Program Overview and Curriculum

Master’s Degree in Computation for Design and Optimization (CDO)

The CDO program is designed with a common core that serves all engineering disciplines, and an elective component that focuses on particular applications. Students must complete coursework distributed as described below (F = course offered in Fall semester; S = course offered in Spring semester).



Core Subjects (3 courses / 36 units)*

Students are required to take three of four core subjects designed to provide foundation materials needed for the study of more advanced elective topics. The core subjects are chosen from the following:

Restricted Electives (2 courses / 24 units)*

Students choose two graduate-level electives from a list of specialized subjects that have computational themes and related components, and that are aligned with the program’s educational mission. The following is a current list of the eligible elective subjects (subjects may be added to the list over time):

  • 1.124J/2.091J Software and Computation for Simulation (F; not offered 2017-18)
  • 1.125J Architecting & Engineering Software Systems (F)
  • 1.204 Computer Modeling: From Human Mobility to Transportation Networks (S)
  • 1.545 Atomistic Modeling & Simulations of Materials & Structures (F; not offered 2017-18)
  • 1.723 Computational Methods for Flow in Porous Media (S)
  • 2.089J/1.128J Computational Geometry (S)
  • 2.093 Finite Element Analysis of Solids and Fluids I (F; not offered 2017-18)
  • 2.094 Finite Element Analysis of Solids and Fluids II (S)
  • 2.098 Introduction to Finite Element Methods for Partial Differential Equations (S)
  • 2.29 Numerical Fluid Mechanics (S)
  • 2.37 Fundamentals of Nanoengineering (S)
  • 3.320 Atomistic Computer Modeling of Materials (S; not offered 2018-19)
  • 4.450 Computational Structural Design and Optimization (F; not offered 2017-18)
  • 6.231 Dynamic Programming and Stochastic Control (S)
  • 6.251J/15.081J Introduction to Mathematical Programming (F)
  • 6.252J/15.084J Nonlinear Optimization (S)
  • 6.256 Algebraic Techniques and Semidefinite Optimization (S; not offered 2018-19)
  • 6.581J/20.482J Foundations of Algorithms and Computational Techniques in Systems Biology (S; not offered 2017-18)
  • 6.673 Introduction to Numerical Simulation in Electrical Engineering (S; not offered 2016 forward)
  • 6.860J / 9.520J Statistical Learning Theory and Applications (F)
  • 6.867 Machine Learning (F)
  • 10.34 Numerical Methods in Chemical Engineering (F)
  • 10.557 Mixed-integer and Nonconvex Optimization (S)
  • 10.637J / 5.698J Quantum Chemical Simulation (F)
  • 12.515 Data and Models (F)
  • 12.521 Computational Geophysical Modeling (S; not offered 2017-18)
  • 12.620 Classical Mechanics:  A Computational Approach (F; not offered 2017-18)
  • 12.714 Computational Data Analysis (S; not offered 2018-19)
  • 15.062J/IDS.145J Data Mining: Finding the Data and Models that Create Value (S; second half of term, Sloan bidding process required)
  • 15.070J/6.265J Advanced Stochastic Processes (S)
  • 15.074 Predictive Data Analytics and Statistical Modeling (S)
  • 15.077J/IDS.211J Statistical Learning and Data Mining (S; Cannot be used if taken Fall 2015 or after & credit also received for 6.867)
  • 15.082 Network Optimization (F; not offered 2016 forward)
  • 15.083J/6.859J Integer Programming and Combinatorial Optimization (S; Sloan bidding process required; not offered 2018-19)
  • 15.764/1.271J Theory of Operations Management (S)
  • 16.110 Flight Vehicle Aerodynamics (F)
  • 16.225J/2.099J Computational Mechanics of Materials (F; not offered 2017-18)
  • 16.413  Principles of Autonomy and Decision Making (F)
  • 16.888 Multidisciplinary System Design Optimization (S; not offered 2018-19)
  • 16.930 Advanced Topics in Numerical Methods for Partial Differential Equations (S; not offered 2017-18)
  • 16.940 Numerical Methods for Stochastic Modeling & Inference (F; not offered in 2017-18)
  • 18.0851 Computational Science and Engineering I (F, S)
  • 18.0861 Computational Science and Engineering II (S)
  • 18.336J/6.335J Fast Methods for Partial Differential and Integral Equations (F)
  • 18.337J/6.338J Numerical Compting and Interactive Software [formerly Parallel Computing] (F)
  • 18.369 Mathematical Methods in Nanophotonics (S; not offered in 2018-19)
  • 22.107 Computational Nuclear Science and Engineering (S; not offered 2017-18)
  • 22.15 Essential Numerical Methods (F; first half of term)
  • 22.212 Nuclear Reactor Anaysis II (F)
  • 22.213 Nuclear Reactor Physics III (S; not offererd 2017-18)
  • 22.315 Applied Computational Fluid Dynamics and Heat Transfer (S; not offererd 2018-19)

Unrestricted Elective (1 course / 12 units)*

Students may choose any graduate-level 12-unit subject from the MIT Subject Listing and Schedule.

Thesis (36 units)

Students write a master’s thesis under the supervision of a faculty advisor.

*Courses that can be repeated for credit cannot be used to satisfy multiple CDO requirements.

English Language Proficiency

All CDO students are required to take the Graduate Writing Exam run by the MIT Program in Writing and Humanistic Studies.  Students who do not receive a passing score are required to take 21W.794 Graduate Technical Writing Workshop.  Students may choose to take the workshop with P/D/F grading (rather than an A – F letter grade), however they must receive a P grade.


The CDO program is designed so that students who are either self-supported or on fellowship can complete the program in 12-18 months. Students supported by research assistant or teaching assistant funds should allow two years to complete the program.

Academic Performance

CDO students are expected to maintain a cumulative grade point average (GPA) of at least 4.5 (out of 5) during the course of their studies. If a student’s term GPA is at or below 4.0 for two sequential terms, if a student receives an Unsatisfactory (“U”) grade in CDO.THG, or if a grade of C or lower is given in any subject, a warning from the CDO directors will be issued to the student, and the MIT Graduate Academic Performance Group will be alerted.

Please see the Office of the Dean for Graduate Education’s Academic Performance page for more information.