MASSACHUSETTS
INSTITUTE OF TECHNOLOGY
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Graduate study in computer science at MIT is centered in Area II of the Department of Electrical Engineering and Computer Science. This memorandum offers a brief road map of the Computer Science graduate program in EECS. This memorandum applies to students entering the Ph.D. program in September 1999 or later. This includes students who completed their M. Eng. degrees before September 1999 but did not begin the rest of their PhD program before September 1999.
Academic
programs for graduate students in the field of computer science lead to the
Master of Engineering, Master of Science, Engineer, and either Doctor of
Philosophy or Doctor of Science
degree. These programs are meant to
prepare students for industrial, educational, governmental, and research
positions. Either the Master of Science
degree or the Master of Engineering degree (or an equivalent) is required for
the Doctoral degree programs.
A thesis based on original work is required for each of the degrees
in computer science. For detailed information on degree requirements
consult the Departmental Memoranda
3903 (Master's program) and 3800 (Doctoral
program) The requirements can be briefly summarized as follows:
· Complete
a Technical
Qualifying Examination (TQE) and a Research Qualifying Examination
(RQE); see below for details.
· Complete
the requirements for a Master's degree. · Complete a minor program consisting of two subjects
approved by the student's Doctoral Committee. The minor area should
be distinct from the student's major area of study. · Complete
any additional subjects (up to two) required by the Doctoral Committee. · Carry out a teaching assistantship as approved by the Doctoral Committee. · Write and present a thesis proposal to the Thesis Committee. · Complete a doctoral thesis. Computer
science is a rapidly evolving field, and much of its knowledge and discipline
is best acquired by direct involvement in research. Active research apprenticeship at an early
stage is regarded as a vital part of the graduate program of every student, and
early affiliation with an appropriate research group is important. For a list of faculty and research staff that
supervise graduate research see Section 5. 2. ExaminationsAs part of the Doctoral program, every student must complete two formal
examinations. The Technical Qualifying Examination (TQE) requires students to demonstrate competence in three different groups. Students should complete all components of the TQE by the end of their second year in residence. See below for more details. The objective of the Research Qualifying Examination (RQE) is to monitor students' research progress as well as skills in presentation, both written and oral. Students should aim to complete the RQE by then end of their second year in residence. See below for more details. For more information on examinations, refer to Departmental memorandum 3800 on the Doctoral program and to the memoranda 3805 Technical Qualifying Examination and Research Qualifying Examination To complete a Ph.D a student must also complete
a minor requirement. 2.1 The TQE in Area IIThe
TQE requires that a student demonstrate competence in four advanced subjects,
selecting at least one subject from each of the three groups (see Table
below). Competence in each subject can be
demonstrated by earning at least an A- grade.
If a student gets two or more grades less than A-, an oral examination
will be required on all subjects for which the grade is less than A-. Each subject grade less than a B- also
requires an oral examination in that subject.
*6.839
may be chosen as a second subject in Artificial Intelligence but not as the
only subject in Group III. **Offered as 6.972, FT06 Each student, with the aid of his or her
graduate counselor, should construct a plan for satisfying the TQE requirement.
This plan should be submitted via the Area-II Graduate Student website http://area2.csail.mit.edu/students.
This should be done by registration day of the second
semester. The plan will be communicated
to the EECS Graduate Office by the Area-II office. A student can submit a new
TQE plan until Drop Date of his/her fourth term, through the website. Completion
of the TQE will be judged based on the plan in effect at that time. Each
student should make an effort to complete four TQE courses during her/his first
three semesters. TQE oral exams should be taken after all four subjects have been completed,
usually by the end of the third semester.
However, under the current system, the student can take a fifth class in
his/her fourth semester to satisfy the TQE requirement, if the grades obtained
in the first four do not satisfy the requirement. 2.2 The RQE in Area IIThe RQE is normally taken on or near completion of a Master's research project or comparable research experience--- preferably at the end of the third graduate term, and in not later than the end of the fourth graduate term.The Area II Chair appoints a two person RQE Committee. The student will provide the committee, two
weeks prior to the exam, a conference-style (less than 20 double-spaced pages)
paper based on original research by the student (usually the SM or MEng thesis). The
RQE Committee conducts an oral examination in which the student is asked to
present his/her research and to defend it in discussion. See Memo 3806 for more details. The request for an RQE should be made through the Area-II Graduate Student website,
http://area2.csail.mit.edu/students.
The Area-II chair
assigns the RQE committee, and the results of the exam will be communicated to
the EECS Graduate Office by the Area-II office. EECS
Memorandum
3807 describes the Minor requirement. A student can file a
proposal for his/her Minor through the Area-II Graduate Student website at http://area2.csail.mit.edu/students
after discussing his Minor with his/her
Graduate Counselor. The Area-II chair
and the student's Graduate Counselor need to approve the Minor proposal. 3. Graduate Subjects The
EECS Department offers a variety of graduate subjects in computer science and
related disciplines.The graduate
subjects in computer science offered by the EECS Department are organized into
three (overlapping) concentration areas: Systems 6.263 Data communications Networks 6.371
Introduction to VLSI Systems 6.375 TQE Complex Digital Systems Design 6.821 TQE Programming Languages 6.823 TQE Computer System Architecture 6.824 TQE Distributed Computer Systems Engineering 6.826 Principles of Computer Systems 6.827 TQE Multithreaded Parallelism: Language
and Compilers 6.828 Operating System Engineering 6.829 TQE Computer Networks
6.831
User Interface Design and Implementation 6.837 Computer Graphics 6.846 Parallel Processing: Systems
Architecture and Applications 6.857 Network and Computer Security Theory 6.336 Introduction to Numerical
Algorithms 6.337 Numerical Methods of Applied
Mathematics 6.338 Parallel Scientific Computing 6.840J
TQE
Theory of Computation (grad.version of 6.045) 6.841J Advanced Complexity Theory 6.844 Computability Theory of and with
Scheme 6.850 Geometric Computing 6.852J
TQE
Distributed Algorithms 6.854J
TQE
Advanced Algorithms 6.855J Network Optimization 6.856J
TQE
Randomized Algorithms 6.859 Combinatorial Optimization 6.874 Computational Functional
Genomics 6.875J
TQE
Cryptography and Cryptanalysis 6.876J Advanced Topics in Cryptography Artificial Intelligence 6.345
TQE Automatic Speech Recognition
6.437
TQE Inference and Information
6.438
TQE Algorithms for Estimation and Inference 6.825
TQE
Techniques in Artificial Intelligence 6.833 Human Intelligence Enterprise 6.834 Intelligent Embedded Systems 6.836 Embodied Intelligence 6.838 Advanced Topics in Computer Graphics 6.839
TQE
Advanced Computer Graphics 6.863J
TQE Natural Language and the Computer
Representation of Knowledge 6.864 TQE Natural Language Processing 6.866 TQE Machine Vision (graduate version of 6.801) 6.867
TQE
Machine Learning and Neural Networks 6.868J The Society of Mind 6.869
TQE
Computer Vision Applications 6.870
Advanced Topics in Computer Vision 6.871 Knowledge-Based Applications
Systems 6.872J Medical Computing 6.873 Medical Decision 6.986 TQE Inference and Information Specialized
seminar subjects, often covering advanced research topics, are offered on an
irregular basis under the course numbers 6.891--9. Detailed information is available from
graduate counselors on registration day and/or EECS. Numerous
additional graduate subjects of interest to Area II students are offered in
other departments of MIT such as Architecture (Course 4), Brain and Cognitive
Sciences (Course 9), Linguistics and Philosophy (Course 24), Management (Course
15), and Mathematics (Course 18).
Courses in computer Science taught in the Division of Applied Science at
Harvard University are also available through cross-registration. 4. Research in Computer SciencePerhaps
the most important facet of graduate education in Area II is involvement in
original research. The primary laboratory concerned with computer science research is
the Computer Science and Artificial Intelligence Laboratory (CSAIL).
The Laboratory for Computer Science and the Artificial Intelligence
Laboratory merged on July 1, 2003 to form CSAIL. Over 750 personnel,
including approximately 85 faculty and research supervising staff and
over 300 graduate students, are affiliated with CSAIL. In addition, there are several
research groups in the Laboratory for Information and Decision
Systems, the Research Laboratory of Electronics, and the Media
Laboratory, which make extensive and sophisticated use of computers
and digital technology in their work. To facilitate involvement in research, entering students are urged to
associate as soon as possible with a research group within a laboratory. This association is readily changed if a
student's interests change. Summaries of computer science research in Area II can be found in
the CSAIL website http://csail.mit.edu. 4. Deadlines and Typical TimelineGetting Started During your first term at MIT, get a sense of the research going on across the Department and its associated Laboratories. Try to meet faculty, research scientists, and other students, and find out about what they are doing. Get started early with the process of identifying a suitable research group (and research advisor!). This is not a formal requirement so early in your graduate career, but we strongly recommend that you do it anyway. There are certain more "structured" requirements that every student must complete on the way to PhD, such as the TQE, RQE, minor, etc. It's important not to lose sight of getting started on research even while attending to these more structured elements. TQE
1) Students must file a TQE form by registration day of the second
term. Master's Proposal and Thesis
1) During the first two terms, students register for 6.991 (Research
Assistant). RQE
Students typically, but not exclusively, choose to focus their
RQE on their Master's research topic. Doctoral Thesis Proposal and Formation of Doctoral Committee The thesis proposal, including a designation of the Doctoral committee, must be submitted by the end of the eighth term (registration day of ninth term). Doctoral Thesis Committee Meeting(s) Although it is not (yet) a formal part of the Doctoral program, we strongly recommend that you convene regular, say annual, meetings of your thesis committee, starting about a year after submission of your proposal. These meetings will serve both to make sure that your committee members agree on the scope of the thesis, and to provide timely course corrections to you about the direction and/or scope of your thesis work. Thesis committee meetings, held regularly or even occasionally, will tend to reduce the surprise level of everyone involved at defense time. Teaching Assistantship All PhD students must complete at least one term of a Teaching Assistantship during the doctoral program. The timing with which students satisfy the TA requirement varies widely; some students TA in their first year; some around the time of or just after their RQE; and some not until nearly the time of their Defense. Many students TA more than once, either for funding reasons, because they are very good at TAing and enjoy it, or both. Students who entered the Doctoral program from the MIT MEng program can satisfy the TA requirement with a TAship that was performed during the MEng year. Minor Program
1) The minor application is typically filed after
completion of the RQE and formation of the Doctoral
Committee, but it can be submitted earlier. Thesis Examination and Public Thesis Defense
1) The thesis examination (by the student's Doctoral
Committee) should be held during the first
half of the student's final term. Signed Thesis The signed thesis is due approximately two weeks before the end of any regular term, and one week before the end of the summer session.
6. Computer Science Faculty and Research StaffIn
the list below, Roman numerals indicate area affiliations of EECS faculty. Affiliations of other faculty and staff are
abbreviated as follows: Department of Mathematics (Math), Department of Brain
and Cognitive Sciences (B&CS), Department of Architecture (Arch),
Mechanical Engineering (ME), Computer
Science and Artificial Intelligence Laboratory (CSAIL). Aaronson, S. (II)
Computational complexity theory, quantum computing, foundations of
quantum mechanics, bounded rationality Adelson, E. (B&CS, CSAIL) Human and machine
vision, including mid-level vision, material perception, image statistics, and motion analysis Abelson, H. (II) Artificial intelligence, scientific computation,
educational computing, societal and legal frameworks for information
technology. Agarwal, A.(II, III) Computer architecture and software systems, design of scalable multiprocessor systems, VLSI processors, compilation and runtime technologies for parallel processing. (On leave Fall 2006) Amarasinghe, S. (II) Program analysis and optimization, computer
architecture. Arvind (II)
Architecture synthesis and verification, term rewriting systems and Lambda
calculus. Parallel architectures and
programming languages. Asanovic, K. (II) Computer
architecture, VLSI design, energy-efficient computing, parallel computing and
embedded systems. (On leave Spring 2006 leave) Balakrishnan, H. (II) Computer
networks, mobile and sensor computing systems, distributed
systems. (On leave Fall 2006) Barzilay, R. (II) Natural Language Processing. Berger, B. (CSAIL/Math)
Algorithms, Computational Biology, Randomness, Parallel Computation. Berwick, R. C. (II, B&CS) Natural
language processing: computer models of language acquisition and parsing.
Computational biology and evolutionary theory.
Artificial Intelligence: formal models of learning, including inductive
inference and computational complexity analysis of language. Cognitive science: word learning, semantics of natural
languages, speech. Braida, L. (VII, II, I)
Development of aids for the deaf based on signal processing and automatic
speech recognition. Computational models
of hearing impairment and speech intelligibility. Brooks, R. A. (II, III) Humanoid
robotics. Artificial life. Chan, V. (I, II, IV) Optical,
wireless and space communications and networks.
Architecture, technology, system designs, and testbed
implementations. New technology,
architectures and applications. Chandrakasan, A. (III, II, V) Energy efficient implementation of digital integrated circuits for systems such as distributed wireless microsensors and portable multimedia devices, the development of protocols and algorithms for wireless communication, and design methodologies for emerging technologies. (On leave Fall 2006) Clark, D. D. (CSAIL) Computer
networks: Internet engineering; hardware
and protocols for high speed large scale network communications. Real-time services over networks. Network-host interfacing. Policy and economic issues; pricing. Collins, M. (II ) Natural Language
Processing: emphasis on statistical or machine learning approaches. Darrell, T. (II) Computer vision,
machine learning, and computer graphics, especially in their application to
problems of human-computer interface. Davis, R. (II) Artificial intelligence, knowledge based
systems, natural interaction, sketch understanding; intellectual property
issues in software. Demaine, E. (II) Algorithms and data structures. Discrete and computational geometry. Combinatorial games. Dennis, J. (CSAIL) Computer system
design to support functional languages and advanced environments for modular
programming.Study of architecture,
performance and reliability issues.(Emeritus) Devadas, S. (II, III) Computer
architecture. Computer security.
Electronic Design Automation. Doyle, J. (CSAIL) Artificial
intelligence and rational psychology.
Theories and architectures for reasoning, knowledge representation, and
decision making. Relations to
philosophy, economics, and physics. Applications
to medicine. Durand, F. (II) Image generation
and creation; realistic rendering, real-time graphics, perceptually-based
algorithms, non-photorealistic rendering, image-based rendering and editing. Edelman, A. (CSAIL/Math)
Scientific Computing, High Performance Architectures, Numerical Analysis,
Numerical Linear Algebra, Random Matrices. Ernst, M. (II) Software engineering, programmer productivity tools, reverse engineering, program understanding, programming environments, compilation, program analysis, optimization, programming language design, formal methods, dynamic analysis, machine learning. (On leave Fall 2006) Freeman, W. (II) Machine learning
applied to computer vision, computer graphics, and image processing. Bayesian models of visual perception;
example-based image synthesis; belief propagation. Gallager, R. G. (I, II) Wireless communication, information theory, all
optical networks, data networks.
(Emeritus) Gifford, D. K (II) Biological
computing. Computer systems. Glass, J. (CSAIL) Automatic speech recognition, synthesis, and
understanding for multi-modal, conversational interaction Goemans, M. (Math, CSAIL) Combinatorial optimization: theory,
applications, design and analysis of algorithms, polyhedral combinatorics. Goldwasser, S. (II) Cryptography, pseudo randomness, property testing, computational number. (On leave Fall 2006 and Spring 2007) Golland, P. (II) Developing novel techniques for image analysis and understanding. Object localization and recognition, shape modeling and representation, statistical analysis, medical imaging. (On leave Fall 2006) Grimson, E. (II) Computer vision, image databases, medical image
processing, image guided surgery, activity recognition, scene reconstruction. Guttag, J. V.(II) Medical software, wireless networking. Hanson, C.(CSAIL) VLSI mixed-signal
design. Radio communications. Signal processing. Horn, B. K. P.(II) Machine vision, diaphanography.
Representation of objects and space.
Photogrammetry, analog networks, computing
images. Indyk, P. (II) Computational geometry, especially in
high-dimensional spaces; databases and information retrieval; learning theory;
design and analysis of algorithms. Jaakkola, T. (II) Statistical inference and machine learning.
Applications to computational biology and information retrieval. Artificial intelligence. Jackson, D. (II) Software design
and specification; design methods, tools and analysis; dependability;
safety-critical systems; reverse engineering; static analysis, model checking,
programming languages. Kaashoek, F. (II) Computer systems:
operating systems, networking, programming languages, compilers, and
computer architecture for distributed systems, mobile systems and parallel
systems. Kaelbling, L. (II) Integrating learning modules into systems programmed
by humans, algorithms for planning and learning in partially observable
environments, learning complex models from perceptual information. Karger, D. (II) Information retrieval and digital libraries; analysis
of algorithms, especially for graphs and optimization problems; applications of
randomization; parallel algorithms. Katabi, D. (II) Computer networks, data communication. Kellis, M. (II) Computational Biology. Genome interpretation, comparative genomics, regulatory networks, cellular signals, developmental biology, evolutionary theory. Algorithms and machine learning applications in genomics. Kelner, J. (CSAIL/Math) Knight, T. F.(CSAIL) Computer
architectures and programming languages for artificial intelligence
applications, image and auditory perception.
Physics of computation. High
speed digital design. Lampson, B. W.(II) Computer
science.Hardware design and machine
architecture through distributed systems and programming languages to user
interfaces and office automation. Larson, R. C.(I, II) Applying advanced
technologies to education in both the ``brick-and-mortar'' and virtual campus.
Probability methods applied to services industries. Leighton, F. T. L.(CSAIL/Math) Internet Algorithms. Parallel algorithms and architectures.
Probabilistic analysis of algorithms.
Combinatorial methods.
Fault-Tolerance in networks. Leiserson, C. E. (II) Theory of computing machinery, parallel computation,
graph theory, algorithms, computer architecture, supercomputing,
multithreading. (On leave Fall 2006 and Spring 2007) Liskov, B. H. (II) Programming methodology,
programming languages, distributed systems, object-oriented
databases. Long, W.(CSAIL ) Application
of artificial intelligence techniques to medical decision making,
particularly in cardiology. Effective
use of physiologic modeling, probabilistic networks, and machine learning. Web based tools to help patients manage their
health in the home. Lozano-Perez, T. (II) Artificial intelligence. Computational chemistry and biology. Robotics and computer vision. Lynch, N. A. (II) Theoretical
aspects of distributed computing.
Distributed algorithm and system design, impossibility results. Semantics, formal modeling, verification, and
performance analysis. Languages
and tools for abstract distributed programming.
Hybrid (continuous/discrete) systems. Madden, S(II) Systems-oriented
database research; focus on adaptive database systems and data processing in
the context of sensor networks. Magnanti, T. (I, II) Network design.
Network equilibrium. Large-scale
optimization. Optimization in
telecommunications, manufacturing, logistics, and transportation. Margolus, N. H. (CSAIL) Highly parallel architectures, spatial-lattice
computers and computations, physical modeling, physics of computation,
reversible computation, quantum computation. Megretski, A. (I, II) Theory and algorithms of analysis and design of
hybrid systems, nonlinear and robust control, non-convex and convex
optimization, formalization of knowledge in education, functional analysis and
operator theory. Meyer, A. R.(II) Software
education environments. Semantics of
programming languages, logic+ of programs, concurrent programs, Lambda
calculus. Micali, S. (II) Cryptography, secure protocols, and computational
complexity theory. Miller, R. (II) Human-computer
interfaces, intelligent interfaces, programming by demonstration, end-user
programming languages, usability, software engineering. Minsky, M. L. (II) Artificial intelligence. Robotics and machine
vision. Representation of knowledge and
structure of personality. Common sense reasoning, theories of emotion and
consciousness. (Emeritus) Mitter, S. K. (I, II) Theory of stochastic dynamical systems, nonlinear
filtering, stochastic and adaptive control.
Mathematical physics and its relationship to systems theory. Image analysis and computer vision. Structure, function and organization of
complex systems. Morris, R. T.(II) The design of an
easy to control data networking infrastructure designed to bring about a new
level of flexibility to network configuration.
The Resilient Overlay Networks Project.
Grid routing protocols. (On leave Fall 2006 and Spring 2007) Moses, J. (II) Organization of
large complex systems, software production, knowledge based systems, and
symbolic manipulation. Penfield, Jr., P. L. (III, II, V)
Information and entropy. Poggio, T.(B&CS, CSAIL) Statistical Learning: theory, algorithms
and applications. Computer and Human Vision. Popovic, J. (II) Geometric modeling, the design of shapes; computer animation, the design of motion. Computer graphics, human-computer interaction, biomechanics, robotics, and design. (On leave Fall 2006) Rinard, M. (II) Program
analysis, transformation, instrumentation, and compilation techniques, with an
emphasis on applying these techniques to object-oriented, real-time,
distributed, and parallel systems. (On leave Spring 2007) Rivest, R. L. (II) Cryptography. Computer/Network Security. Algorithms. Rubinfeld, R.(II)Sublinear time algorithms, randomized computation, computational complexity theory. Rudolph, L. (CSAIL) Rus, D. (II) Robotics, Mobile Computing, Sensor Networks,
Information Access. Saltzer, J. H. (II) Computer systems and computer networks. (Emeritus.) Sarpeshkar, R. (III, I, VII, II ) Analog VLSI for adaptive sensory and
neural systems including audition, vision, and micromechanical systems. Hybrid (analog-digital) spike-based VLSI
computation:low-power analog-to-digital
conversion, digital arithmetic and sequence recognition. Analog VLSI for bionic applications,
especially speech processors for the deaf. Seneff, S. (CSAIL) Spoken Conversational
Systems, spoken language understanding and generation, genomics. Shrobe, H. (CSAIL) Artificial intelligence; The Intelligent Room; Information
Survivability; Self-Adaptive Software. Sipser, M. (CSAIL/Math) Computational complexity theory,
probabilistic methods, analysis of algorithms, mathematical logic. Sollins, K. R. (CSAIL) Pervasive systems and networks, information
systems and infrastructure, naming, and security Stonebraker, M.(CSAIL) Data base management
systems, stream processing systems, data warehouses. Sudan, M. (II) Complexity of
finding 'approximate' solutions to combinatorial optimization problems;
interplay of algebra with computer science and coding theory. Sussman, G. J. (II) Artificial intelligence: basic research on learning,
problem solving and programming.
Computational performance models for intelligent behavior, especially modelling the behavior of engineers.Numerical models of physical systems. Szolovits, P. (II) Application of artificial intelligence techniques to
medical decision making. Effective
representation of knowledge. Personal health information systems, medical
confidentiality. Tedrake, R. (II) Machine learning and robotics, including reinforcement learning, optimal control, legged robots, nonlinear control theory, biological motor control, and computational neuroscience. Particular emphasis on solving difficult robotic control problems through a close coupling of mechanical design and learning control. (On leave Fall 2006) Teller, S. (II) Large-scale machine vision; robotic mapping and fine-grained localization; sensor networks; representation of and interaction with complex georeferenced datasets; applied computational geometry; sensing and perception for autonomous driving. Terman, C. (CSAIL) Computer and DSP architectures; VLSI circuits;
design methodologies and CAD tools; circuit simulation; computer languages. Tidor, B. (II, VII) Computational Biology and Chemistry, Protein and
Systems Modeling, Molecular Biophysics, Rational Drug Design, Electrostatic
Optimization. Antonio Torralba(II) Troxel, D. E. (III, II) Applications of digital systems. Tsitsiklis, J. N. (I, II) Analysis, optimization and algorithms for deterministic and stochastic systems. Resource allocation in dynamic environments. Communication networks. (On leave Fall 2006) Vempala, S. (CSAIL/Math) Algorithms. Randomness, Geometry, Combinatorics. Information retrieval. Ward, S. A.(II) Computer
architecture and operating systems. White, J. K.(III, II, I)
Simulation and optimization techniques for design problems in the fields of
integrated circuit interconnect and packaging, micromachined
devices (MEMS), and biodevices (BIOMEMS). Winston, P. H.(II) Artificial
intelligence. Role of vision and
language in computational explanation of human intelligence. Wroclawski, J. (CSAIL) Distributed systems. High performance network
protocols. Upper layer network architecture.
Graphics. Zue, V. W. (II, VII) Human-human and human-machine communication
using spoken and written languages.
Audio/visual cue integration.
Detection and rendering of paralinguistic information. Acoustic-phonetic analysis of
speech and strategies for lexical access.. |