I / Applied Statistics for Engineers and Scientists I
Description: A coordinate-free treatment of the theory of univariate linear models, including multiple regression and analysis of variance models.
Prerequisite: MATH 415 or STAT 415 or STAT 514 ; STAT 512 ; MATH 436 or MATH 441 - STAT 552 – Linear Models II / Applied Statistics for Engineers and Scientists II
Description: Treatment of other normal models, including generalized linear, repeated measures, random effects, mixed, correlation, and some multivariate models.
Prerequisite: STAT 552 - STAT 553 – Asymptotic tools
Description: A rigorous but non-measure-theoretic introduction to statistical large-sample theory for Ph.D. students. STAT 553 Asymptotic Tools (3) STAT 553 covers most standard statistical asymptotics theory but does not require any knowledge of measure theory (it does not define convergence with probability one, for example). It covers convergence of random variables in both the univariate and multivariate settings, Slutsky’s theorem(s) and the delta method, the Lindeberg-Feller central limit theorem, power and sample size, likelihood-based estimation and testing, and U-statistics. Although there is no measure theory in the course, it is a mathematically rigorous course and major results are proved. Many common applications of the theory in mathematical statistics are discussed, and most assignments require the use of a computer.
Prerequisite: STAT 513 and STAT 514 - IST/STAT 557 – Data mining I
Description: This course on data mining will cover methodology, major software tools and applications in this field. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining.
Prerequisite: STAT 318 or STAT 416 and basic programming skills - IST/STAT 558 – Data mining II
Description: Advanced data mining techniques: temporal pattern mining, network mining, boosting, discriminative models, generative models, data warehouse, and choosing mining algorithms.
Prerequisite: STAT 557 or IST 557 - STAT 561 – Statistical Inference I
Description: Classical optimal hypothesis test and confidence regions, Bayesian inference, Bayesian computation, large sample relationship between Bayesian and classical procedures.
Prerequisite: STAT 514; Concurrent: STAT 517 - STAT 562 – Statistical Inference II
Description: Basic limit theorems; asymptotically efficient estimators and tests; local asymptotic analysis; estimating equations and generalized linear models.
Prerequisite: STAT 561