printer friendly pageCME 300: First Year Seminar Series
Required for first-year ICME Ph.D. students; recommended for first-year 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
Instructors:
Gerritsen, M. (PI)
CME 306: Numerical Solution of Partial Differential Equations (MATH 226)
Hyperbolic partial differential equations: stability, convergence and qualitative properties; nonlinear hyperbolic equations and systems; combined solution methods from elliptic, parabolic, and hyperbolic problems. Examples include: Burger's equation, Euler equations for compressible flow, Navier-Stokes equations for incompressible flow. Prerequisites:
MATH 220A or
CME 302.
Terms: Spr
| Units: 3
Instructors:
Ying, L. (PI)
CME 308: Stochastic Methods in Engineering (MATH 228)
Review of basic probability; Monte Carlo simulation; state space models and time series; parameter estimation, prediction, and filtering; Markov chains and processes; stochastic control; and stochastic differential equations. Examples from various engineering disciplines. Prerequisites: exposure to probability; background in real variables and analysis.
Terms: Spr
| Units: 3
Instructors:
Papanicolaou, G. (PI)
CME 323: Distributed Algorithms and Optimization
The emergence of large distributed clusters of commodity machines has brought with it a slew of new algorithms and tools. Many fields such as Machine Learning and Optimization have adapted their algorithms to handle such clusters. Topics include distributed algorithms for: Optimization, Numerical Linear Algebra, Machine Learning, Graph analysis, Streaming and online algorithms, and other problems that are challenging to scale on a commodity cluster. Throughout the class, topics will be illustrated with hands-on exercises using the high-speed cluster programming framework, Spark, with computing resources provided by the instructor.
Terms: Spr
| Units: 3
Instructors:
Bosagh Zadeh, R. (PI)
CME 337: Spectral Graph Theory and Algorithmic Applications (MS&E 337)
Brings students to the forefront of a very active area of research. Reviews classic results relating graph expansion and spectra, random walks, random spanning trees, and their electrical network representation. Covers recent progress on graph sparsification, Kadison-Singer problem and approximation algorithms for traveling salesman problems.
Terms: Spr
| Units: 3
Instructors:
Saberi, A. (PI)
CME 338: Large-Scale Numerical Optimization (MS&E 318)
The main algorithms and software for constrained optimization emphasizing the sparse-matrix methods needed for their implementation. Iterative methods for linear equations and least squares. The simplex method. Basis factorization and updates. Interior methods. The reduced-gradient method, augmented Lagrangian methods, and SQP methods. Prerequisites: Basic numerical linear algebra, including LU, QR, and SVD factorizations, and an interest in MATLAB, sparse-matrix methods, and gradient-based algorithms for constrained optimization. Recommended: MS&E 310, 311, 312, 314, or 315;
CME 108, 200, 302, 304, 334, or 335.
Terms: Spr
| Units: 3
Instructors:
Saunders, M. (PI)
;
Sun, Y. (PI)
CME 364B: Convex Optimization II (EE 364B)
Continuation of 364A. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Monotone operators and proximal methods; alternating direction method of multipliers. Exploiting problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Applications in areas such as control, circuit design, signal processing, and communications. Course requirements include project. Prerequisite: 364A.
Terms: Spr
| Units: 3
Instructors:
Duchi, J. (PI)
CME 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CS 371)
Computational approaches to understanding the three-dimensional spatial organization of biological systems and how that organization evolves over time. The course will cover cutting-edge research in both physics-based simulation and computational analysis of experimental data, at scales ranging from individual molecules to entire cells. Prerequisite:
CS 106A or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
Terms: Spr
| Units: 3
CME 390: Curricular Practical Training
May be repeated three times for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
3 times
(up to 3 units total)
Instructors:
Jain, K. (PI)
;
Murray, W. (PI)
CME 399: Special Research Topics in Computational and Mathematical Engineering
Graduate-level research work not related to report, thesis, or dissertation. May be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
6 times
(up to 30 units total)
Instructors:
Boyd, S. (PI)
;
Candes, E. (PI)
;
Carlsson, G. (PI)
;
Darve, E. (PI)
;
Gerritsen, M. (PI)
;
Hastie, T. (PI)
;
Kamvar, S. (PI)
;
Levi, O. (PI)
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