The Design and Computation Group inquires into the varied nature and practice of computation in architectural design, and the ways in which design meaning, intentions, and knowledge are constructed through computational thinking, representing, sensing, and making. We focus on the development of innovative computational tools, processes and theories, and the application of these in creative, socially meaningful responses to challenging design problems.
Faculty, research staff, and students work in diverse and mutually supportive areas including: visualization, digital fabrication and construction processes and technologies, shape representation and synthesis, building information modeling (BIM), generative and parametric design, critical studies of digital and information technologies, digital heritage, and software and hardware development of advanced tools for spatial design and analysis. Our aim is to cover the many facets of a rapidly changing and growing area with in-depth, agenda-setting research and teaching.
Our work is informed simultaneously by architectural practice as well as a variety of other disciplinary perspectives including mathematics, computer science, cognitive science, philosophy, anthropology, STS (Science, Technology, and Society), media studies, and art. Students are strongly encouraged to take advantage of the interdisciplinary environment of MIT, and to take subjects and participate in research across different MIT departments to explore and develop their interests. They are expected to acquire both the technical skills and the theoretical and conceptual foundations to rethink and challenge the limits of current design processes and practices, and to consider the social and cultural implications of their positions.
This area of study offers a concentration in the Master of Science in Architecture Studies (SMArchS) program and a doctoral (PhD) program. Please go to the Design and Computation Group's list of Dissertations and Theses to see the work done at the culmination of the degree programs.
PublishedFebruary 1, 2022
Spring 2022 Public Program
MIT’s Department of Architecture is pleased to announce our spring 2022 public program; a continuing conversation on where we are now.NewsArchitecture + UrbanismArt Culture + TechnologyBuilding TechnologyComputationHistory Theory + Criticism
PublishedOctober 27, 2021
Desktop: A Material History of MIT Architecture During a Year ApartThis past year, as we typed away in our respective remote-classrooms, we often wondered what our classmates were making.NewsAga Khan ProgramArchitecture + UrbanismArt Culture + TechnologyBuilding TechnologyComputationHistory Theory + CriticismUndergraduates
PhD in Computation
The PhD program is broadly conceived around computational ideas as they pertain to the description, generation, and construction of architectural form. Issues range from the mathematical foundations of the discipline to the application and extension of advanced computer technology. The mission of the program is to enhance and enrich design from a computational perspective, with clear implications for practice and teaching.
Faculty, research staff, and students work in diverse but overlapping and mutually supportive areas. Work on shape representation, generative and parametric design is directed at a new computational basis for design. Work on digital modeling and rendering seeks to extend the possibilities of visualizing design ideas and un-built work, as well as to improve architectural design practice where designers and technical collaborators are geographically separated. Work on rapid prototyping and CAD/CAM technologies aims to expand design possibilities through the physical modeling of design ideas, and to revolutionize the construction and building phase of architectural practice.
Research employs computational media for the representation and use of design knowledge. Faculty, research staff, and students associated with the group combine education in architecture and urban design with education in computer graphics, art, mathematics, and other fields.
The minimum residency requirement for the PhD degree is two years and it is expected that most students will take no more than five years to complete the degree.
Each student will be assigned a faculty advisor in Computation upon admission. The advisor will consult on the student's initial plan of study and on the choice of subjects in subsequent terms. He or she will assist the student in selecting an advisory committee and subsequently a dissertation committee. Often, but not always, the faculty advisor becomes the dissertation committee chair if the student so desires.
Doctoral Research Opportunity in Computation and Advanced Urbanism
The Norman B. Leventhal Center of Advanced Urbanism and Departments of Architecture and Urban Studies and Planning have established a collaborative doctoral-level concentration in Advanced Urbanism. Urbanism is a rapidly growing field that has many branches. At MIT, we speak of Advanced Urbanism as the field which integrates research on urban design, urbanization and urban culture.
The concentration in Advanced Urbanism seeks doctoral applicants (one to two per year) who have: 1) at least one professional design degree (in architecture, landscape architecture, urban design, etc.); 2) research interests in urbanism that would draw upon both ARCH and DUSP faculty advising; and 3) a commitment to engage with the research community at the LCAU and within their home department throughout their time at MIT. Applicants should apply for admission to an existing ARCH or DUSP PhD program and must meet all specific admissions requirements of the respective PhD program. Admissions committees nominate applicants who fit the urbanism program to a joint advanced urbanism admissions committee. The selected applicants are admitted by their home department discipline group (DUSP; AKPIA, BT, Computation, HTC) with financial support and research assistantships from LCAU.
Prospective students with questions pertaining to the doctoral studies in Advanced Urbanism should reach out to their prospective home doctoral program and to LCAU doctoral committee members: Rafi Segal and Brent Ryan. Or to the mailing list email@example.com. See links at top for program-specific information.
The Master of Science in Architecture Studies (SMArchS) is a two-year program of advanced study founded on research and inquiry in architecture as a discipline and as a practice. The program is intended both for students who already have a professional degree in architecture and those interested in advanced non-professional graduate study.
SMArchS in Computation
The Computation Group inquires into the varied nature and practice of computation in architectural design, and the ways in which design meaning, intention, and knowledge are constructed through sensing, thinking, and making computationally. It focuses on the development of innovative computational tools, processes and theories, and applying these in creative, socially meaningful responses to challenging design problems.
Course 1 - Civil and Environmental Engineering
1.000 | Computer Programming for Scientific and Engineering Applications
Instructor: R. Juanes
1.001 | Engineering Computation & Data Science
Instructor: J. Williams
1.022 | Introduction to Networks Models
Instructor: A. Jadbabaie
Provides an introduction to complex networks, their structure, and function, with examples from engineering, applied mathematics and social sciences. Topics include spectral graph theory, notions of centrality, random graph models,
contagion phenomena, cascades and diffusion, and opinion dynamics.
1.124J | Software and Computation for Simulation
Instructor: Prof. J. Williams
Modern software development techniques and algorithms for engineering computation. Hands-on investigation of computational and software
techniques for simulating engineering systems, such as sensor networks, traffic networks, and discrete simulation of materials using atomistic and particle methods. Covers data structures and algorithms for modeling, analysis, and visualization in the setting of multi-core and distributed computing. Treatment of basic topics, such as queuing, sorting and search algorithms, and more advanced numerical techniques
based on state machines and distributed agents. Foundation for in-depth exploration of image processing, optimization, finite element and particle methods, computational materials, discrete element methods, and network methods. Knowledge of an object-oriented
1.125 | Artitecting and Engineering Software Systems
Instructor: Prof. J. Williams
Software architecting and design of cloud-based software-intensive systems. Targeted at future engineering managers who must understand both the business and technical issues involved in architecting enterprise-scale systems. Student teams confront technically challenging problems. Introduces modern devops concepts and cloud-computing, including cloud orchestration for machine learning. Also discusses cyber-security issues of key management and use of encrypted messaging for distributed ledgers, e.g., blockchain. Students face problem solving in an active learning lab setting, completing in-class exercises and weekly assignments leading to a group project. Some programming experience preferred.
Course 2 - Mechanical Engineering
2.007 | Design & Manufacturing I
Instructor: S. Kim, A. Winter
2.089J | Computational Geometry
2.093 | Finite Element Analysis of Solids & Fluids I
2.739J | Product Design & Development
Course 3 - Materials Science and Engineering
3.032 | Mechanical Behavior of Materials
Instructor: L. Gibson
Course 4 - Architecture
4.s50 | Special Subject: Architectural Computation
4.110 | Design Across Scales, Disciplines and Problem Contexts
4.140J/MAS.863.J | How to Make Almost Anything
Instructor: Neil Gershenfeld
4.246 | DesignX Accelerator
Students work in entrepreneurial teams to advance innovative ideas, products, services, and firms oriented to design and the built environment. Lectures, demonstrations, and presentations are supplemented by workshop time, when teams interact individually with instructors and industry mentors, and by additional networking events and field trips. At the end of the term, teams pitch for support of their venture to outside investors, accelerators, companies, or cities. Limited to 30; preference to students in DesignX Program. The DesignX program provides a ton of really valuable resources if you're interested in starting a company -- frequent mentorship from faculty and industry experts who care about the work you're doing, time and space to turn an idea into a company, funding, workshops, etc. The more you can put in, the more you'll get out. There's an application process in the fall semester.
4.450 | Computational Structure Design & Optimization
Instructor: Caitlin Mueller
4.481 | Building Technology Seminar
Instructor: BT Faculty
Course 6 - Electrical Engineering and Computer Science
6.0001 | Introduction to Computer Science and Programming in Python
Instructor: A. Bell
6.005 6.031 | Elements of Software Construction
Instructor: M. Goldman
6.034 | Artifical Intelligence
Instructor: K. Koile
6.036 | Introduction to Machine Learning
6.041A/6.041B | Introduction to Probability I & II
Instructor: J.N. Tsitsiklis
6.045J | Automata, Computability and Complexity
6.046J | Design & Analysis of Algorithms
Instructor: S. Devadas
6.170 | Software Studio
Instructor: D.N. Jackson
6.431A/6.431B | Intro to Probability I & II
6.801 | Machine Vision
Instructor: B.K.P. Horn
6.803 | The Human Intelligence Enterprise
6.804 | Computational Cognitive Science
Instructor: J. Tenenbaum
6.809J | Interactive Music Systems
Instructor: E.Egozy, L.Kaelbling
6.834J | Cognitive Robotics
6.835 | Intelligent Multimodal User Interfaces
6.837 | Computer Graphics
6.838 | Shape Analysis
Instructor: J. Solomon
The DesignX program provides a ton of really valuable resources if you're interested in starting a company -- frequent mentorship from faculty and industry experts who care about the work you're doing, time and space to turn an idea into a company, funding, workshops, etc. The more you can put in, the more you'll get out. There's an application process in the fall semester.
6.844 | Artifical Intelligence
Instructor: K. Koile
6.849 | Geometric Folding Algorithms: Linkages, Origami, Polyhedra
6.850 | Geometric Computing
6.860J | Statistical Learning Theory & Applications
Instructor: T. Poggio
6.861J | Aspects of Computational Theory of Intelligence
Instructor: T. Poggio
6.862 | Applied Machine Learning
If you want to take this course for graduate credit, be sure to pre-register and fill out the application well in advance of the semester. It fills up fast.
6.863J | Natural Language & the Computer Representation of Knowledge
6.865 | Advanced Computational Photography
6.883 | Modeling with Machine Learning
6.901J | Innovation Engineering: Moving Ideas to Impact
Instructor: F. Murray
Course 9 - Brain and Cognitive Sciences
9.012 | Cognitive Science
Instructor: E. Gibson, P. Sinha, J. Tenebaum
9.19/9.190 | Computational Psycholinguistics
Instructor: R.P. Levy
Introduces computational approaches to natural language processing and acquisition by humans and machines, combining symbolic and probabilistic modeling techniques. Covers models such as n-grams, finite state automata, and context-free and mildly context-sensitive grammars, for analyzing phonology, morphology, syntax, semantics, pragmatics, and larger document structure. Applications range from accurate document classification and sentence parsing by machine to modeling human language acquisition and real-time understanding. Covers both theory and contemporary computational tools and datasets. Students taking graduate version complete additional assignments.
9.10 | Cognitive Neuroscience
Instructor: R. Desimore, E. K. Miller
9.523 | Aspects of Computational Theory of Intelligence
Instructor: Tomaso Poggio
9.660 | Computational Cognitive Science
9.65 | Cognitive Processes
Course 11 - Urban Studies and Planning
11.321 | Data Science and Machine Learning in Real Estate
Small group study of advanced subjects under staff supervision. For graduate students wishing to pursue further study in advanced areas of real estate not covered in regular subjects of instruction. This is a great general introduction to data science and machine learning, and is suitable for both those with and without prior experience. It does a great job of highlighting use cases and challenges of applications in real estate and the built environment that you wouldn't get from a standard ML course, with many guest speakers from industry. The homework and projects were very manageable for beginners.
Course 15 - Management
15.371J | Innovation Teams
Instructor: L. Perez-Breva
15.871 | Introduction to Systems Dynamics
Instructor: Prof. H. Rahmandad
Course 18 - Mathematics
18.06 | Linear Algebra
Instructors: Fall: S. Johnson; Spring: A. Edelman
Reviews linear algebra with applications to life sciences, finance, engineering, and big data. Covers singular value decomposition, weighted least squares, signal and image processing, principal component analysis, covariance and correlation matrices, directed and undirected graphs, matrix factorizations, neural
nets, machine learning, and computations with large matrices. Students in Course 18 must register for the undergraduate version, 18.065.
This class covers a broad range of mathematical tools involving matrices, which are are everywhere in data analysis and machine learning
(including deep neural network technology). Prof. Strang wrote a textbook specifically for this class, which he uses for lectures and homework (the textbook is a great resource for everything that involves matrices — but it is not an introduction to linear algebra). I would recommend this class to both Masters and PhD students, given that you have some understanding of linear algebra. There is weekly homework, math problems done by hand, sometimes including one simple programming exercise. There are no exams or quizzes. There is only one open ended final project. Prof. Strang is one the best and most dedicated Professors I’ve met at MIT, and a really great teacher — you’ll certainly enjoy him delivering a lecture, even if you don’t understand the material."
18.065 | Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
Instructor: Prof. Gilbert Strang
18.0851 | Computational Science & Engineering I
Instructor: Fall: W. Gilbert Stran; Spring: L. Demanet
18.900 | Geometry and Topology in the Plane
Instructor: P. Seidel
Covers selected topics in geometry and topology, which can be visualized in the two-dimensional plane. Polygons and polygonal paths. Billiards. Closed curves and immersed curves. Algebraic curves. Triangulations and complexes. Hyperbolic geometry. Geodesics and curvature. Other topics may be included as time permits.
18.4041 | Theory of Computation
Instructor: Michael Sipser
Course MAS - Media Arts and Sciences
MAS.S66 | Human Machine Symbiosis
Instructor: Pattie Maes
MAS.131 | Computational Camera & Photography
MAS.581 | Networks, Complexity and their Applications
MAS.630 | Affective Computing
Instructor: R.W. Picard
Great course with lots of one-on-one feedback with the instructor and TA. Excellent if you're looking to perform experiments with human subjects since it walks you through the process of getting COUHES approval. The workload can be reasonable and is hugely dependent on how much you choose to tackle in the course project.
MAS.712 | Learning Creative Learning
MAS.834 | Tangible Interfaces
Instructor: H. Ishii
MAS.836 | Sensor Technologies for Responsive Environments
MAS.863.J | How to Make Almost Anything
Instructor: N. Gershenfeld
Course 21 - Humanities
21W.820J | Writing: Science, Technology, and Society
21G.152 | Chinese II
21A.819 | Qualitative Research Methods
COMPSCI 164 | Software Engineering
Instructor: David K. Malan
COMPSCI 171 | Visualization
Instructor: Hanspeter Pfister
APCOMP 209 | A Data Science 1: Introduction to Data Science
Instructor: Pavlos Protopapas and Kevin A. Rader
ENG-SCI 153 | Laboratory Electronics
Instructor: David Abrams
EDU T402 | Team Learning
Instructor: Daniel Wilson
GSD 6338 | Introduction to Computational Design
Instructor: Panagiotis Michalatos
GSD 6317 | Material Distributions: Gradients of Compliance
Instructor: Not Listed
GSD SCI 6317 | Material Practice as Research: Digital Design and Fabrication
Instructor: Not Listed
GSD SCI 6459 | Mechatronic Optics
Instructor: Andrew Witt
Lawrence SassAssociate Professor, Chair of Computation Group
Terry KnightProfessor/Associate Department Head for Strategy & Equity
Takehiko NagakuraAssociate Professor
Skylar TibbitsAssociate Professor, Director of Undergraduate Programs, Assistant Director for Education at the Morningside Academy for Design