CME 251: The Shape of Data: Geometric and Topological Data Analysis (CS 233)
Mathematical computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces  as well as for other data embedded into geometric spaces. Global and local geometry descriptors allowing for various kinds of invariances. The rudiments of computational topology and persistent homology on sampled spaces. Clustering and other unsupervised techniques. Spectral methods for geometric data analysis. Nonlinear dimensionality reduction. Alignment, matching, and map computation between geometric data sets. Function spaces and functional maps.Networks of data sets and joint analysis for segmentation and labeling. The emergence of abstractions or concepts from data. Prerequisites: discrete algorithms at the level of 161; linear algebra at the level of CM103.
Terms: Spr

Units: 3

Grading: Letter or Credit/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
Instructors:
Friend, A. (PI)
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
Instructors:
Nelson, B. (PI)
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
Instructors:
Dror, R. (PI)
;
Jung, Q. (TA)
CME 285: Computational Modeling in the Cardiovascular System (BIOE 285)
This course introduces computational modeling methods for cardiovascular blood flow and physiology. Topics in this course include analytical and computational methods for solutions of flow in deformable vessels, onedimensional equations of blood flow, cardiovascular anatomy, lumped parameter models, vascular trees, scaling laws, biomechanics of the circulatory system, and 3D patient specific modeling with finite elements; course will provide an overview of the diagnosis and treatment of adult and congenital cardiovascular diseases and review recent research in the literature in a journal club format. Students will use SimVascular software to do clinicallyoriented projects in patient specific blood flow simulations.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Marsden, A. (PI)
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)
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
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