The Master of Science in Scientific Computing
Notice to Fall 2015 MS applicants: The department has started to review the applications. Please note that this process can take several weeks. We cannot guarantee a decision date; you will be contacted by the Graduate School when an admission decision has been made on your application. If you have any questions and concerns regarding your application, please contact the Math Department at admissions@math.nyu.edu.
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 fulltime and parttime
students, with most courses meeting in the evening. The
program is selfcontained 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
recentlycreated 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 highlevel language (e.g., Java, C, C++, Fortran. Python) as well as data structures, equivalent to a firstyear 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 April 1st for the fall semester. The program admits students both on a fulltime and on a parttime 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
eMail: admissions@math.nyu.edu
eMail: 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:
 33 points of course credit (11 courses) comprised of
 4 core courses (12 points) in mathematics
 4 core courses (12 points) in computer science
 3 elective courses (9 points)
 3 points of credit from a master's capstone project.
Core Courses
The following are the two required core courses
in mathematics:
 MATHGA 2010 Numerical Methods I (fall semester)
 MATHGA 2020 Numerical Methods II (spring semester)
 MATHGA 2701 Methods of Applied Mathematics (fall semester)
 MATHGA2490 Partial Differential Equations I (fall)
 MATHGA 2702 Fluid Dynamics (fall semester)
 MATHGA2962 Mathematical Statistics (if offered)
 MATHGA2704 Applied Stochastic Analysis (spring semester)
 DSGA1002 Statistical and Mathematical Methods
 MATHGA Advanced Topics: Optimization
 MATHGA Advanced Topics: Optimization and Data Analysis
 MATHGA Advanced Topics: Monte Carlo
 MATHGA Advanced Topics: Computational Fluid Dynamics
 MATHGA Advanced Topics: Finite Element Methods
The following are the two required core courses in computer science:
 CSCIGA 1170 Fundamental Algorithms (fall, spring and summer terms)
 CSCIGA 2110 Programming Languages (fall, spring, and summer terms)
 CSCIGA 3033 Open Source Tools (fall term)
 CSCIGA 2270 Computer Graphics (spring term)
 CSCIGA 2565 Machine Learning (fall term)
 CSCIGA.2566 Foundations of Machine Learning
 DSGA1001 Introduction to Data Science (fall)
 DSGA1003 Machine Learning and Computational Statistics (spring)
 DSGA1004: Big Data (spring)
 CSCIGA Graphics Processing Units (GPUs)
 CSCIGA Advanced Topics: HighPerformance Computing
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: MATHGA 2962 Mathematical Statistics (spring semester),
 DSGA1001 Introduction to Data Science (fall), or
 DSGA1002 Statistical and Mathematical Methods

and either
 MATHGA 2043 Scientific Computing (fall and spring semesters)

or both
 MATHGA
2010 Numerical Methods I (fall semester) and
 MATHGA 2020 Numerical Methods II (spring semester)
The following are the three required core courses in computer science for the concentration in data science:
 CSCIGA 1170 Fundamental Algorithms (fall, spring and summer terms)
 CSCIGA 3033 Open Source Tools (fall term)
 CSCIGA 2565 Machine Learning (fall term), or
 DSGA1003 Machine Learning and Computational Statistics (spring)
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 realworld 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:
 MATHGA.2011/CSCIGA.2945 Advanced topics: Data Science
 CSCIGA Advanced Topics: HighPerformance Computing
 DSGA1006 Capstone Project in Data Science
 CSCIGA Advanced Computer Graphics
 CSCIGA Multicore Processors: Architecture &Programming
 CSCIGA Software Engineering
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 highperformance computing center with both sharedmemory and distributedmemory 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, illposed problems,
scientific computing.
Aleksandar Donev.
B.S. 2001, Michigan State; Ph.D. 2006, Princeton. Research
interests: multiscale methods, fluctuating hydrodynamics,
coarsegrained 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, CarnegieMellon. 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, computerhuman 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: largescale 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 HolmesCerfon, B.S. 2005 University of British Columbia, PhD 2010 NYU. Research interests: softmatter 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, multiscale 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:
 Students must maintain an average of B or better over their first twelve credits. Students who fail to achieve this cannot continue in the master's program.
 Students cannot obtain a master's degree unless they have maintained an overall average of B or better. Students at risk of failing to meet this requirement receive a warning letter from the department.
 Students cannot obtain more than four nocredit grades, withdrawals, or unresolved incomplete grades during their academic tenure, and no more than two such grades in the first six courses for which they have registered.
Tamar Arnon
arnon@cims.nyu.edu
Tel. 212 9983257
For further academic information please contact
Aleksandar Donev, Director of the Master's Program in Scientific Computing
donev@courant.nyu.edu
Revised summer 2013