Mail Code: 94305-4065

Phone: (650) 723-2620

Email: helnnn@stanford.edu

Web Site: http://stanford.edu/group/mathcompsci

Courses offered by Mathematical and Computational Science program are listed under the subject code MCS on the Stanford Bulletin's ExploreCourses website.

This interdisciplinary undergraduate degree program in MCS is administrated by the departments of Mathematics, Computer Science, and Statistics. It provides a core of mathematics basic to all the mathematical sciences and an introduction to concepts and techniques of computation, optimal decision making, probabilistic modeling, and statistical inference.

Using the faculty and courses of the departments listed above, this major prepares students for graduate study or employment in the mathematical and computational sciences or in those areas of applied mathematics which center around the use of computers and are concerned with the problems of the social and management sciences. A biology option is offered for students interested in applications of mathematics, statistics, and computer science to the biological sciences (bioinformatics, computational biology, statistical genetics, neurosciences); and in a similar spirit, an engineering and statistics option.

## Undergraduate Mission Statement for Mathematical and Computational Science

The mission of the Mathematical and Computational Science Program is to provide students with a core of mathematics basic to all the mathematical sciences and an introduction to concepts and techniques of computation, optimal decision making, probabilistic modeling and statistical inference. The program is interdisciplinary in its focus, and students are required to complete course work in mathematics, computer science, statistics, and management science and engineering. A computational biology track is available for students interested in biomedical applications. The program prepares students for careers in academic, financial and government settings as well as for study in graduate or professional schools.

## Learning Outcomes

The program expects undergraduate majors to be able to demonstrate the following learning outcomes. These learning outcomes are used in evaluating students and the department's undergraduate program. Students are expected to be able to demonstrate:

- understanding of principles and tools of statistics.
- command of optimization and its applications and the ability to analyze and interpret problems from various disciplines.
- an understanding of computer applications emphasizing modern software engineering principles.
- an understanding of multivariate calculus, linear algebra, and algebraic and geometric proofs.

## Bachelor of Science in Mathematical and Computational Science

The requirement for the bachelor's degree, beyond the University's basic requirements, is an approved course program of 78-84 units, distributed as follows:

Units | ||
---|---|---|

Mathematics (MATH) | 28 | |

MATH 41 | Calculus ^{1} | 5 |

MATH 42 | Calculus ^{1} | 5 |

Select one of the following: | 5 | |

Linear Algebra and Differential Calculus of Several Variables | ||

Honors Multivariable Mathematics | ||

Select one of the following: | 5 | |

Integral Calculus of Several Variables | ||

Honors Multivariable Mathematics | ||

Select one of the following: | 5 | |

Ordinary Differential Equations with Linear Algebra | ||

Honors Multivariable Mathematics | ||

Select one of the following: | 3 | |

Applied Matrix Theory | ||

Linear Algebra and Matrix Theory | ||

Computer Science (CS) | 22-24 | |

CS 103 | Mathematical Foundations of Computing | 5 |

CS 106A | Programming Methodology ^{1} | 5 |

and | ||

CS 106B | Programming Abstractions | 5 |

or | ||

CS 106X | Programming Abstractions (Accelerated) | 5 |

Select two of the following: | 7-9 | |

Introduction to Scientific Computing | ||

Computer Organization and Systems | ||

Introduction to Automata and Complexity Theory | ||

Design and Analysis of Algorithms | ||

Computers, Ethics, and Public Policy | ||

Management Science and Engineering (MS&E) | 7-11 | |

Linear and Nonlinear Optimization | ||

Stochastic Modeling | ||

Or select three of the following: | ||

Introduction to Optimization | ||

Introduction to Stochastic Modeling | ||

Linear and Nonlinear Optimization | ||

Stochastic Modeling | ||

Stochastic Control | ||

Statistics (STATS) | 11-12 | |

STATS 116 | Theory of Probability | 5 |

STATS 200 | Introduction to Statistical Inference | 3 |

Select one of the following: | 3 | |

Introduction to Applied Statistics | ||

Introduction to Regression Models and Analysis of Variance |

^{1} | Students who scored a 5 on both the Calculus AB and BC advanced placement exams (total of 10 units) can be waived out of MATH 41 and MATH 42; A score of 4 or 5 in Computer Science A will receive credit for CS106A (5 units). |

### Writing in the Major Requirement

The University requires students to complete at least one approved writing-intensive course in each of their majors. See the Hume Center for Writing and Speaking web site for a full description of the WIM requirement.

Units | ||
---|---|---|

Choose one from the M&CS designated WIM courses to fulfill the Writing in the Major requirement: | 3-4 units | |

Applied Group Theory | ||

Applied Number Theory and Field Theory | ||

Groups and Rings | ||

Fundamental Concepts of Analysis | ||

Computers, Ethics, and Public Policy | ||

Statistical Methods in Computational Genetics |

### Mathematical and Computational Science Electives

Choose three courses in Mathematical and Computational Science 100-level or above, at least 3 units each from two different departments. At least one must be from following list:

Units | ||
---|---|---|

Choose three courses from the following: | 9 units | |

Advanced Topics in Econometrics | ||

Causal Inference and Program Evaluation | ||

Introduction to Financial Economics | ||

Game Theory and Economic Applications | ||

Experimental Economics | ||

The Fourier Transform and Its Applications | ||

Introduction to Linear Dynamical Systems | ||

Introduction to Statistical Signal Processing | ||

Computer Systems Architecture | ||

Convex Optimization I | ||

Convex Optimization II | ||

Probabilistic Analysis | ||

Simulation | ||

Stochastic Control | ||

Mathematics of Sports | ||

Applied Matrix Theory | ||

Functions of a Complex Variable | ||

Introduction to Combinatorics and Its Applications | ||

Linear Algebra and Matrix Theory | ||

Functions of a Real Variable | ||

Complex Analysis | ||

Partial Differential Equations I | ||

Fundamental Concepts of Analysis | ||

Lebesgue Integration and Fourier Analysis | ||

Calculus of Variations | ||

Metalogic (Winte) | ||

Data Mining and Analysis | ||

Applied Multivariate Analysis | ||

Introduction to Time Series Analysis | ||

Introduction to the Bootstrap | ||

Statistical Models in Biology | ||

Introduction to Statistical Learning | ||

Introduction to Stochastic Processes | ||

Introduction to Stochastic Processes | ||

Stochastic Processes | ||

Statistical Methods in Finance | ||

A Course in Bayesian Statistics | ||

For Computer Science (CS), electives can include courses not taken as units under the CS list above and the following: | ||

Introduction to Numerical Methods for Engineering | ||

Software Development for Scientists and Engineers | ||

Numerical Linear Algebra | ||

Object-Oriented Systems Design | ||

Principles of Computer Systems | ||

Operating Systems and Systems Programming | ||

Compilers | ||

Logic and Automated Reasoning | ||

Design and Analysis of Algorithms | ||

Software Project | ||

Artificial Intelligence: Principles and Techniques | ||

Introduction to Robotics | ||

Experimental Robotics | ||

Probabilistic Graphical Models: Principles and Techniques | ||

Machine Learning | ||

Program Analysis and Optimizations | ||

Mining Massive Data Sets | ||

Interactive Computer Graphics | ||

The following courses are not offered this year but may be used by students who completed them in fulfillment of this requirement:CS 164 |

With the adviser's approval, courses other than those offered by the sponsoring departments may be used to fulfill part of the elective requirement. These may be in fields such as biology, economics, electrical engineering, industrial engineering, and medicine, etc., that might be relevant to a mathematical sciences major, depending on a student's interests.

- At least three quarters before graduation, majors must file with their advisers a plan for completing degree requirements.
- All courses used to fulfill major requirements must be taken for a letter grade with the exception of courses offered satisfactory/no credit only.
- The student must have a grade point average (GPA) of 2.0 or better in all course work used to fulfill the major requirement.
- Electives that are not offered this year, but may be offered in subsequent years, are eligible for credit toward the major.

### Mathematical and Computational Science Biology Track (Option)

Students in the Biology track take the introductory courses for the Mathematics and Computational Science major with the following allowable substitutions as electives.

Units | ||
---|---|---|

STATS/BIO 141 | Biostatistics ^{1} | 3-5 |

Take three courses from the Biology Core: | 10 | |

Genetics, Biochemistry, and Molecular Biology | ||

Cell Biology and Animal Physiology | ||

Plant Biology, Evolution, and Ecology | ||

Or take two courses from the core and one of the following: | 3-4 | |

Evolutionary Paleobiology | ||

Evolution | ||

Conservation Biology: A Latin American Perspective | ||

Theoretical Population Genetics | ||

Molecular and Cellular Immunology | ||

Honors students select the following three courses: | 1-4 | |

Statistical Methods in Computational Genetics | ||

Fundamentals of Molecular Evolution | ||

Population Studies | ||

The following courses are not offered this year but may be used by students who completed them in fulfillment of this requirement: BIO102, 160A & 160B |

^{1} | Can replace STATS 191 Introduction to Applied Statistics or STATS 203 Introduction to Regression Models and Analysis of Variance |

### Mathematical and Computational Science Engineering Track (Option)

Students in the Engineering track take the introductory courses for the Mathematics and Computational Sciences major with the following allowable substitutions.

Units | ||
---|---|---|

With consent of an MCS advisor, MATH 51-52-53 series may be substituted for CME 100-102-104. Depending on the exact material taught in relevant years, an additional math course might be necessary ^{**} | ||

Vector Calculus for Engineers | ||

Ordinary Differential Equations for Engineers | ||

Linear Algebra and Partial Differential Equations for Engineers | ||

STATS 116 may be replaced by: | 3-5 | |

Statistical Methods in Engineering and the Physical Sciences | ||

STATS 191/STATS 203 may be replaced by: | 3-4 | |

Data Mining and Analysis | ||

Engineering Track Electives: | ||

Select one of the following: | 3-4 | |

Functions of a Complex Variable | ||

Introduction to Combinatorics and Its Applications | ||

Complex Analysis | ||

Mathematics of Computation | ||

Partial Differential Equations II | ||

Calculus of Variations | ||

Metalogic | ||

Select two of the following: | 3-5 | |

Dynamics | ||

Introduction to Chemical Engineering | ||

Biotechnology | ||

Engineering Thermodynamics | ||

Introductory Electronics | ||

Introduction to Materials Science, Nanotechnology Emphasis | ||

Feedback Control Design |

** | Only M&CS majors pursuing the |

### Mathematical and Computational Science Statistics Track (Option)

Students in the Statistics track take the introductory courses for the Mathematics and Computational Sciences major with the following additional courses - (87 units total)

##### Required:

Units | ||
---|---|---|

STATS 217 | Introduction to Stochastic Processes | 3 |

Advanced CS, such as: | ||

CS 246 | Mining Massive Data Sets | 3-4 |

Advanced MS&E, such as: | ||

MSE 220 | Probabilistic Analysis | 3-4 |

or | ||

Simulation | ||

Statistics Track Electives: | ||

Select three of the following: | 9 | |

Data Mining and Analysis | ||

Applied Multivariate Analysis | ||

Introduction to Time Series Analysis | ||

Introduction to the Bootstrap | ||

Introduction to Statistical Learning | ||

Stochastic Processes | ||

A Course in Bayesian Statistics |

### Honors Program

The honors program is designed to encourage a more intensive study of mathematical sciences than the B.S. program. In addition to meeting all requirements for the B.S., the student must:

- Maintain an average letter grade equivalent to at least a 3.5 in all academic work.
- Complete at least 15 units in mathematical sciences in addition to the requirements for the major listed above. Include in these 15 units at least one of the following:
- An approved upper-level or graduate course
- Participation in a small group seminar
- At least 3 units of directed reading

- Prepare a statement describing major area of concentration for honors work.
- Describe how each course selected added to the student's knowledge and understanding in area chosen for concentration.
- Students interested in honors should consult with their adviser by last quarter of their junior year to prepare their program of study. Honors work may be concentrated in fields such as biological sciences, environment, physics, etc.
- Suggested electives for students pursuing Honors: CME 206, CS 229, CS 248, EE 364, MATH 171, MATH 172, STATS 202, STATS 216, STATS 217.

## Minor in Mathematical and Computational Science

The minor in Mathematical and Computational Science is intended to provide an experience of the four constituent areas: Computer Science, Mathematics, Management Science and Engineering, and Statistics. Five basic courses are required:

Select two of the followning: | 10 | |

Programming Methodology | ||

and | ||

Programming Abstractions | ||

or | ||

Programming Abstractions (Accelerated) | ||

Select one of the following: | 3-5 | |

Linear Algebra and Differential Calculus of Several Variables | ||

or | ||

Applied Matrix Theory | ||

Select one of the following: | 3-4 | |

Linear and Nonlinear Optimization | ||

or | ||

Stochastic Modeling | ||

Select two of the following: | 8 | |

Theory of Probability | ||

and either | ||

Introduction to Applied Statistics | ||

or | ||

Introduction to Statistical Inference |

In addition to the above, the minor requires three courses from the following, two of which must be in different departments:

Units | ||
---|---|---|

Select three of the following: | 9 | |

Introduction to Scientific Computing | ||

Mathematical Foundations of Computing | ||

Computer Organization and Systems | ||

Introduction to Automata and Complexity Theory | ||

Design and Analysis of Algorithms | ||

The Fourier Transform and Its Applications | ||

Game Theory and Economic Applications | ||

Stochastic Control | ||

Applied Matrix Theory | ||

Functions of a Complex Variable | ||

Introduction to Combinatorics and Its Applications | ||

Applied Group Theory | ||

Applied Number Theory and Field Theory | ||

Functions of a Real Variable | ||

Partial Differential Equations I | ||

Fundamental Concepts of Analysis | ||

Calculus of Variations | ||

Metalogic | ||

Introduction to Applied Statistics | ||

Introduction to Statistical Inference | ||

Data Mining and Analysis | ||

Introduction to Regression Models and Analysis of Variance | ||

Introduction to Stochastic Processes |

Other upper-division courses appropriate to the program major may be substituted with consent of the program director. Undergraduate majors in the constituent programs may not count courses in their own departments.

*Co-Directors:* Bradley Efron, Susan Holmes

*Steering Committee:* Takeshi Amemiya (Economics, emeritus), Emmanuel Candes (Mathematics, Statistics), Gunnar Carlsson (Mathematics), Richard Cottle (Management Science and Engineering, emeritus), Bradley Efron (Statistics), Margot Gerritsen (ICME), Peter Glynn (Management Science and Engineering), Susan Holmes (Statistics), Lester Mackey (Statistics), Parviz Moin (Engineering), George Papanicolaou (Mathematics), Eric Roberts (Computer Science), David Rogosa (Education), Tim Roughgarden (Computer Science), Chiara Sabatti (Statistics), Amin Saberi (Management Science and Engineering), David Siegmund (Statistics), Jonathan Taylor (Statistics), Brian White (Mathematics).