University Catalog > Courses > Computational Sciences and Informatics (CSI)

Computational Sciences and Informatics (CSI)

School of Computational Sciences

600/SYST 500 Quantitative Foundations for Computational Sciences (3:3:0). Not applicable to the 48-hour course total for the CSI PhD. Prerequisites: MATH 213 and 214. Accelerated review of mathematical tools for scientific applications and analysis. Topics include vectors and matrices; differential and difference equations; linear systems; Fourier, Laplace, and Z-transforms and probability theory.

601 Computational Science Tools I (1:1:0). Not applicable to the 48-hour course total for the CSI PhD. Prerequisites: A year of college calculus and a course in computer programming. Introduction to basic tools in computational science. Covers UNIX, editors, LaTeX, HTML, and graphics. Emphasizes application and use rather than theory. Substantial portion of instruction is delivered via a distance-learning web interface.

602 Computational Science Tools II (1:1:0). Not applicable to the 48-hour course total for the CSI PhD. Prerequisites: CSI 601 and knowledge of matrix algebra. Introduction to basic tools in computational science. Covers MATLAB, MAPLE, and GNUPlot. Emphasizes application and use rather than theory. Substantial portion of instruction is delivered via a distance-learning web interface.

603 Introduction to Scientific Programming I (1:1:0). Not applicable to the 48-hour course total for the CSI PhD. Prerequisite: CSI 601 or permission of instructor. Introduction to programming in C or Fortran. Emphasizes application and languages rather than theory. Features a combination of lecture and lab. Assignments are completed via a distance-learning web interface.

604 Introduction to Scientific Programming II (1:1:0). Not applicable to the 48-hour course total for the CSI PhD. Prerequisites: CSI 601 and 603 or permission of instructor. Introduction to programming in an object-oriented language such as C++. Features a combination of lecture and lab.

605 Software Construction Tools for Scientists (1:1:0). Not applicable to the 48-hour course total for the CSI PhD. Prerequisites: CSI 601, 603, 604 or programming experience with C, C++, or Fortran and familiarity with the UNIX operating system; or permission of instructor. Introduction to the tools commonly used for software construction and development. Covers revision control, debuggers, profilers, Makefiles, and regular expressions. Designed for students who wish to develop moderate to large software systems and need an introduction to the basic tools used in construction.

606 Scientific Graphics and Visualization Tools (1:1:0). Not applicable to the 48-hour course total for the CSI PhD. Prerequisite: CSI 601 or permission of instructor. Introduction to the use of scientific visualization tools for data analysis. Use of specific packages will be taught on a rotating basis. Packages include PV-WAVE, S-Plus, SV, XMGR, and the pnm tools.

607 Database Tools for Scientists (1:1:0). Not applicable to the 48-hour course total for the CSI PhD. Prerequisites: CSI 601 and 602 or permission of instructor. Introduction to database tools. Teaches the student how to deal with the relation model, on which database packages like Oracle are based. Under this language, database design concepts, table operations, triggers, sequences, and introduction to simple query language (SQL) will be covered.

610 Introduction to Computational Sciences (3:3:0). Not applicable to the 48-hour course total for the CSI PhD. Prerequisites: CSI 601, 602, 603, 604, 605, and 700 or permission of instructor. Covers advanced numerical methods, computer architecture, and scientific software development. Includes software design, construction, and validation techniques commonly used in industry. Also serves as an introduction to high-performance computing.

612 Physical Chemistry of Solids (3:3:0). Prerequisites: MATH 113, 114, 213, PHYS 260 or 266, CHEM 331 and 332. An advanced course of physical chemistry for first-year graduate students with emphasis on solid-state materials. It covers advanced chemical thermodynamics, kinetics, diffusion, and solid-state reactions in different classes of materials, including metals, ionic crystals, and semiconductors. Computer applications to modeling solid-state reactions are also included.

632 Global Ecology (3:3:0). Prerequisites: General chemistry, general physics, introductory statistics, and calculus. Intensive review of ecology necessary to begin research in global change. Covers basic principles of physiological ecology, population dynamics, dynamics of ecological communities and ecosystems, biogeography, biological diversity, and the dynamics of the biosphere, including the effects of life on the atmosphere, oceans, and solid surfaces.

639 Ethics in Scientific Research (3:3:0). An examination of ethical issues in scientific research. Begins with a reflection on the purpose of scientific research and review of the foundational principles used for evaluating ethical issues. The course will equip students with skills for survival in scientific research through training in moral reasoning and teaching of responsible conduct. Students will discuss current ethical issues in research and will learn to apply critical thinking skills to the design, execution, and analysis of experiments. Important issues include, for example, the use of animals and humans in research, ethical standards in the computer community, and research fraud. In addition, currently accepted guidelines for behavior in areas such as data ownership, manuscript preparation, and conduct of persons in authority may be presented and discussed in terms of relevant ethical issues.

654 Data and Data Systems in the Physical Sciences (3:3:0). Prerequisite: Competency in programming at the level of CSI 601-607 or permission of instructor. This course introduces the student to data issues associated with modern physical sciences. Specifically, it examines data access, formats, browsing, analysis, visualization, and data information systems in federated environments. Illustrative examples are used from the physical sciences, including astronomy and space sciences; Earth sciences; Earth observing and other fields of physics; as well as model output data and associated special issues. The student is introduced to some mathematical techniques that are particularly important for large databases.

655/PHYS 575 Introduction to Physics and Chemistry of the Atmosphere (3:3:0). Prerequisites: PHYS 305 and 262. Introduction to basic physical and chemical processes that operate in Earth's atmosphere. Emphasizes those concepts that provide a global description of the current atmospheric state and those processes that relate to global change and atmospheric evolution. Covers equilibrium structure, radiative transfer models, thermodynamics of various atmospheric layers, and the various processes defining these layers.

659 Dispersal Methods of Hazardous Releases (3:3:0). Prerequisites: CSI 655 or permission of instructor. Covers topics including the physics of aerosols; engineering and mechanics of building ventilation systems; and mechanical dissemination utilizing hand-held, automatic, vehicle, and truck mounted systems. Course also covers the basic concepts, theories, and models of pollutant dispersal in the atmosphere and the related atmospheric systems affecting dispersal of biological agents.

660/ASTR 535 Space Instrumentation and Exploration (3:3:0). Prerequisites: PHYS 262, MATH 213 or equivalent, or permission of instructor. Survey of the instruments, devices, and methods used for space and planetary exploration. Covers remote sensing of Earth and other solar system bodies. Planned manned and unmanned missions by the United States and other countries.

661/ASTR 530 Astrophysics (3:3:0). Prerequisites: PHYS 303, 305, 308; MATH 214. Survey of contemporary astrophysics. Topics include physical concepts, stellar spectra, Hertzsprung-Russell diagram, stellar atmospheres, stellar structure, interstellar matter, stellar evolution, high-energy phenomena, hydrodynamical processes in astrophysics, accretion disk formation, and shock formation.

672/STAT 652 Statistical Inference (3:3:0). Prerequisites: STAT 544 or permission of instructor. Critical aspects of probability, random variables and distributions, characteristic functions, and stochastic convergence. Optimal estimation, maximum-likelihood estimation, asymptotic theory, Bayesian methods, likelihood-ratio tests, statistical decision theory, sequential methods.

678/STAT 658 Times Series Analysis and Forecasting (3:3:0). Prerequisites: STAT 544 or CSI 672, or permission of instructor. Modeling stationary and nonstationary processes, autoregressive, moving average and mixed model processes, hidden periodicity models, properties of models, autocovariance functions, autocorrelation functions, partial autocorrelation function, spectral density functions, identification of models, estimation of model parameters, and forecasting techniques.

685 Fundamentals of Materials Science (3:3:0). Prerequisite: Undergraduate degree in physics, chemistry, materials, electrical or mechanical engineering, or related sciences, or permission of instructor. Covers fundamental concepts, methods, and applications of materials science. Also covers structure of modern materials (metallic alloys and compounds, ceramic materials, semiconductors, polymers, and nanostructured materials), materials properties (mechanical, thermal, and electric), experimental methods of materials characterization, application of computers in materials science, and elements of materials design.

687/PHYS 512 Solid State Physics and Applications (3:3:0). Prerequisite: PHYS 502 or equivalent. Covers crystal structures, binding, lattice vibrations, the free electron model, metals, semiconductors and semiconductor devices, superconductivity, and magnetism.

700/MATH 685 Numerical Methods (3:3:0). Prerequisites: MATH 214, 203, and some programming experience. Covers computational techniques for the solution of problems arising in science and engineering. Algorithms are developed for the treatment of typical problems in applications, with special emphasis on the types of data encountered in practice. The course covers theoretical development, as well as implementation, efficiency, and accuracy issues in using algorithms and interpreting the results. When applicable, computer graphical techniques are used to enhance interpretation of results.

701 Foundations of Computational Science (3:3:0). Prerequisites: Competency in UNIX and programming at the level of CSI 601-604, CSI 700, or permission of instructor. Covers the mapping of mathematical models to computer software, including all aspects of the development of scientific software, such as architecture, data structures, advanced numerical algorithms, languages, documentation, optimization, validation, verification, and software reuse. Examples in bioinformatics, computational biology, computational physics, and global change demonstrate scientific advances enabled by computation. Class projects involve working in teams to develop software that implements mathematical models, using the software to address important scientific questions, and conducting computational experiments with it.

702 High-Performance Computing (3:3:0). Prerequisites: CSI 700 and CSI 701, or permission of instructor. Hardware and software associated with high-performance scientific computing. Computer architectures, processor design, programming paradigms, parallel and vector algorithms. Emphasis on the importance of software scalability in science problems.

703 Scientific and Statistical Visualization (3:3:0). Prerequisite: STAT 554 or CS 652, or permission of instructor. Covers visualization methods used to provide new insights and intuition concerning measurements of natural phenomena and scientific and mathematical models. Presents case study examples from a variety of disciplines to illustrate what can be done. Topics include human perception and cognition, an introduction to the graphics laboratory, elements of graphing data, representation of space-time and vector variables, representation of 3-D and higher dimensional data, dynamic graphical methods, and virtual reality. Students are required to work on a visualization project. Software tools on the Silicon Graphics workstation are emphasized, but other workstations and software may be used for the project.

709 Topics in Computational Sciences and Informatics (3:3:0). Prerequisites: Admission to PhD program and permission of instructor. Covers selected topics in computational sciences and informatics not covered in fixed-content computational sciences and informatics courses. May be repeated for credit as needed.

710 Scientific Databases (3:3:0). Prerequisite: INFS 614 or equivalent, or permission of instructor. Study of database support for scientific data management. Covers requirements and properties of scientific databases, data models for statistical and scientific databases, semantic and object-oriented modeling of application domains, statistical database query languages and query optimization, advanced logic query languages, and case studies such as the human genome project and Earth-orbiting satellites.

711/CHEM 633 Chemical Thermodynamics and Kinetics (3:3:0). Prerequisites: CHEM 331 and 332. Advanced study of thermodynamics and kinetics. Covers application of kinetics to the elucidation of reaction mechanisms, and application of statistical thermodynamics to the theory of elementary reaction rates.

712/CHEM 728 Introduction to Solid Surfaces (3:3:0). Prerequisite: CHEM 422 or equivalent. Introduction to the properties of solid surfaces. Includes gas absorption isotherms, surface area measurement techniques, real and clean surfaces, physisorption and chemisorption, methods of gas absorption and desorption, measurement of heats of adsorption, desorption kinetics, electron spectroscopies and their surface sensitivities, instrumentation needed, and principles of vacuum technology.

713/CHEM 732 Quantum Chemistry (3:3:0). Prerequisite: CHEM 332. Illustration of the fundamental concepts of quantum mechanics with applications to chemical systems, including atomic and molecular electronic structure and properties, molecular symmetry, and intermolecular forces.

714 Spectroscopy and Structure (3:3:0). Prerequisite: CHEM 332. Covers quantum mechanics of the interaction of atoms and molecules with electromagnetic radiation. Also covers modern spectroscopic methods as applied to the elucidation of molecular structure and dynamics.

719 Topics in Computational Chemistry (3:3:0). Prerequisite: Permission of instructor. Covers selected topics in computational chemistry not covered in fixed-content computational chemistry courses. May be repeated for credit as needed.

720 Fluid Mechanics (3:3:0). Prerequisites: CSI 700, 780, or permission of instructor. Covers basic and advanced fluid mechanics and the continuous hypothesis to define fluids. Introduces tensor analysis; Euclidean and Lagrangian representation of fluid flow; Laplace's equation; the continuity equation; Navier-Stokes equations; the Bernoulli theorem and Crocco's form of the equations; steady and unsteady flows; potential, incompressible, and compressible flows; gravity and sound waves; gas dynamics; and viscous flows.

721 Computational Fluid Dynamics I (3:3:0). Prerequisites: Course in partial differential equations such as MATH 678 or equivalent, knowledge of linear algebra at the level of MATH 603 or CSI 740/MATH 625, coding experience in FORTRAN or C, or permission of instructor. Covers fundamentals of computational fluid dynamics, including spatial and temporal approximation techniques for partial differential equations, solution of large systems of equations, data structures, solvers of the Laplace/full potential equation, and simple Euler solvers. Two major projects are included: a Laplace solver and a 2-D Euler solver on unstructured grids. Students are expected to write their own codes.

722 Computational Fluid Dynamics II (3:3:0). Prerequisite: CSI 721 or permission of instructor. Covers some of the more advanced topics in computational fluid dynamics, including high-resolution schemes for hyperbolic PDEs, advanced Euler solvers, Navier-Stokes solvers, grid generation, adaptive mesh refinement, efficient use of supercomputing hardware, and future trends. Projects include topics in grid generation and adaptive refinement. Students are expected to write their own codes.

723 Fluid Mechanics II (3:3:0). Prerequisites: CSI 720 or permission of instructor. Covers gas dynamics, shock waves, the method of characteristics, boundary layer flows, instabilities, and turbulence modeling. Special topics include biological, non-Newtonian, and free surface flows; aeroelasticity; and magneto-hydrodynamics.

729 Topics in Continuum Systems (3:3:0). Prerequisite: Permission of instructor. Covers selected topics in the computational aspects of continuum systems not covered in fixed-content courses in dynamical systems. May be repeated for credit as needed. Possible topics that may be considered are smooth-particle hydrodynamics, radiation hydrodynamics, algorithms for continuum systems, adaptive grids for continuum computations, spectral methods in computational fluid dynamics, algorithms for concurrent machines, formation of high-energy particle jets in astrophysical applications, application to Earth atmospheric problems, and flow considerations in molten materials.

734 Computational Neurobiology (3:3:0). Prerequisites: BINF 631 or equivalent and ordinary differential equations, or permission of instructor. Intense review of neurobiology for graduate students interested in studying how nerve cells integrate and transmit signals, and how behavior emerges from the integrated actions of populations or circuits of nerve cells. Covers electrical and biochemical properties of single neurons, and electrical and chemical communication between neurons. Emphasis is on mathematical descriptions and computational techniques used to study and understand neurons and networks of neurons.

735 Computational Neuroscience Systems (3:3:0). Prerequisites: CSI 734 (previously or concurrently), BINF 631, or permission of instructor. Overview of the nervous system and biological neural networks. Includes learning and memory, sensory systems, and motor systems. Stresses design and application of computational models. Students are required to propose and design a computational model that addresses some open issue in neuroscience.

739 Topics in Bioinformatics (3:3:0). Prerequisite: Permission of instructor. Selected topics in bioinformatics not covered in fixed-content bioinformatics courses. May be repeated for credit as needed.

740/MATH 625 Numerical Linear Algebra (3:3:0). Prerequisites: MATH 203 and some programming experience. Covers computational methods for matrix systems; theory and development of numerical algorithms for the solution of linear systems of equations, including direct and iterative methods; analysis of sensitivity of system to computer round off; and solution of least squares problems using orthogonal matrices. Also covers computation of eigenvalues and eigenvectors, singular value decomposition, and applications.

741/ECE 721 Nonlinear Dynamical Systems (3:3:0). Prerequisites: Knowledge of linear algebra, advanced calculus, and differential equations. Contemporary topics in the field of nonlinear dynamical systems are illustrated in mathematical models from the natural sciences and engineering. Traditional qualitative analysis of difference and differential equations provides the background for understanding chaotic behavior when it occurs in these models. Topics include stability of equilibria and periodic orbits, bifurcation theory, Hamiltonian systems, Lyapunov exponents, and chaotic attractors.

742/MATH 687 The Mathematics of the Finite Element Method (3:3:0). Prerequisite: MATH 446 or 685, or permission of instructor. The finite element method is a commonly used technique for developing numerical approximations to problems involving ordinary and partial differential equations. This course develops the underlying mathematical foundation for the method, examines several specific types of finite elements, analyzes the convergence rates and approximation properties of the method, and uses it to solve a number of important equations. Students develop their own codes and are expected to complete independent projects.

744 Linear and Nonlinear Modeling in the Natural Sciences (3:3:0). Prerequisite: Permission of instructor. Develops the tools of mathematical modeling while carrying out numerical simulations of the models. Examples from across the sciences are considered throughout the course. Topics include basic issues (models, simplification, linearity, and nonlinearity), dimensionless parameters, dimensional analysis, models involving differential equations, examples from population growth and chemical kinetics, models involving partial differential equations, diffusion, transport, nonlinearity and shocks, probabilistic modeling, perturbation methods, extrapolation, and an introduction to stability.

745 Mathematical Tomography (3:3:0). Prerequisite: MATH 675. Covers physical principles of tomography; the Radon transform in Euclidean space; inversion formulas; the Radon transform on distributions; integral geometry and generalized Radon transforms; the Radon transform on symmetric spaces; and applications to CAT, PET, radar imaging, and synthetic aperture radar.

746 Wavelet Theory (3:3:0). Prerequisites: Knowledge of convolution and Fourier transforms of sequences; some familiarity with Hilbert space theory helpful but not required; knowledge of a scientific programming language. Study of the theory and computational aspects of wavelets and the wavelet transform. Emphasizes computational aspects of wavelets, defining the Fast Wavelet Transform in one and two dimensions and developing the appropriate numerical algorithms, then develops the theory of wavelet bases on the real line, discussing multiresolution analysis, splines, time-frequency localization, and wavelet packets.

748/MATH 629 Symbolic Computation (3:3:0). Prerequisites: Undergraduate degree in a scientific discipline, and a course in abstract algebra. Provides the mathematical and computational background for computational algebraic geometry and its applications. Includes notions of algebra, geometry, algorithms, the concept of Groebner bases, automatic theorem proving, and serial and parallel algorithms and their complexity. Topics are related to applications in engineering and computer science. Students are expected to complete projects.

749 Topics in Computational Mathematics (3:3:0). Prerequisite: Permission of instructor. Selected topics in computational mathematics not covered in fixed-content computational mathematics courses. May be repeated for credit as needed.

750 Earth Systems and Global Changes (3:3:0). Prerequisite: Course in ecology, environmental geology, atmospheric physics, or permission of instructor. Introduction to the global system interactions responsible for global environmental change. Discusses the natural causes of past and present global changes, how human activities affect these global system changes, and the ecological and human consequences of these global changes. Topics include climate and hydrological systems, global warming, deforestation, ozone depletion, ecological system dynamics, introduction to climate and global change monitoring, satellite instrumentation and calibration, and model predictions.

758 Visualization and Modeling of Complex Systems (3:3:0). Prerequisite: Permission of instructor. Covers elements of modeling and analysis of Earth and space sciences data and systems. Concentrates on both sample projects and student-initiated projects as a means of using visualization and graphical analysis techniques as they apply to the modeling of complex data sets and systems. Several different analysis and visualization packages are used. Spacecraft data sets from the Naval Research Laboratory (NRL) Backgrounds Data Center and other NRL data sets are available for course projects. A perusal of data sets from the World Wide Web is also possible. Modeling and analysis are accompanied by appropriate readings from the current literature.

761/ASTR 761 N-Body Methods and Particle Simulations (3:3:0). Prerequisites: PHYS 613/CSI 780 and CSI 700 or permission of instructor. Covers particle methods as a tool in solving a variety of physical systems. Emphasizes the study and development of the numerical results and visualization of these results in complex physical systems. Applications and projects include stellar and galaxy dynamics, smoothed particle hydrodynamics, plasma simulations, and semiconductor device theory algorithms on parallel and vectorized systems.

763 Statistical Methods in Space Sciences (3:3:0). Prerequisite: ASTR 530 or permission of instructor. Covers statistical and data analysis methods applicable to prob lems in space science, remote sensing, and astrophysics. Includes parametric and nonparametric hypothesis testing, parameter estimation, correlation analysis, time series analysis, spatial analysis, and image reconstruction. Emphasizes the imperfect nature of actual data sets and hypothesis. Examples are drawn from current space science research.

764/ASTR 764 Computational Astrophysics (3:3:0). Prerequisite: ASTR 530. Covers statistical mechanics concepts important in astrophysics. Presents unified approach to particle acceleration and interaction theory based on analytical and numerical analysis of Boltzmann and Liouville equations. Discusses computational methods relevant to particle transport problems, with emphasis on Fokker-Planck and Monte Carlo solution techniques. Applications from space sciences include studies of cosmic ray acceleration, photon comptonization, particle transport in the near-Earth environment, energy transport in stellar atmospheres, and self-gravitating system dynamics.

765/ASTR 765 High-Energy and Accretion Astrophysics (3:3:0). Prerequisite: PHYS 502, ASTR 530, PHYS 613/CSI 780, or permission of instructor. Overview of the field of atomic and nuclear physics. Covers nuclear reactions of use to high-energy astrophysics; radiation processes in cosmic plasmas, emphasizing quantum mechanical calculations; stellar evolution and nucleosynthesis; computational models of stellar evolution; binary stars and accretion disks; numerical models of the structure of accretion disks; compact stars, white dwarfs, neutron stars, and black holes; acceleration processes and cosmic rays; interstellar medium and propagation of cosmic rays; high-energy processes in the center of galaxies; and ground- and space-based techniques and observations.

766/ASTR 766 Relativity and Cosmology (3:3:0). Prerequisites: ASTR 530 and MATH 314, or permission of instructor. Covers special relativity, four-dimensional space-time, general relativity, non-Euclidean geometries, geodesic and field equations, test of general relativity theory, black holes, cosmic background radiation, thermodynamic considerations in cosmology, and cosmological models.

769/ASTR 769 Topics in Space Sciences (3:3:0). Prerequisite: Permission of instructor. Selected topics in space sciences not covered in fixed-content space sciences courses. May be repeated for credit as needed.

771/STAT 751 Computational Statistics (3:3:0). Prerequisites: STAT 544, 554, and 652. Covers the basic computationally intensive statistical methods and related methods, which would not be feasible without modern computational resources. Covers nonparametric density estimation including kernel methods, orthogonal series methods and multivariate methods, recursive methods, cross-validation, nonparametric regression, penalized smoothing splines, the jackknife and bootstrapping, computational aspects of exploratory methods including the grand tour, projection pursuit, alternating conditional expectations, and inverse regression methods.

773/STAT 663 Statistical Graphics and Data Exploration (3:3:0). Prerequisite: Three hundred-level course in statistics; STAT 554 strongly recommended. Exploratory data analysis provides a reliable alternative to classical statistical techniques, which are designed to be the best possible when stringent assumptions apply. Topics include graphical techniques such as scatter plots, box plots, parallel coordinate plots, and other graphical devices; re-expression and transformation of data; influence and leverage; and dimensionality reduction methods such as projection pursuit.

775/OR 719/STAT 719 Computational Models of Probabilistic Reasoning (3:3:0). Prerequisites: STAT 652 or 664, or permission of instructor. Introduction to theory and methods for building computationally efficient software agents that reason, act, and learn environments characterized by noisy and uncertain information. Covers methods based on graphical probability and decision models. Students study approaches to representing knowledge about uncertain phenomena, and planning and acting under uncertainty. Topics include knowledge engineering, exact and approximate inference in graphical models, learning in graphical models, temporal reasoning, planning, and decision-making. Practical model building experience is provided. Students apply what they learn to a semester-long project of their own choosing.

776/IT 746 Stochastic Calculus (3:3:0). Prerequisites: STAT 652; ECE 630 or 632; or permission of instructor. Introduction to modern theory of stochastic calculus. Covers stochastic integrals, martingales, counting processes, diffusion processes, and Ito-type processes in general. Applications of these methods to engineering, biology, and economics are considered in some detail.

777 Principles of Knowledge Mining (3:3:0). Prerequisites: INFS 614 or equivalent, or permission of instructor. A presentation of principles and methods for synthesizing task-oriented knowledge from computer data and prior knowledge, and presenting it in human-oriented forms, such as symbolic descriptions, natural language-like representations, and graphical forms. Topics include fundamental concepts of knowledge mining, methods for target data generation and optimization, statistical and symbolic approaches, knowledge representation and visualization, and new developments such as inductive databases, knowledge generation languages, and knowledge scouts.

778/IT 776 Real Analysis and Statistics (3:3:0). Prerequisites: STAT 652; ECE 620, 621, or 630; or permission of instructor. Advanced calculus and linear algebra needed for doctoral work in statistics and related fields. Covers topology, vector spaces, matrices, continuity, differentiation, sequences and series of real numbers and real-valued functions, Riemann and Riemann-Stieltjes integrals, and multidimensional calculus. Presents applications in probability and statistics, including response surface methodology.

779 Topics in Computational Statistics (3:3:0). Prerequisite: Permission of instructor. Selected topics in computational statistics not covered in fixed-content computational statistics courses. May be repeated for credit as needed.

780/PHYS 613 Computational Physics and Applications (3:3:0). Prerequisites: PHYS 510; FORTRAN, C, or C++ programming; or permission of instructor. PHYS 502 or equivalent recommended. Study of diverse physical systems with emphasis on modeling and simulation. Development of numerical algorithms and application of numerical methods to gain understanding of the mechanisms and processes taking place in the physical system. Several projects are undertaken, which are drawn from such areas as atomic and molecular interactions, molecular dynamics, quantum systems, chaos, percolation, random walks, and aggregation mechanisms.

781 Plasma Science (3:3:0). Prerequisite: PHYS 513 or PHYS 722/CSI 785, PHYS 711/CSI 782/CHEM 730; or permission of instructor. Study of ionized matter, theory, and some computation with application to astrophysics, industrial plasma processing, magnetosphere, and ionosphere problems. Vlasov and fluid equations are derived and applied in plasma science, including the study of plasmas with and without magnetic fields.

782/PHYS 711 Statistical Mechanics (3:3:0). Prerequisites: PHYS 502 and 613 or permission of instructor. Covers microcanonical, canonical, and grand canonical ensembles and fluctuations, Fermi-Dirac and Bose-Einstein statistics, the ideal monatomic gas and diatomic gas, the Liouville equation, equipatition of energy, crystals, imperfect gases, kinetic theory, quantum statistics, and transport processes.

783/PHYS 736 Computational Quantum Mechanics (3:3:0). Prerequisites: PHYS 502 and PHYS 613/CSI 780, or permission of instructor. Study of the fundamental concepts of quantum mechanics from a computational point of view, review of systems with spherically symmetric potentials, many electron-atom solutions to Schroedinger's equation, electron spin in many-electron systems, atomic structure calculations, algebra of many-electron calculations, Hartree-Fock self-consistent field method, molecular structure calculations, scattering theory computations, and solid-state computations.

784/PHYS 732 Quantum Mechanics (3:3:0). Prerequisite: PHYS 502 or permission of instructor. Study of the fundamental concepts of quantum mechanics, time evolution, Schroedinger and Heisenberg formalism, harmonic oscillators, propagators, Feynman path integrals, rotations and angular momentum, angular momentum eigenvalues and eigenstates, Bell's inequality, symmetries, conservation laws, degeneracy, perturbation theory, WKB methods, and scattering theory.

785/PHYS 722 Electromagnetic Theory (3:3:0). Prerequisites: PHYS 513 and PHYS 613/CSI 780, or permission of instructor. Advanced study of electric and magnetic fields. Topics include electrostatic fields, magnetostatic fields, boundary-value problems in field theory, multipoles, simple radiating systems, relativistic electrodynamics, and radiation by moving charges.

786 Molecular Dynamics Modeling (3:3:0). Prerequisite: PHYS 613/CSI 780 or CHEM 633/CSI 711, or permission of instructor. Introduction to simulation methods used in the physical chemistry sciences. Covers computational approaches to modeling molecular and condensed matter systems, including interatomic and molecular potentials, molecular dynamics, time averages, ensemble distributions, numerical sampling, thermodynamic functions, response theory, transport coefficients, and dynamic structure. Includes stochastic simulations such as Brownian motion, Langevin dynamics, Monte Carlo methods and random walks, and an introduction to cellular automata.

787 Computational Materials Science (3:3:0). Prerequisites: PHYS 512/CSI 687 and PHYS 736/CSI 783, or permission of instructor. Covers selected topics in the computational aspects of condensed matter, such as methods of electronic structure calculations, surface science, molecular clusters, lattice dynamics, nanomaterials, semiconductors, superconductivity, quantum Hall effect, magnetism, Hubbard model, mesoscopic systems, and liquids.

788/PHYS 728 Simulation of Large-Scale Physical Systems (3:3:0). Prerequisites: PHYS 613/CSI 780 and CSI 700, or permission of instructor. Study of diverse large-scale physical systems, with emphasis on the modeling and simulation of these multifaceted systems. Several projects are undertaken, which are drawn from such areas as many-body dynamics, atmospheric structure and dynamics, high- temperature plasmas, stellar structure, hydro dynamical systems, galactic structure and interactions, and cosmology.

789/PHYS 780 Topics in Computational Physics (3:3:0). Prerequisite: Permission of instructor. Selected topics in computational physics not covered in fixed-content computational physics courses. May be repeated for credit as needed.

796 Directed Reading and Research (1-6:0:0). Prerequisite: Permission of instructor. Reading and research on a specific topic in computational sciences and informatics under the direction of a faculty member. May be repeated as necessary.

798 Research Project (3:0:0). Prerequisites: Twelve graduate credits and permission of instructor. Project chosen and completed under the guidance of a graduate faculty member, resulting in an acceptable technical report.

799 Master's Thesis (1-6:0:0). Prerequisites: Twelve graduate credits and permission of instructor. Project chosen and completed under the guidance of a graduate faculty member, resulting in an acceptable technical report (master's thesis) and oral defense. Graded S/IP.

853 Atmospheric Transport and Dispersion (3:3:0). Prerequisites: CLIM 710 or 711 or equivalent, or permission of instructor. This course develops the basic concepts, theories, and models describing pollutant dispersal in the atmosphere. The related atmospheric systems affecting transport, transformation, and removal of air pollutants are also discussed, with a strong emphasis on the fundamental issues associated with hazard prediction. The content presented is essential for students engaging in graduate research in atmospheric transport and dispersion modeling.

854 Computing and Communication Systems for Earth Observing (3:3:0). Prerequisite: EOS 754 or permission of instructor. In-depth study of computing and communications systems, with emphasis on performance issues and capacity for sustaining modern Earth observing systems. Covers functional breakdown of ground receiving stations, international communications standards for space data telemetry (such as CCSDS) and their impact on data fidelity and processing, and instrumentation for ground stations and trade-off between on-board versus ground station processing. Also discussed are computer system performance appreciation and computing systems limitations; implications of data product levels and standards for processing, input/output, and storage requirements; and applications of high performance computing, storage hierarchies, and parallel input/output concepts and systems for speeding data access and processing.

873 Computational Learning and Discovery (3:0:0). Prerequisites: CS 580 or equivalent, or permission of instructor. This course presents modern ideas, theories, and methods for computational learning and discovery, along with relevant applications. Application areas include medical diagnosis, earth science data analysis, and neuronal modeling. The course will include a background elucidation of fundamental concepts in computational learning, addressing in particular discovery of equations, theory of causality, and comparison with biological and cognitive models. Students will have an opportunity to make presentations on topics of their research interest, and to work on projects involving state-of-the art systems.

876/IT 876 Measure and Linear Spaces (3:3:0). Prerequisite: IT 776/CSI 778 or permission of instructor. Covers measure theory and integration, convergence theorems, and the theory of linear spaces and functional analysis, including normed linear spaces, inner product spaces, Banach and Hilbert spaces, Sobelev spaces, and reproducing kernels. Topics include wavelets, applications to stochastic processes, and nonparametric functional inference.

877/IT 877 Geometric Methods in Statistics (3:3:0). Prerequisite: STAT 751 or permission of instructor. Develops the foundations of geometric methods for statistics. Topics include n-dimension Euclidian geometry; projective geometry; differential geometry, including curves, surfaces, and n-dimensional differentiable manifolds; and computational geometry, including computation of convex hulls, tessellations of two-, three-, and n-dimensional spaces, and finite element grid generation. Examples include applications to scientific visualization.

888 Topics in Quantum Systems (3:3:0). Prerequisite: PHYS 736/CSI 783 or PHYS 732/CSI 784, or permission of instructor. Covers selected topics in quantum systems in physics and chemistry not covered in fixed-content courses in quantum mechanics. May be repeated for credit as needed. Possible topics are new spectroscopic methods, density functional theory, energy transfer and fluorescence, nuclear magnetic resonance, Mossbauer spectroscopy, advanced computational considerations in atomic and/or molecular structure, nuclear scattering theory, quantum considerations in condensed matter problems, and quantum gravity.

898 Research Colloquium in Computational Sciences and Informatics (1:1:0). Presentations in specific research areas in computational sciences and informatics by School of Computational Sciences faculty and staff members, and professional visitors. May be repeated for credit; however, a maximum of 3 credits of CSI 898, 899, and 991 may be applied toward the PhD.

899 Colloquium in Computational Sciences and Informatics (1:1:0). Presentations in a variety of areas of computational sciences and informatics by School of Computational Sciences faculty and staff members, and professional visitors. May be repeated for credit; however, a maximum of 3 credits of CSI 898, 899, and 991 may be applied toward the PhD.

909 Advanced Topics in Computational Sciences and Informatics (3:3:0). Prerequisite: Permission of instructor. Covers selected topics in computational sciences and informatics not covered in fixed-content courses. May be repeated for credit as necessary.

972/IT 972 Mathematical Statistics I (3:3:0). Prerequisite: STAT 652 or equivalent. Focuses on the theory of estimation. Principles of estimation are explored, including the method of moments, least squares, maximum likelihood, and maximum entropy methods. Methods of minimum variance unbiased estimation are covered in detail. Other topics include sufficiency and completeness of statistics, Fisher information, Cramer-Rao bounds, Bhattacharyya bounds, asymptotic consistency and distributions, statistical decision theory, minimax and Bayesian decision rules, and applications to engineering and scientific problems.

973/IT 973 Mathematical Statistics II (3:3:0). Prerequisite: CSI 972. Continuation of CSI 972. Concentrates on the theory of hypothesis testing. Topics include characterizing the decision process, simple versus simple hypothesis tests, Neyman Pearson Lemma, uniformly most powerful tests, unbiasedness of tests, invariance of tests, randomized tests, and sequential tests. Applications of the testing principles are made to situations in the normal distribution family and to other families of distributions.

976/IT 976 Statistical Inference for Stochastic Processes (3:3:0). Prerequisite: CSI 776 or permission of instructor. Covers the modern theory of parameter estimation and hypothesis testing for stochastic processes, counting processes with random intensities, and solutions to stochastic differential equations driven by martingales. Applications to engineering, biology, and economics are considered.

978/IT 978 Statistical Analysis of Signals (3:3:0). Prerequisites: STAT 544 and 658 or equivalent. Advanced course in the analysis of discrete- and continuous-time signals using methods of stochastic differential equation and time series. Familiarity with the methods of harmonic analysis and times series modeling is presumed. Topics include state-space modeling and eigen-value processing, nonlinear modeling of signals, non-Gaussian stochastic process structure, detection and estimation of vector-valued signals, robust signal detection, and array processing and target tracking.

979/IT 979 Advanced Topics in Computational Statistics (3:3:0). Prerequisite: Permission of instructor. Covers selected topics in computational statistics not covered in fixed-content computational statistics courses. May be repeated for credit as needed.

986 Advanced Topics in Large-Scale Physical Simulation (3:3:0). Prerequisite: Permission of instructor. Covers simulation of physical systems not covered in fixed-content physical simulation courses. May be repeated for credit as needed.

991 Seminar in Scientific Computing (1:1:0). Considers selected topics in a specific area of computational sciences and informatics either not covered in fixed-content courses or as an extension of fixed-content courses. Format for presentation is that of a seminar with student participation. May be repeated for credit; however, a maximum of 3 credits of CSI 898, 899, and 991 may be applied toward the PhD.

996 Doctoral Reading and Research (1-6:0:0). Prerequisites: Admission to doctoral program and permission of instructor. Reading and research on a specific topic in computational sciences and informatics under the direction of a faculty member. May be repeated as needed.

998 Doctoral Dissertation Proposal (1-12:0:0). Prerequisite: Permission of advisor. Covers development of a research proposal under the guidance of a dissertation director and the doctoral committee. The proposal forms the basis for the doctoral dissertation. This course may be repeated as needed; however, no more than a total of 24 credits in CSI 998 and 999 may be applied toward satisfying doc toral degree requirements. Out of the 24-hour total, no more than 12 credits of CSI 998 may be applied.

999 Doctoral Dissertation (1-12:0:0). Prerequisite: Admission to doctoral candidacy. Involves doctoral dissertation research under the direction of the dissertation director. May be repeated as needed; however, no more than a total of 24 credits in CSI 998 and 999 may be applied toward satisfying doctoral degree requirements.