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CSME M.S. Program

Program Structure

As of January 2022, the CSME M.S. Program is no longer accepting new students. Thank you for your interest.

The structure of the CSME M.S. Program is described completely on this webpage. It is based on the CSME M.S. Proposal Document, which can be found on the CSME Resources webpage. However, as the program periodically evolves to address new developments, the information on this webpage should be viewed as the most accurate and current information about the CSME M.S. Program.

M.S. Program Overview

The M.S. component of the CSME Graduate Program at UCSD is a stand-alone program resulting in an M.S. degree in Computational Science. The stand-alone M.S. program is appropriate for scientists and engineers who would like some specialized training in computational science at the level of a Master’s degree but who are not interested in pursuing a doctoral degree. The M.S. students who graduate from the CSME Program gain both a solid theoretical foundation and practical experience in solving real scientific problems using the latest mathematical algorithms, computer software, and computer hardware. CSME M.S. Graduates are attractive to industry and government organizations that increasingly require expertise in computational science.

M.S. Program Admission and Undergraduate Preparation

Prospective students must apply directly to the CSME M.S. Program; there is no affiliated department as there is in the case of the CSME Ph.D. Program. For more information about the mechanics of the CSME M.S. (and Ph.D.) admission process see the CSME Application and Admission webpage.

As of January 2022, the CSME M.S. Program is no longer accepting new students. 

Students applying to the CSME Masters Program must present official evidence of a receipt of a baccalaureate degree from an accredited institution of higher education or the equivalent, with training comparable to that provided by the University of California. A minimum scholastic average of B or better is required for course work completed in upper-division, or prior graduate study. In addition, students are required to have completed two years of calculus through Ordinary Differential Equations and Linear Algebra. Applicants must demonstrate advanced undergraduate-level proficiency in numerical analysis and in computer algorithms and data structures (see Basic Proficiency).

M.S. Program Structure and Requirements 

(For requirements before 2020, see here)

The M.S. Program in CSME is designed to be a 2-year program centered around lecture and laboratory courses which focus on obtaining mastery of the primary tools used in computational science. Extracurricular training is an important component of the program with an expectation of team based laboratory projects on relevant topics from computational science. All students must successfully complete requirements in:

  • Coursework
  • Qualifying Exams
  • Basic Proficiency

Note: Students are typically able to complete the requirements for the CSME M.S. degree within four or five quarters of admission inot the program. 

Required Coursework

Students must successfully complete a total of 38 units of required courses as listed below. Each course, with the exception of the Research and Seminar Courses of item 5 below, must be taken for a letter grade and completed with a grade of B- or better. When a letter grade is not possible, with the approval from the CSME Excecutive Committee, the course may be taken for S/U and completed with a Statisfactory grade. Required Courses:

 

  1. (8 units) Two quarter-long courses from the following LIST A:
  • MATH 270ABC: Numerical Analysis
  • MATH 271ABC: Numerical Optimization
  • MATH 272ABC: Numerical Partial Differential Equations
  • MATH 274: Numerical Methods
  • MATH 275: Numerical PDE
  1. (4 units) PHYS 244: Parallel Computing
  2. (16 units) Four additional quarter-long courses from LIST A or the following LIST B:
  • MATH 202A: Applied Algebra
  • MATH 210ABC: Mathematical Methods in Physics and Engineering
  • MATH 212AB: Introduction to Mathematical Biology
  • MATH 214: Introduction to Computational Stochastics
  • MATH 231ABC: Partial Differential Equations
  • MATH 245ABC: Convex Analysis and Optimization
  • MATH 261ABC: Probabilistic Combinatorics and Algorithms
  • MATH 273ABC: Advanced Techniques in Computational Mathematics
  • MATH 276: Numerical Analysis in Multiscale Biology
  • MATH 280ABC: Probability Theory
  • MATH 281ABC: Mathematical Statistics
  • MATH 282AB: Applied Statistics
  • MATH 283: Statistical Methods in Bioinformatics
  • MATH 284: Survival Analysis
  • MATH 285: Stochastic Processes
  • MATH 286: Stochastic Differential Equations
  • MATH 287ABCD: Time Series Analysis
  • MATH 289C: Exploratory Data Analysis and Inference
  • MATH 294: The Mathematics of Finance
  • Other mathematics courses as approved by the CSME Executive Committee
  1. (8 units) Two quarter-long courses from the following LIST C:
  • BGGN 260: Neurodynamics
  • COGS 225: Image Recognition
  • COGS 283: Big Visual Data Processing
  • CHEM 285: Quantum Chemistry Lab
  • CHEM 286: Molecular Simulations Lab
  • CSE 202: Algorithm Design and Analysis
  • CSE 210: Principles of Software Engineering
  • CSE 224: Graduate Networked Systems
  • CSE 232: Principles of Database Systems
  • CSE 250ABC: Principles of Artificial Intelligence
  • CSE 252ABC: Computer Vision
  • CSE 253: Neural Networks for Pattern Recognition
  • CSE 254: Statistical Learning
  • CSE 255: Data Mining and Predictive Analysis
  • CSE 258: Recommender Systems and Web Mining
  • CSE 276ABCDE: Robotics
  • CSE 280A: Algorithms in Computational Biology
  • ECE 225AB: Probability and Statistics for Data Science
  • ECE 227: Big Network Data
  • ECE 228: Machine Learning for Physical Applications
  • ECE 251ABC: Digital Signal Processing
  • ECE 271ABC: Statistical Learning
  • ECE 272AB: Stochastic Processes in Dynamic Systems
  • MAE 209: Continuum Mechanics Applied to Medicine/Biology
  • MAE 210ABC: Fluid Mechanics
  • MAE 232ABC/SE 276ABC: Finite Element Methods in Solid Mechanics
  • MAE 260: Fundamentals and Applications of Computational Materials Science
  • MAE 261: Cardiovascular Fluid Mechanics
  • MAE 280AB: Linear Systems Theory
  • MAE 294ABC/SIO 203ABC: Introduction to Applied Mathematics
  • PHYS 203AB: Advanced Classical Electrodynamics
  • PHYS 216: Fluid Dynamics
  • PHYS 219: Condensed Matter/Materials Science Lab
  • PHYS 221A: Nonlinear and Nonequilibrium Dynamics of Physical Systems
  • PHYS 225AB: General Relativity
  • PHYS 241, 242: Computational Physics
  • PHYS 243: Stochastic Methods
  • PHYS 270AB: Quantitative Biology Lab
  • PHYS 277: Physics of the Cell
  • SE 233: Computational Techniques in Finite Elements
  • SE 276ABC: Finite Element Methods in Solid Mechanics
  • SE 279: Meshfree Methods for Linear and Nonlinear Mechanics
  • Other science courses as approved by the CSME Executive Committee
  1. (2 units) Two total units among Research or Seminar Courses:
  • Research courses (independent study): MATH 299; CSE 298; ECE 298; MAE 296, 298; PHYS 297, 298; COGS 298; or other research courses as approved by the CSME Executive Committee
  • Seminar courses: MATH 218, 278ABC, 288; CSE 259; ECE 294, 295, 296, 297; PHYS 250, 251, 252, 253, 254; or other seminar courses as approved by the CSME Executive Committee

The built-in flexibility in the coursework structure allows for students to design tracks in the direction of their interests. For example, the field of data science is rapidly growing, with daily advances and impact in many industries; CSME Masters students can in essence design their own data science track, supplementing their computational science foundation of physical modeling and numerical analysis with CSE courses in artificial intelligence or ECE courses in statistical learning.

Note:

  • Deviations from the coursework structure, through petition, may be possible but are rarely granted since the structure was built to provide the flexibility needed
  • Full-time students are required to register for a minimum of twelve (12) units every quarter. Eight (8) of these twelve (12) units must be graduate-level CSME Program courses or, with approval from the CSME Executive Committee, other courses in computational science-related subjects, and must be taken for a letter grade

Qualifying Exams

M.S. students must receive a letter grade of B or above in each of three qualifying exam courses, selected to provide a general broad set of tools in computational science. These three courses must consist of:

  1. One graduate-level course with a final exam or final project from LIST A
  2. PHYS 244
  3. One graduate-level course with a final exam or final project from LIST B or LIST C

Note:

  • The final exam or final project in each of the three courses in essence serves as a
  • qualifying exam
  • Courses used to satisfy the qualifying exam requirement can also be used to satisfy
  • coursework requirements

Basic Proficiency

M.S. students must demonstrate advanced undergraduate-level proficiency in numerical methods and in computer algorithms and data structures. This can be satisfied in any of the following ways:

  • Taking UCSD's courses in both subjects
    • Numerical Analysis (MATH 174/274 or MATH 175/275 or MATH 270A)
    • Data Structures and Algorithms (CSE 100 or CSE 101)
  • Having previously taken courses on these subjects from other institutions
  • Demonstrating knowledge on the topics in an oral exam administered by a member of the CSME Executive Committee

Time Limits

Normative Time to Degree: M.S. students are expected to complete their requirements within two years of admission into the program.

Relationship of the CSME M.S. Program with Existing Graduate Programs at UCSD

The CSME M.S. program is a stand-alone program, with no specific departmental affiliation. (The UCSD Mathematics Department functions purely as an administrative unit.) However, we expect that some students from the CSME M.S. Program will decide to continue their education and get a Ph.D. in a department offering a CSME specialization in Computational Science. The procedure for doing this is the same as for changing from any M.S. program to a Ph.D. program at UCSD, and generally involves a Graduate Petition (please contact the individual department involved).