Master of Engineering in Computation and Cognition

The Department of Electrical Engineering and Computer Science and the Department of Brain and Cognitive Sciences offer a joint curriculum leading to a Master of Engineering (MEng) in Computation and Cognition that focuses on the emerging field of computational and engineering approaches to brain science, cognition and machine intelligence.
This program is only open to Computation and Cognition (6-9) majors at MIT and is best suited for students who wish to work towards a master’s degree but do not intend enroll in a doctoral program in a related field.
The curriculum provides flexibility to accommodate students with a wide diversity of interests in this area, from biologically-inspired approaches to artificial intelligence, to reverse engineering circuits in the brain. This joint master’s program prepares students for careers that include advanced applications of artificial intelligence and machine learning, as well as further graduate study in systems and cognitive neuroscience. Students in the program are full members of both departments.
The Master of Engineering in Computation and Cognition is a five- to five-and-a-half-year program in which Course 6-9 students earn a bachelors and master's degree. Students may earn the degree concurrently or sequentially with their undergraduate degree. Students will meet all degree requirements for the 6-9 major and complete an additional 90 units including 24 units of thesis work. The MEng compresses the coursework necessary for a four-year bachelor's and a two-year master's degree into ten or eleven semesters. Students begin fulfilling MEng requirements in their latter semesters as undergraduates.
If you are a 6-9 major and your research program is adequately developed, you will be admitted into the MEng program if you meet these criteria:
- A technical GPA 4.25 or better (letter grades in 6-9 courses and any other courses in 6, 8, or 18)
- Completion of 9 subjects (or 102 units) counting toward the 6-9 major
- An overall GPA of 4.0 is required by the end of the term in which students apply
- One UROP or SuperUROP (The EECS or BCS Office may contact research supervisors for their views on candidates).
- A commitment from a faculty member to supervise their thesis research. Note: Non-MIT faculty such as postdocs, faculty at other universities and Institute research scientists can serve as co-advisors upon approval from the BCS Director of Education, but a MIT faculty advisor will still be needed. Purely engineering thesis projects will not be accepted.
Students in their junior year or first semester of senior year should submit an application in November or April. The application deadline is the last day of classes for that semester according to the MIT Academic Calendar. Decisions are typically sent out 2-3 weeks after the grade deadline as those grades will be considered in the application review process.
MEng Application Website (available in November and April; see application for deadline)
The Master of Engineering degree is awarded only to students who have already received, or who will simultaneously receive, the Bachelor of Science in Computation and Cognition (Course 6-9). Refer to the undergraduate degree chart for requirements.
The graduate component of the MEng program is described below.
Course 6-9P Graduate Requirements
Required Subjects | ||
6.THM | Master of Engineering Program Thesis | 24 |
Restricted Electives | ||
Four graduate subjects totaling at least 42 units, which includes two subjects from the EECS advanced subjects and two from the BCS advanced subjects | 42-48 | |
Two subjects from the list of mathematics restricted electives | 24 | |
Total Units | 90-96 |
EECS Advanced Subjects
6.231 | Dynamic Programming and Reinforcement Learning | 12 |
6.241[J] | Dynamic Systems and Control | 12 |
6.245 | Multivariable Control Systems | 12 |
6.246, 6.247 | Advanced Topics in Control | 12 |
6.248, 6.249 | Advanced Topics in Numerical Methods | 12 |
6.251[J] | Introduction to Mathematical Programming | 12 |
6.252[J] | Nonlinear Optimization | 12 |
6.254 | Game Theory with Engineering Applications | 12 |
6.255[J] | Optimization Methods | 12 |
6.256[J] | Algebraic Techniques and Semidefinite Optimization | 12 |
6.260, 6.261 | Advanced Topics in Communications | 12 |
6.262 | Discrete Stochastic Processes | 12 |
6.263[J] | Data-Communication Networks | 12 |
6.265[J] | Discrete Probability and Stochastic Processes | 12 |
6.267 | Heterogeneous Networks: Architecture, Transport, Proctocols, and Management | 12 |
6.268 | Network Science and Models | 12 |
6.332, 6.333 | Advanced Topics in Circuits | 12 |
6.334 | Power Electronics | 12 |
6.335[J] | Fast Methods for Partial Differential and Integral Equations | 12 |
6.336[J] | Introduction to Numerical Simulation | 12 |
6.337[J] | Introduction to Numerical Methods | 12 |
6.338[J] | Numerical Computing and Interactive Software | 12 |
6.339[J] | Numerical Methods for Partial Differential Equations | 12 |
6.341 | Discrete-Time Signal Processing | 12 |
6.344 | Digital Image Processing | 12 |
6.345[J] | Automatic Speech Recognition | 12 |
6.347, 6.348 | Advanced Topics in Signals and Systems | 12 |
6.374 | Analysis and Design of Digital Integrated Circuits | 12 |
6.375 | Complex Digital Systems Design | 12 |
6.434[J] | Statistics for Engineers and Scientists | 12 |
6.435 | Bayesian Modeling and Inference | 12 |
6.436[J] | Fundamentals of Probability | 12 |
6.437 | Inference and Information | 12 |
6.438 | Algorithms for Inference | 12 |
6.440 | Essential Coding Theory | 12 |
6.441 | Information Theory | 12 |
6.442 | Optical Networks | 12 |
6.443[J] | Quantum Information Science | 12 |
6.450 | Principles of Digital Communication | 12 |
6.452 | Principles of Wireless Communication | 12 |
6.453 | Quantum Optical Communication | 12 |
6.454 | Graduate Seminar in Area I | 6 |
6.456 | Array Processing | 12 |
6.521[J] | Cellular Neurophysiology and Computing 1 | 12 |
6.522[J] | Quantitative Physiology: Organ Transport Systems | 12 |
6.524[J] | Molecular, Cellular, and Tissue Biomechanics | 12 |
6.525[J] | Medical Device Design | 12 |
6.542[J] | Laboratory on the Physiology, Acoustics, and Perception of Speech | 12 |
6.544, 6.545 | Advanced Topics in BioEECS | 12 |
6.552[J] | Signal Processing by the Auditory System: Perception | 12 |
6.555[J] | Biomedical Signal and Image Processing | 12 |
6.556[J] | Data Acquisition and Image Reconstruction in MRI | 12 |
6.561[J] | Fields, Forces, and Flows in Biological Systems | 12 |
6.630 | Electromagnetics | 12 |
6.631 | Optics and Photonics | 12 |
6.632 | Electromagnetic Wave Theory | 12 |
6.637 | Optical Imaging Devices, and Systems | 12 |
6.644, 6.645 | Advanced Topics in Applied Physics | 12 |
6.685 | Electric Machines | 12 |
6.690 | Introduction to Electric Power Systems | 12 |
6.695[J] | Engineering, Economics and Regulation of the Electric Power Sector | 12 |
6.719 | Nanoelectronics | 12 |
6.720[J] | Integrated Microelectronic Devices | 12 |
6.728 | Applied Quantum and Statistical Physics | 12 |
6.730 | Physics for Solid-State Applications | 12 |
6.731 | Semiconductor Optoelectronics: Theory and Design | 12 |
6.732 | Physics of Solids | 12 |
6.735, 6.736 | Advanced Topics in Materials, Devices, and Nanotechnology | 12 |
6.774 | Physics of Microfabrication: Front End Processing | 12 |
6.776 | High Speed Communication Circuits | 12 |
6.777[J] | Design and Fabrication of Microelectromechanical Systems | 12 |
6.780[J] | Control of Manufacturing Processes | 12 |
6.781[J] | Nanostructure Fabrication | 12 |
6.820 | Foundations of Program Analysis | 12 |
6.823 | Computer System Architecture | 12 |
6.824 | Distributed Computer Systems Engineering | 12 |
6.828 | Operating System Engineering | 12 |
6.829 | Computer Networks | 12 |
6.830 | Database Systems | 12 |
6.832 | Underactuated Robotics | 12 |
6.833 | The Human Intelligence Enterprise | 12 |
6.834[J] | Cognitive Robotics | 12 |
6.835 | Intelligent Multimodal User Interfaces | 12 |
6.836 | Multicore Programming | 12 |
6.837 | Computer Graphics | 12 |
6.838 | Shape Analysis | 12 |
6.839 | Advanced Computer Graphics | 12 |
6.840[J] | Theory of Computation | 12 |
6.841[J] | Advanced Complexity Theory | 12 |
6.842 | Randomness and Computation | 12 |
6.845 | Quantum Complexity Theory | 12 |
6.846 | Parallel Computing | 12 |
6.849 | Geometric Folding Algorithms: Linkages, Origami, Polyhedra | 12 |
6.850 | Geometric Computing | 12 |
6.851 | Advanced Data Structures | 12 |
6.852[J] | Distributed Algorithms | 12 |
6.853 | Topics in Algorithmic Game Theory | 12 |
6.854[J] | Advanced Algorithms | 12 |
6.856[J] | Randomized Algorithms | 12 |
6.857 | Network and Computer Security | 12 |
6.858 | Computer Systems Security | 12 |
6.860[J] | Statistical Learning Theory and Applications | 12 |
6.863[J] | Natural Language and the Computer Representation of Knowledge | 12 |
6.864 | Advanced Natural Language Processing | 12 |
6.865 | Advanced Computational Photography | 12 |
6.866 | Machine Vision | 12 |
6.867 | Machine Learning | 12 |
6.869 | Advances in Computer Vision | 12 |
6.870 | Advanced Topics in Computer Vision | 12 |
6.872[J] | Biomedical Computing | 12 |
6.874[J] | Computational Systems Biology: Deep Learning in the Life Sciences | 12 |
6.875[J] | Cryptography and Cryptanalysis | 12 |
6.876 | Advanced Topics in Cryptography | 12 |
6.878[J] | Advanced Computational Biology: Genomes, Networks, Evolution | 12 |
6.881 | Advanced Topics in Artificial Intelligence | 12 |
6.882 | Advanced Topics in Artificial Intelligence | 12 |
6.883 | Advanced Topics in Artificial Intelligence | 12 |
6.884 | Advanced Topics in Artificial Intelligence | 12 |
6.885-6.888 | Advanced Topics in Computer Systems | 12 |
6.889-6.893 | Advanced Topics in Theoretical Computer Science | 12 |
6.894-6.896 | Advanced Topics in Graphics and Human-Computer Interfaces | 12 |
6.935[J] | Financial Market Dynamics and Human Behavior | 9 |
6.945 | Large-scale Symbolic Systems | 12 |
6.946[J] | Classical Mechanics: A Computational Approach | 12 |
1 |
Cannot count as EECS Advanced Subject if undergraduate version is taken as part of the Course 6-9 SB degree. |
BCS Advanced Subjects
9.016[J] | Acoustics, Production and Perception of Speech | 12 |
9.021[J] | Cellular Neurophysiology and Computing 1 | 12 |
9.073[J] | Statistics for Neuroscience Research 2 | 12 |
9.110[J] | Nonlinear Control | 12 |
9.123[J] | Neurotechnology in Action | 12 |
9.181[J] | Developmental Neurobiology 1 | 12 |
9.190 | Computational Psycholinguistics 1 | 12 |
9.272[J] | Topics in Neural Signal Processing | 12 |
9.285[J] | Audition: Neural Mechanisms, Perception and Cognition | 12 |
9.301[J] | Neural Plasticity in Learning and Memory | 9 |
9.34[J] | Biomechanics and Neural Control of Movement | 12 |
9.422[J] | Principles of Neuroengineering | 12 |
9.455[J] | Revolutionary Ventures: How to Invent and Deploy Transformative Technologies | 9 |
9.520[J] | Statistical Learning Theory and Applications 1 | 12 |
9.530 | Emergent Computations Within Distributed Neural Circuits 1 | 12 |
9.583[J] | Functional Magnetic Resonance Imaging: Data Acquisition and Analysis | 12 |
9.601[J] | Language Acquisition I | 9 |
9.660 | Computational Cognitive Science 1 | 12 |
9.822[J] | Psychology and Economics | 12 |
1 |
Cannot count as BCS Advanced Subject if undergraduate version is taken as part of the Course 6-9 SB degree. |
2 |
Subject can count as BCS Advanced Subject or Mathematics Restricted Elective, but not both. |
Mathematics Restricted Electives
Probability and Statistics (maximum of 1) | ||
Introduction to Inference 1 | ||
Introduction to Probability 1 | ||
Fundamentals of Probability | ||
Statistics for Brain and Cognitive Science 1 | ||
Statistics for Neuroscience Research 2 | ||
Topics in Neural Signal Processing 2 | ||
Introduction to Probability and Statistics | ||
Probability and Random Variables | ||
Fundamentals of Statistics | ||
Discrete Mathematics (maximum of 1) | ||
Mathematics for Computer Science 1 | ||
Principles of Discrete Applied Mathematics | ||
Linear Algebra (maximum of 1) | ||
Linear Algebra 1 | ||
Modern Algebra | ||
Complex Variables (maximum of 1) | ||
Complex Variables with Applications | ||
Methods for Scientists and Engineers | ||
Real Analysis (maximum of 1) | ||
Real Analysis | ||
Other Subjects | ||
Statistical Physics I | ||
Computational Science and Engineering I | ||
Computational Science and Engineering II | ||
Introduction to Numerical Analysis | ||
Theory of Numbers |
1 |
Cannot count as Mathematics Restricted Elective if taken as part of the Course 6-9 SB degree. |
2 |
Subject can count as BCS Advanced Subject or Mathematics Restricted Elective, but not both. |
Departmental funding for the MEng program is not guaranteed. However, students may apply for funding from two sources: teaching assistantships and research assistantships. Students will have the opportunity to apply for funding before they begin the MEng program. Full-time TA or RA assistantships pay a monthly stipend, full tuition and health insurance. Students with TAships should expect to work approximately 20 hours a week on teaching. Students may request funding as a research assistant from their thesis supervisor, however, RA support for MEng students can be difficult to secure and advanced planning with research advisors is recommended.
Information regarding teaching assistantships in Course 6 including the application process can be found here. BCS does not typically have teaching assistantships to offer to MEng students.
Students with a full-time TAship or RAship may only register for two 12 unit subjects in addition to 12 units of thesis credit and 12 units of assistantship credit. Students holding a half TAship or half RAship may register for an additional class. Because students receive credit for their thesis work as well as TAships and RAships, they are registered for 48 to 60 units each term.
MEng students are only eligible for RAships and TAships during their first three regular semesters (summers are excepted) as a graduate student. If a student has been a graduate TA at least once or has unusual circumstances that have delayed progress on the thesis or classes, the student may request one additional term (a fourth term) of support eligibility.
For more detailed information regarding the cost of attendance, including specific costs for tuition and fees, books and supplies, housing and food as well as transportation, please visit the SFS website.