Courant Institute New York University FAS CAS GSAS

The Master of Science in Scientific Computing


The Program

The departments of mathematics and computer science at NYU's Courant Institute of Mathematical Sciences offer a master's degree in scientific computing. The program provides broad yet rigorous training in areas of mathematics and computer science related to scientific computing. It aims to prepare people with the right talents and background for a technical career doing practical computing.

The program accommodates both full-time and part-time students, with most courses meeting in the evening. The program is self-contained and terminal, providing a complete set of skills in a field where the need is greater than the supply. The masters program focuses on computational science, which includes modeling and numerical simulation as used in engineering design, development, and optimization.

During the academic years of 2012 and 2013 a concentration in data sciences existed within the scientific computing program; this concentration has been discontinued as of 2014. Incoming students interested in data sciences should consider the recently-created Masters of Science in Data Science within the NYU Center for Data Science.

Starting Fall of 2014 the modified the program requirements and guidelines listed below will apply to all incoming students. The new list of required/approved courses includes the previous list but gives additional flexibility for students to tailor the list of courses to their background and interests. Students presently enrolled in the Modeling and Simulation track can choose to complete the program either under the new or the old requirements. Students enrolled in the Data Science concentration should consult the expanded course options and modified requirements below since this increases flexibility while maintaining consistency with the previous requirements. These students should contact Professor Esteban Tabak for help in deciding on classes to take.


Scientific Computing: Overview

Scientific computing is an indispensable part of almost all scientific investigation and technological development at universities, government laboratories, and within the private sector. Typically a scientific computing team consists of several people trained in some branch of mathematics, science, statistics, or engineering. What is often lacking is expertise in modern computing tools such as visualization, modern programming paradigms, and high performance computing. The master's program in scientific computing aims to satisfy these needs, without omitting basic training in numerical analysis and computer science. Many graduates of this program work at technologically advanced institutions, especially in research and development, where their skills and experience complement those without interdisciplinary degrees. The program is also open to students who will go on to pursue doctoral studies in computer science, mathematics, or statistics.

The master's program in scientific computing focuses on the mathematics and computer science related to advanced computer modeling and simulation. The program is similar in structure to terminal master's programs in engineering, combining classroom training with practical experience. The coursework ranges from foundational mathematics and fundamental algorithms to such practical topics as data visualization and software tools. Electives encourage the exploration of specific application areas such as mathematical and statistical finance, applications of machine learning, fluid mechanics, finite element methods, and biomedical modeling. The program culminates in a master's project, which serves to integrate the classroom material.


Admission Requirements

The program requires least three semesters of Calculus (including multivariate calculus), as well as linear algebra. Experience with programming in a high-level language (e.g., Java, C, C++, Fortran. Python) as well as data structures, equivalent to a first-year sequence in computer science,  is also required. It is highly desirable that applicants have undergraduate major or significant experience in mathematics, a quantitative science or engineering, or economics.

The deadline for application to the program is March 15th for the fall semester. The program admits students both on a full-time and on a part-time basis. The application process takes place online via the Graduate School of Arts and Sciences; please visit the Graduate School Admissions site.

For more information, please contact us at

Office of Admissions and Student Affairs
Department of Mathematics
Courant Institute of Mathematical Sciences
251 Mercer Street
New York, NY 10012-1185

Tel. (212) 998-3238
Fax (212) 995-4121

e-Mail: admissions@math.nyu.edu

e-Mail: arnon@cims.nyu.edu

web page: http://www.math.nyu.edu


Degree Requirements

A candidate for a master's degree in scientific computing must accrue the following:

Students with exceptional backgrounds may petition the program director for permission to substitute other appropriate courses for core courses. Advanced students may be permitted to do a masters thesis as an alternative to the master's capstone project.

Core Courses

The following are the two required core courses in mathematics:

Students must also complete at least two of the following core courses in mathematics: Advanced students may also choose the two additional core courses from the following advanced topics courses in applied math and numerical analysis, typically offered bi-anually:

The following are the two required core courses in computer science:

Students must also complete at least two of the following core courses in computer science:
Advanced students may also choose the two additional core courses from advanced topics courses in computer science of relevance to Scientific Computing, such as:
The departments of mathematics and computer science publish annual brochures describing all courses offered each year. Students should consult these lists of course offerings to determine the availability of desired courses.

Concentration in Data Science

This section is meant only for students presently enrolled in the Data Science concentration; this concentration is no longer offered. To graduate, students enrolled in the old concentration are required to take the following core courses in mathematics for the concentration in data science:         One of

The following are the three required core courses in computer science for the concentration in data science:

and one of Students in the concentration in data science must complete 33 points points of course credit (11 courses), including core and elective courses. They must also obtain 3 points from a master's capstone project.

The Capstone Project

The master's program culminates in a capstone project. The capstone project course is usually taken during the final year of study. During the project, students go through the entire process of solving a real-world problem, from collecting and processing data to designing and fully implementing a solution. The problems and data sets come from settings identical to those encountered in industry, academia, or government.

The following is a list of courses approved to meet the capstone requirement:

Advanced students can obtain permission from the director of the program to do an individual capstone project under the supervision of a faculty member.


Computing Facilities

The Courant Institute makes available for graduate training and coursework a network of workstations maintained by systems administrators. All graduate students have computer accounts for the duration of their studies. NYU also runs a high-performance computing center with both shared-memory and distributed-memory computers.


Faculty

Many members of the departments of mathematics and computer science have research interests bearing on scientific computing. The list includes

Marsha J. Berger. B.S. 1974, Binghamton; M.S. 1978, Ph.D. 1982, Stanford. Research interests: computational fluid dynamics, adaptive mesh refinement, parallel computing.

Yu Chen. B.S. 1982, Tsinghua; M.S. 1988, Ph.D. 1991, Yale. Research Interests: numerical scattering theory, ill-posed problems, scientific computing.

Aleksandar Donev. B.S. 2001, Michigan State; Ph.D. 2006, Princeton. Research interests: multi-scale methods, fluctuating hydrodynamics, coarse-grained particle methods, jamming and packing.

Davi Geiger. B.S. 1980, Pontifica (Brazil); Ph.D. 1990, MIT. Research interests: computer vision, information theory, medical imaging, and neuroscience.

Jonathan B. Goodman. B.S. 1977, MIT; Ph.D. 1982, Stanford. Research interests: numerical analysis, fluid dynamics, computational physics, partial differential equations.

Leslie Greengard. B.A. 1979, Wesleyan; M.S. 1987, Yale School of Medicine; Ph.D. 1987, Yale. Research interests: scientific computing, fast algorithms, potential theory.

Yann LeCun. B.S. 1983, ESIEE (Paris); D.E.A. 1984, Ph.D. 1987, Pierre and Marie Curie University (Paris). Research interests: machine learning.

Andrew Majda. B.S. 1970, M.S. 1971, Ph.D. 1973, Stanford. Research interests: modern applied mathematics, atmosphere/ocean science, turbulence, statistical physics.

Bhubaneswar Mishra. B.S. 1980, India Institute of Technology, Kharagpur; M.S. 1982, Ph.D. 1985, Carnegie-Mellon. Research interests: robotics, mathematical and theoretical computer science.

Michael L. Overton. B.S. 1974, British Columbia; M.S. 1977, Ph.D. 1979, Stanford. Research interests: numerical linear algebra, optimization, linear and semidefinite programming.

Kenneth Perlin. B.A. 1979, Harvard; M.S. 1984, Ph.D. 1986, NYU. Research interests: computer graphics, simulation, computer-human interfaces, multimedia.

Charles S. Peskin. B.A. 1968, Harvard; Ph.D. 1972, Yeshiva. Research interests: physiology, fluid dynamics, numerical methods.

Aaditya V. Rangan. B.A. 1999, Dartmouth; Ph.D. 2003, Berkeley. Research interests: large-scale scientific modeling of physical, biological, and neurobiological phenomena.

Tamar Schlick. B.S. 1982, Wayne State; M.S. 1984, Ph.D. 1987, NYU. Research interests: mathematical biology, numerical analysis, computational chemistry.

Michael J. Shelley. B.S. 1981, Colorado; M.S. 1984, Ph.D. 1985, Arizona. Research interests: scientific computation, fluid dynamics, neuroscience.

Eero Simoncelli. B.A. 1984, Harvard; M.S. 1988, Ph.D. 1993, MIT. Research interests: image processing, computational neuroscience, computer vision.

Esteban Tabak. Bach. 1988, Buenos Aires; Ph.D. 1992, MIT. Research interests: fluid dynamics, conservation laws, optimization and data analysis.

Olof B. Widlund. C.E. 1960, Tekn. L. 1964, Technology Institute, Stockholm; Ph.D. 1966, Uppsala. Research interests: numerical analysis, partial differential equations, parallel computing.

Margaret H. Wright. B.S. 1964, M.S. 1965, Ph.D. 1976, Stanford. Research interests: mathematical optimization, numerical methods, nonlinear programming.

Denis Zorin. B.S. 1991, Moscow Institute of Physics and Technology; M.S. 1993, Ohio State; Ph.D. 1997, Caltech. Research interests: computer graphics, geometric modeling, subdivision surfaces, multiresolution surface representations, perceptually based methods for computer graphics.

Miranda Holmes-Cerfon, B.S. 2005 University of British Columbia, PhD 2010 NYU. Research interests: soft-matter physics, fluid dynamics, oceanography, stochastic methods.

Antoine Cerfon, B.S. 2003, M.S. 2005 Ecole des Mines de Paris, PhD 2010 MIT. Research interests: Computational plasma physics, multi-scale methods, fast algorithms.

Dimitris GIannakis, MSci 2001 Cambridge, PhD 2009 Chicago. Research interests: geometrical data analysis, statistical modeling, climate dynamics.


Academic Standards

To register for courses, students must maintain good academic standing, fulfilling the following requirements:

Up to two core courses taken elsewhere can earn transfer credit, subject to the normal NYU graduate school restrictions on transfer of credit and the approval of the program director. At least 30 credits must be taken at NYU. For further administrative information please contact

            Tamar Arnon
            arnon@cims.nyu.edu
            Tel. 212 998-3257

For further academic information please contact

            Aleksandar Donev, Director of the Master's Program in Scientific Computing
            Donev@cims.nyu.edu




Revised summer 2013