MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Department of Electrical Engineering and Computer Science

GUIDE TO GRADUATE STUDY IN AREA II:
Computer Science

for students who started on or after September, 1999

Fall, 2007

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.

1. Computer Science Programs

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 3800new (Doctoral program), available from the EECS Graduate Office. 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.

· Complete any additional subjects (up to two) required by the Doctoral Committee.

· Carry out a teaching assignment as approved by the Doctoral Committee.

· Write and present a thesis proposal to the Thesis Committee.

· Complete a doctoral thesis.

Several of the requirements require approval of the student's Doctoral Committee. Ideally, this is the student's Thesis Committee, composed of a Ph.D. thesis supervisor and at least two Ph.D. thesis readers. Students are encouraged to form thesis committees as early as they can, preferably by the time of the RQE. If a thesis committee does not exist by the time of the RQE, then the RQE committee (with input from the faculty counselor) will evaluate the student's courses, plans for a minor, and plans for a teaching assignment at the time of the RQE. Also, if a thesis committee does not exist by the term following the RQE, then the department will appoint a temporary doctoral committee.

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. Examinations

As 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 3800new on the Doctoral program and to the memoranda 3805 Technical Qualifying Examination and 3806 Research Qualifying Examination available from the Area II website http://theory.lcs.mit.edu/areaii or the EECS Graduate Office.

2.1 The TQE in Area II

The TQE requires that a student demonstrate competence in four advanced courses, selecting at least one course from each of the three groups (see Table below). Competence in each course 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 courses for which the grade is less than A-. Each course grade less than a B- also requires an oral examination in that course.

GROUP I:  SYSTEMS 6.821 6.824 or 6.829 6.823 or 6.371
GROUP II:  THEORY 6.840 or 6.875 6.852 6.854 or 6.856
GROUP III:  ARTIFICIAL INTELLIGENCE 6.345 or 6.866 6.825 or 6.863 or 6.867 6.839*

*6.839 may be chosen as a second subject in Artificial Intelligence but not as the only subject in Group III.

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 to the EECS Graduate Office (38-444), on form 3805 (at the end of the TQE Memo, 3805). This should be done by registration day of the second semester. Each student should plan to complete TQE courses during their first year, or, with approval of graduate counselor, no later than the end of the third semester.

TQE oral exams should be taken after all four courses have been completed, usually by the end of the third semester.

2.2 The RQE in Area II

The 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.

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 TQE Introduction to VLSI Systems
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 Multithreaded Parallelism: Language and Compilers
6.829 TQE Computer Networks
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
6.852J 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
6.825 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
6.866 TQE Machine Vision (graduate version of 6.801)
6.867 TQE Machine Learning and Neural Networks
6.868J The Society of Mind
6.871 Knowledge-Based Applications Systems
6.872J Medical Computing
6.873 Medical Decision Support
6.878 - Computational Biology: Genomes, Networks, Evolution

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 Science

Perhaps 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.

5. Summary of Deadlines and Typical Timeline

Getting 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.
2) The TQE must be completed by the end of the third term.

Master's Proposal and Thesis

1) During the first two terms, students register for 6.991 (Research Assistant).
2) In the third and fourth term, students must register for 6.THG and submit their masters proposal.
3) The student must file a completed Master's thesis by the end of the fourth term.
(Note: the student must be registered for 6.THG in the term in which the thesis is submitted.
Students may register for 6.THG, and may file a thesis, during the summer as well.)
4) A one-term grace period is generally granted to students who TA, but we urge students to take advantage of this only if needed.

RQE

Students typically, but not exclusively, choose to focus their RQE on their Master's research topic.
1) Students must file a RQE form by Registration day of the fourth term.
2) Students may not schedule an RQE to occur before completion of the TQE requirement.
3) The RQE must be completed by the end of the fourth term.

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 (beginning of ninth term).

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.
2) The minor must be completed by registration day of the ninth term.

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.
2) The public thesis defense should be held during the first half of the final term.
3) These examination and defense are often held concurrently, but need not be. Consult your committee.

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 Staff

Aaronson, S. (II) Computational complexity, quantum computing, foundations of quantum mechanics, bounded rationality.

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.

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.

Balakrishnan, H. (II) Computer networks, mobile and sensor computing systems, distributed systems.

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.

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.

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)

Goemans, M. (Math, CSAIL) Combinatorial optimization: theory, applications, design and analysis of algorithms, polyhedral combinatorics.

Garland, S. J. (CSAIL) Practical applications of formal methods to software design and development. Specifying and reasoning about distributed systems and network protocols. Automated deduction. Software-based signal processing.

Gifford, D. K.(II) Biological computing. Computer systems.

Glass, J. (CSAIL) Automatic speech recognition, synthesis, and understanding for multi-modal, conversational interaction

Goldwasser, S. (II) Cryptography, pseudo randomness, property testing, computational number
theory, multi-party computations.

Golland, P. (II) Developing novel techniques for image analysis and understanding. Object localization and recognition, shape modeling and representation, statistical analysis, medical imaging.

Grimson, W. 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.

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.

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.

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.

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.

Rivest, R. L. (II) Cryptography. Computer/Network Security. Algorithms.

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

Spielman, D. (CSAIL/Math) Analysis and Design of Algorithms, Error-Correcting Codes, Complexity Theory.

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.

Teller, S. (II) Computer graphics; photorealistic image synthesis; real-time visual simulation; reconstruction of geometric models from instrumented imagery; interaction with complex geometric datasets; applied computational geometry.

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.

Torralba, A. (II) Computer vision, machine learning and human perception; development of computer vision systems and solving real world recognition tasks; modeling human perceptual and cognitive capabilities; object recognition, classification of whole scenes; visiual recognition and classification of places and objects.

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.

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.

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