CME 20Q:
Computational Modeling for Future Leaders
Preference to sophomores. How can we harness and exploit the power of computational modeling? What responsibilities are there in developing and using computer models? In this course we will analyze fundamental issues inherent to computational modeling such as uncertainty, predictability, error, and resolution. We will furthermore examine the social context of computational modeling including the public perception of computational models, how computer modeling impacts politics and policy, and how politics and policy, in turn, influence computer modeling.
Terms: Aut

Units: 3

Grading: Letter (ABCD/NP)
CME 100:
Vector Calculus for Engineers (ENGR 154)
Computation and visualization using MATLAB. Differential vector calculus: analytic geometry in space, functions of several variables, partial derivatives, gradient, unconstrained maxima and minima, Lagrange multipliers. Introduction to linear algebra: matrix operations, systems of algebraic equations, methods of solution and applications. Integral vector calculus: multiple integrals in Cartesian, cylindrical, and spherical coordinates, line integrals, scalar potential, surface integrals, Green¿s, divergence, and Stokes¿ theorems. Examples and applications drawn from various engineering fields. Prerequisites: 10 units of AP credit (Calc BC with 4 or 5, or Calc AB with 5), or Math 41 and 42. Note: Students enrolled in section 10002 and 100A02 are required to attend the discussion section (section 03) on Thursdays 4:305:50pm.
Terms: Aut, Win

Units: 5

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors: ;
Khayms, V. (PI);
Mani, A. (PI);
Osgood, B. (PI);
Ahluwalia, V. (TA);
Genin, M. (TA);
Haaland, C. (TA);
Harris, S. (TA);
Inamdar, A. (TA);
Jiang, R. (TA);
Li, Y. (TA);
Patki, R. (TA);
Ruan, K. (TA);
Sheshadri, A. (TA);
Siripuram, A. (TA);
Zhang, W. (TA);
de Lichy, C. (TA)
CME 100A:
Vector Calculus for Engineers, ACE
Students attend CME100/ENGR154 lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Enrollment by department permission only. Prerequisite: application at:http://soe.stanford.edu/current_students/edp/programs/ace.html
Terms: Aut, Win

Units: 6

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors: ;
Khayms, V. (PI);
Mani, A. (PI);
Osgood, B. (PI);
Ahluwalia, V. (TA);
Genin, M. (TA);
Inamdar, A. (TA);
Jiang, R. (TA);
Li, Y. (TA);
Patki, R. (TA);
Ruan, K. (TA);
Sheshadri, A. (TA);
Siripuram, A. (TA);
Zhang, W. (TA);
de Lichy, C. (TA)
CME 102:
Ordinary Differential Equations for Engineers (ENGR 155A)
Analytical and numerical methods for solving ordinary differential equations arising in engineering applications: Solution of initial and boundary value problems, series solutions, Laplace transforms, and nonlinear equations; numerical methods for solving ordinary differential equations, accuracy of numerical methods, linear stability theory, finite differences. Introduction to MATLAB programming as a basic tool kit for computations. Problems from various engineering fields. Prerequisite: 10 units of AP credit (Calc BC with 4 or 5, or Calc AB with 5), or Math 41 and 42. Recommended: CME100.
Terms: Aut, Win, Spr, Sum

Units: 5

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors: ;
Le, H. (PI);
Moin, P. (PI);
Chen, L. (TA);
Dancoisne, B. (TA);
DebaillonVesque, O. (TA);
Dupont, E. (TA);
Gao, P. (TA);
Genin, M. (TA);
Harris, S. (TA);
Patki, R. (TA);
Paudel, S. (TA);
Shaikh, S. (TA);
Sheshadri, A. (TA);
Sunder Raj, A. (TA);
Suresha, S. (TA);
Usmani, S. (TA);
de Lichy, C. (TA);
shirian, y. (TA)
CME 102A:
Ordinary Differential Equations for Engineers, ACE
Students attend CME102/ENGR155A lectures with additional recitation sessions; two to four hours per week, emphasizing engineering mathematical applications and collaboration methods. Prerequisite: students must be enrolled in the regular section (CME102) prior to submitting application at:http://soe.stanford.edu/current_students/edp/programs/ace.html
Terms: Aut, Win, Spr

Units: 6

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors: ;
Le, H. (PI);
Moin, P. (PI);
Chen, L. (TA);
Dancoisne, B. (TA);
Dupont, E. (TA);
Gao, P. (TA);
Genin, M. (TA);
Patki, R. (TA);
Paudel, S. (TA);
Sunder Raj, A. (TA);
Suresha, S. (TA);
Usmani, S. (TA);
de Lichy, C. (TA)
CME 103:
Introduction to Matrix Methods (EE 103)
Introduction to applied linear algebra with emphasis on applications. Vectors, norm, and angle; linear independence and orthonormal sets. Matrices, left and right inverses, QR factorization. Least squares and model fitting, regularization and crossvalidation, timeseries prediction, and other examples. Constrained leastsquares; applications to leastnorm reconstruction, optimal control, and portfolio optimization. Newton methods and nonlinear leastsquares. Prerequisites: MATH 51 or CME 100.
Terms: Aut

Units: 45

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
CME 151:
Introduction to Data Visualization
Bring your data to life with beautiful and interactive visualizations. This course is designed to provide practical experience on combining data science and graphic design to effectively communicate knowledge buried inside complex data. Each lecture will explore a different set of free industrystandard tools, for example d3.js, three.js, ggplots2, and processing; enabling students to think critically about how to architect their own interactive visualization for data exploration, web, presentations, and publications. Geared towards scientists and engineers, and with a particular emphasis on web, this course assumes an advanced background in programming methodology in multiple languages (particularly R and Javascript). Assignments are short and focus on visual experimentation with interesting data sets or the students' own data. Topics: data, visualization, web. Prerequisites: some experience with general programming is required to understand the lectures and assignments.
Terms: Aut, Win

Units: 1

Grading: Satisfactory/No Credit
CME 192:
Introduction to MATLAB
This short course runs for the first eight weeks of the quarter and is offered each quarter during the academic year. It is highly recommended for students with no prior programming experience who are expected to use MATLAB in math, science, or engineering courses. It will consist of interactive lectures and applicationbased assignments.nThe goal of the short course is to make students fluent in MATLAB and to provide familiarity with its wide array of features. The course covers an introduction of basic programming concepts, data structures, and control/flow; and an introduction to scientific computing in MATLAB, scripts, functions, visualization, simulation, efficient algorithm implementation, toolboxes, and more.
Terms: Aut, Win, Spr

Units: 1

Grading: Satisfactory/No Credit
CME 193:
Introduction to Scientific Python
This short course runs for the first four weeks of the quarter. It is recommended for students who are familiar with programming at least at the level of CS106A and want to translate their programming knowledge to Python with the goal of becoming proficient in the scientific computing and data science stack. Lectures will be interactive with a focus on real world applications of scientific computing. Technologies covered include Numpy, SciPy, Pandas, Scikitlearn, and others. Topics will be chosen from Linear Algebra, Optimization, Machine Learning, and Data Science. Prior knowledge of programming will be assumed, and some familiarity with Python is helpful, but not mandatory.
Terms: Aut, Win, Spr

Units: 1

Grading: Satisfactory/No Credit
CME 195:
Introduction to R (STATS 195)
This short course runs for the first four weeks of the quarter and is offered in fall and spring. It is recommended for students who want to use R in statistics, science, or engineering courses and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be applicationdriven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, graphs, control structures, etc, and some useful packages in R.
Terms: Aut, Spr

Units: 1

Grading: Satisfactory/No Credit
CME 200:
Linear Algebra with Application to Engineering Computations (ME 300A)
Computer based solution of systems of algebraic equations obtained from engineering problems and eigensystem analysis, Gaussian elimination, effect of roundoff error, operation counts, banded matrices arising from discretization of differential equations, illconditioned matrices, matrix theory, least square solution of unsolvable systems, solution of nonlinear algebraic equations, eigenvalues and eigenvectors, similar matrices, unitary and Hermitian matrices, positive definiteness, CayleyHamilton theory and function of a matrix and iterative methods. Prerequisite: familiarity with computer programming, and MATH51.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
CME 206:
Introduction to Numerical Methods for Engineering (ME 300C)
Numerical methods from a user's point of view. Lagrange interpolation, splines. Integration: trapezoid, Romberg, Gauss, adaptive quadrature; numerical solution of ordinary differential equations: explicit and implicit methods, multistep methods, RungeKutta and predictorcorrector methods, boundary value problems, eigenvalue problems; systems of differential equations, stiffness. Emphasis is on analysis of numerical methods for accuracy, stability, and convergence. Introduction to numerical solutions of partial differential equations; Von Neumann stability analysis; alternating direction implicit methods and nonlinear equations. Prerequisites: CME 200/ME 300A, CME 204/ME 300B.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
CME 207:
Numerical Methods in Engineering and Applied Sciences (AA 214A, GEOPHYS 217)
Scientific computing and numerical analysis for physical sciences and engineering. Advanced version of CME206 that, apart from CME206 material, includes nonlinear PDEs, multidimensional interpolation and integration and an extended discussion of stability for initial boundary value problems. Recommended for students who have some prior numerical analysis experience. Topics include: 1D and multiD interpolation, numerical integration in 1D and multiD including adaptive quadrature, numerical solutions of ordinary differential equations (ODEs) including stability, numerical solutions of 1D and multiD linear and nonlinear partial differential equations (PDEs) including concepts of stability and accuracy. Prerequisites: linear algebra, introductory numerical analysis (CME 108 or equivalent).
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
CME 211:
Software Development for Scientists and Engineers (EARTH 211)
Basic usage of the Python and C/C++ programming languages are introduced and used to solve representative computational problems from various science and engineering disciplines. Software design principles including time and space complexity analysis, data structures, objectoriented design, decomposition, encapsulation, and modularity are emphasized. Usage of campus wide Linux compute resources: login, file system navigation, editing files, compiling and linking, file transfer, etc. Versioning and revision control, software build utilities, and the LaTeX typesetting software are introduced and used to help complete programming assignments. Prerequisite: introductory programming course equivalent to CS 106A or instructor consent.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
CME 214:
Software Design in Modern Fortran for Scientists and Engineers (EARTH 214)
This course introduces software design and development in modern Fortran. Course covers the functional, objectoriented, and parallel programming features introduced in the Fortran 95, 2003, and 2008 standards, respectively, in the context of numerical approximations to ordinary and partial differential equations; introduces objectoriented design and design schematics based on the Unified Modeling Language (UML) structure, behavior, and interaction diagrams; cover the basic use of several opensource tools for software building, testing, documentation generation, and revision control. Recommended: Familiarity with programming in Fortran 90, basic numerical analysis and linear algebra, or instructor approval
Terms: alternate years, given next year

Units: 3

Grading: Letter (ABCD/NP)
CME 242:
Mathematical and Computational Finance Seminar (STATS 239)
Terms: Aut, Spr

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
CME 244:
Project Course in Mathematical and Computational Finance
For graduate students in the MCF track; students will work individually or in groups on research projects.
Terms: Aut, Win, Spr, Sum

Units: 16

Grading: Letter (ABCD/NP)
CME 245:
Topics in Mathematical and Computational Finance
Current topics for enrolled students in the MCF program; can be repeated up to three times.
Terms: Aut, Win, Spr, Sum

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
CME 250:
Introduction to Machine Learning
A Short course presenting the principles behind when, why, and how to apply modern machine learning algorithms. We will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of overfitting/underfitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data. The principles behind various algorithmsthe why and how of using themwill be discussed, while some mathematical detail underlying the algorithmsincluding proofswill not be discussed. Unsupervised machine learning algorithms presented will include kmeans clustering, principal component analysis (PCA), and independent component analysis (ICA). Supervised machine learning algorithms presented will include support vector machines (SVM), classification and regression trees (CART), boosting, bagging, and random forests. Imputation, the lasso, and crossvalidation concepts will also be covered. The R programming language will be used for examples, though students need not have prior exposure to R. Prerequisite: undergraduatelevel linear algebra and statistics; basic programming experience (R/Matlab/Python).
Terms: Aut, Win, Spr

Units: 1

Grading: Satisfactory/No Credit
CME 252:
Introduction to Optimization
This course introduces mathematical optimization and modeling, with a focus on convex optimization. Topics include: varieties of mathematical optimization, convexity of functions and sets, convex optimization modeling with CVXPY, gradient descent and basic distributed optimization, indepth examples from machine learning, statistics and other fields and applications of biconvexity and nonconvex gradient descent.nRecommended prerequisite: familiarity with linear algebra, differential multivariable calculus, and basic probability and statistics. Experience with Python will be helpful, but not required.
Terms: Aut

Units: 1

Grading: Satisfactory/No Credit
CME 253:
Introduction to GPU Computing and CUDA
Covers the fundamentals of accelerating applications with GPUs (Graphics Processing Units); GPU programming with CUDA and OpenACC, debugging, thrust/CUB, profiling, optimization, debugging, and other CUDA tools. Libraries to easily accelerate compute code will be presented and deployment on larger systems will be addressed, including multiGPU environments. Several practical examples will be detailed, including deep learning. Prerequiste: knowledge of C/C++ at the level of CME211 or CS106b.
Terms: given next year

Units: 1

Grading: Satisfactory/No Credit
CME 257:
Advanced Topics in Scientific Computing with Julia
This short course runs from the 2nd to the 5th week of the quarter. This course will rapidly introduce students to the new Julia language, with the goal of giving students the knowledge and experience necessary to begin contributing to the language and package ecosystem while using Julia for their own scientific computing needs. The course will begin with learning the basics of Julia with an emphasis on its objectoriented features, and then introduce students to Github and package development. Additional topics include: common packages, interfacing with C shared object libraries, and Julia's core linear algebra implementation. Lectures will be interactive, with an emphasis on collaboration and learning by example. Prerequisites: Data structures at the level of CS106B, experience with one or more scientific computing languages (e.g. Python, Matlab, or R), and some familiarity with C/C++ and the Unix shell. No prior experience with Julia or Github is required.
Terms: Aut

Units: 1

Grading: Satisfactory/No Credit
CME 263:
Introduction to Linear Dynamical Systems (EE 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: leastsquares approximations of overdetermined equations, and leastnorm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singularvalue decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multiinput/multioutput systems, impulse and step matrices; convolution and transfermatrix descriptions. Control, reachability, and state transfer; observability and leastsquares state estimation. Prerequisites: linear algebra and matrices as in MATH104; differential equations and Laplace transforms as in EE102B.
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
CME 279:
Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, BIOPHYS 279, CS 279)
Computational approaches to understanding the threedimensional spatial organization of biological systems and how that organization evolves over time. The course will cover cuttingedge research in both physicsbased simulations and computational analysis of experimental data, at scales ranging from individual molecules to multiple cells. Prerequisites: elementary programming background (106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
CME 291:
Master's Research
Students require faculty sponsor. (Staff)
Terms: Aut, Win, Spr, Sum

Units: 16

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors: ;
Aiken, A. (PI);
Alonso, J. (PI);
Bambos, N. (PI);
Biondi, B. (PI);
Boneh, D. (PI);
Bosagh Zadeh, R. (PI);
Boyd, S. (PI);
Butte, A. (PI);
Candes, E. (PI);
Carlsson, G. (PI);
Constantinou, C. (PI);
Darve, E. (PI);
Davis, R. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Farhat, C. (PI);
Fedkiw, R. (PI);
Feinstein, J. (PI);
Fringer, O. (PI);
Fruchter, R. (PI);
Gerritsen, M. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Harris, J. (PI);
Jain, K. (PI);
Jameson, A. (PI);
Johari, R. (PI);
Kahn, S. (PI);
Kamvar, S. (PI);
Khayms, V. (PI);
Koltun, V. (PI);
Langley, P. (PI);
Lele, S. (PI);
Leskovec, J. (PI);
Levinson, D. (PI);
Lew, A. (PI);
Liu, T. (PI);
Manning, C. (PI);
McFarland, D. (PI);
Mignot, E. (PI);
Moin, P. (PI);
Murray, W. (PI);
Napel, S. (PI);
Ng, A. (PI);
Papanicolaou, G. (PI);
Pelger, M. (PI);
Rajaratnam, B. (PI);
Re, C. (PI);
Reed, E. (PI);
Saberi, A. (PI);
Saunders, M. (PI);
Shaqfeh, E. (PI);
Suckale, J. (PI);
Taylor, C. (PI);
Wall, D. (PI);
Wara, M. (PI);
Wechsler, R. (PI);
Wong, W. (PI);
Ye, Y. (PI);
Zenios, S. (PI);
Thompson, J. (GP)
CME 292:
Advanced MATLAB for Scientific Computing
Short course running first four weeks of the quarter (8 lectures) with interactive lectures and application based assignment. Students will be introduced to advanced MATLAB features, syntaxes, and toolboxes not traditionally found in introductory courses. Material will be reinforced with inclass examples, demos, and homework assignment involving topics from scientific computing. MATLAB topics will be drawn from: advanced graphics (2D/3D plotting, graphics handles, publication quality graphics, animation), MATLAB tools (debugger, profiler), code optimization (vectorization, memory management), objectoriented programming, compiled MATLAB (MEX files and MATLAB coder), interfacing with external programs, toolboxes (optimization, parallel computing, symbolic math, PDEs). Scientific computing topics will include: numerical linear algebra, numerical optimization, ODEs, and PDEs.
Terms: Aut

Units: 1

Grading: Satisfactory/No Credit
CME 300:
First Year Seminar Series
Required for firstyear ICME Ph.D. students; recommended for firstyear ICME M.S. students. Presentations about research at Stanford by faculty and researchers from Engineering, H&S, and organizations external to Stanford. May be repeated for credit.
Terms: Aut, Win, Spr

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
CME 302:
Numerical Linear Algebra
First in a three quarter graduate sequence. Solution of systems of linear equations: direct methods, error analysis, structured matrices; iterative methods and least squares. Parallel techniques. Prerequisites: CME 108, MATH 103 or 113.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
CME 303:
Partial Differential Equations of Applied Mathematics (MATH 220)
Firstorder partial differential equations; method of characteristics; weak solutions; elliptic, parabolic, and hyperbolic equations; Fourier transform; Fourier series; and eigenvalue problems. Prerequisite: foundation in multivariable calculus and ordinary differential equations.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
CME 309:
Randomized Algorithms and Probabilistic Analysis (CS 265)
Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms. Prerequisites: CS 161 and STAT 116, or equivalents and instructor consent.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
CME 330:
Applied Mathematics in the Chemical and Biological Sciences (CHEMENG 300)
Mathematical solution methods via applied problems including chemical reaction sequences, mass and heat transfer in chemical reactors, quantum mechanics, fluid mechanics of reacting systems, and chromatography. Topics include generalized vector space theory, linear operator theory with eigenvalue methods, phase plane methods, perturbation theory (regular and singular), solution of parabolic and elliptic partial differential equations, and transform methods (Laplace and Fourier). Prerequisites: CME 102/ENGR 155A and CME 104/ENGR 155B, or equivalents.
Terms: Aut

Units: 3

Grading: Letter (ABCD/NP)
CME 334:
Advanced Methods in Numerical Optimization (MS&E 312)
Topics include interiorpoint methods, relaxation methods for nonlinear discrete optimization, sequential quadratic programming methods, optimal control and decomposition methods. Topic chosen in first class; different topics for individuals or groups possible. Individual or team projects. May be repeated for credit.
Terms: Aut

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
CME 390:
Curricular Practical Training
Educational opportunities in high technology research and development labs in applied mathematics. Qualified ICME students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and followon projects they expect to perform. May be repeated three times for credit.
Terms: Aut, Win, Spr, Sum

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
CME 399:
Special Research Topics in Computational and Mathematical Engineering
Graduatelevel research work not related to report, thesis, or dissertation. May be repeated for credit.
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Letter or Credit/No Credit
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors: ;
Alonso, J. (PI);
Athey, S. (PI);
Bambos, N. (PI);
Beroza, G. (PI);
Boahen, K. (PI);
Boneh, D. (PI);
Bosagh Zadeh, R. (PI);
Boyd, S. (PI);
Bustamante, C. (PI);
Candes, E. (PI);
Carlsson, G. (PI);
Darve, E. (PI);
Delp, S. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Dror, R. (PI);
Farhat, C. (PI);
Fedkiw, R. (PI);
Fringer, O. (PI);
Genesereth, M. (PI);
Gerritsen, M. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Hong, H. (PI);
Iaccarino, G. (PI);
James, D. (PI);
Jameson, A. (PI);
Johari, R. (PI);
Kamvar, S. (PI);
Khatib, O. (PI);
Khayms, V. (PI);
Kitanidis, P. (PI);
Kosovichev, A. (PI);
Kumar, S. (PI);
Lai, T. (PI);
Langley, P. (PI);
Lee, P. (PI);
Lele, S. (PI);
Leskovec, J. (PI);
Levinson, D. (PI);
Levitt, M. (PI);
Lew, A. (PI);
Linder, C. (PI);
Liu, T. (PI);
Marsden, A. (PI);
Moerner, W. (PI);
Moin, P. (PI);
Montanari, A. (PI);
Motwani, R. (PI);
Murray, W. (PI);
Ng, A. (PI);
Pande, V. (PI);
Papanicolaou, G. (PI);
Pitsch, H. (PI);
Plevritis, S. (PI);
Poulson, J. (PI);
Rajagopal, R. (PI);
Rajaratnam, B. (PI);
Re, C. (PI);
Reed, E. (PI);
Roughgarden, T. (PI);
Ryzhik, L. (PI);
Saberi, A. (PI);
Salisbury, J. (PI);
Saunders, M. (PI);
Shaqfeh, E. (PI);
Suckale, J. (PI);
Taylor, C. (PI);
Tchelepi, H. (PI);
Tibshirani, R. (PI);
Wein, L. (PI);
Weissman, T. (PI);
Wong, W. (PI);
Xing, L. (PI);
Ye, Y. (PI);
Ying, L. (PI);
Kanagawa, K. (GP)
CME 444:
Computational Consulting
Advice by graduate students under supervision of ICME faculty. Weekly briefings with faculty adviser and associated faculty to discuss ongoing consultancy projects and evaluate solutions. May be repeated for credit.
Terms: Aut, Win, Spr

Units: 13

Repeatable for credit

Grading: Satisfactory/No Credit
CME 500:
Departmental Seminar
Weekly research lectures by experts from academia, national laboratories, industry, and doctoral students. May be repeated for credit. In autumn and winter 201415, this seminar will predominantly feature current graduate students talking about their research.
Terms: Aut, Win, Spr

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
CME 510:
Linear Algebra and Optimization Seminar
Recent developments in numerical linear algebra and numerical optimization. Guest speakers from other institutions and local industry. Goal is to bring together scientists from different theoretical and application fields to solve complex scientific computing problems. May be repeated for credit.
Terms: Aut, Win, Spr

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
Terms: Aut, Win, Spr, Sum

Units: 0

Repeatable for credit

Grading: TGR
Instructors: ;
Alonso, J. (PI);
Bambos, N. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Carlsson, G. (PI);
Darve, E. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Farhat, C. (PI);
Fedkiw, R. (PI);
Fringer, O. (PI);
Gerritsen, M. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Iaccarino, G. (PI);
Jameson, A. (PI);
Johari, R. (PI);
Kamvar, S. (PI);
Khayms, V. (PI);
Kosovichev, A. (PI);
Kumar, S. (PI);
Langley, P. (PI);
Lele, S. (PI);
Lew, A. (PI);
Moin, P. (PI);
Motwani, R. (PI);
Murray, W. (PI);
Papanicolaou, G. (PI);
Saberi, A. (PI);
Saunders, M. (PI);
Shaqfeh, E. (PI);
Taylor, C. (PI);
Wein, L. (PI);
Wong, W. (PI);
Xing, L. (PI);
Ye, Y. (PI)
CME 802:
TGR Dissertation
Terms: Aut, Win, Spr, Sum

Units: 0

Repeatable for credit

Grading: TGR
Instructors: ;
Alonso, J. (PI);
Andrews, J. (PI);
Athey, S. (PI);
Bambos, N. (PI);
Beroza, G. (PI);
Boneh, D. (PI);
Boyd, S. (PI);
Candes, E. (PI);
Carlsson, G. (PI);
Darve, E. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Farhat, C. (PI);
Fedkiw, R. (PI);
Fringer, O. (PI);
Gerritsen, M. (PI);
Giesecke, K. (PI);
Glynn, P. (PI);
Goel, A. (PI);
Guibas, L. (PI);
Hanrahan, P. (PI);
Hong, H. (PI);
Iaccarino, G. (PI);
Jameson, A. (PI);
Johari, R. (PI);
Kamvar, S. (PI);
Khatib, O. (PI);
Khayms, V. (PI);
Kitanidis, P. (PI);
Kosovichev, A. (PI);
Kumar, S. (PI);
Lai, T. (PI);
Langley, P. (PI);
Lele, S. (PI);
Leskovec, J. (PI);
Levitt, M. (PI);
Lew, A. (PI);
Liu, T. (PI);
Marsden, A. (PI);
Moerner, W. (PI);
Moin, P. (PI);
Motwani, R. (PI);
Murray, W. (PI);
Papanicolaou, G. (PI);
Pinsky, P. (PI);
Pitsch, H. (PI);
Rajaratnam, B. (PI);
Re, C. (PI);
Reed, E. (PI);
Roughgarden, T. (PI);
Saberi, A. (PI);
Salisbury, J. (PI);
Saunders, M. (PI);
Shaqfeh, E. (PI);
Taylor, C. (PI);
Taylor, J. (PI);
Tchelepi, H. (PI);
Wein, L. (PI);
Wong, W. (PI);
Xing, L. (PI);
Ye, Y. (PI);
Ying, L. (PI)