Computer Science

A list of the research interests of all the Computer Science professors at MIT:

  • Computational complexity, quantum computing.
  • Artificial intelligence, scientific computation, educational computing, societal and legal frameworks for information technology.
  • Program analysis and optimization, computer architecture.
  • Architecture synthesis and verification, digital design, term rewriting systems and lambda calculus. Parallel architectures and programming languages.
  • Computer networks, mobile and sensor computing systems, distributed systems.
  • Natural language processing.
  • Natural language processing: computer models of language acquisition and parsing. Computational biology and evolutionary theory including evolution of language. Artificial intelligence: formal models of learning, including inductive inference and computational complexity analysis of language. Cognitive science: word learning, semantics of natural languages.
  • Natural language processing and machine learning.
  • Theory of computation. The interdisciplinary fields of algorithmic game theory, computational biology, social networks and applied probability.
  • Artificial intelligence, intelligent multimodal interfaces and natural interaction; intellectual property issues in software.
  • Algorithms and data structures. Discrete and computational geometry. Combinatorial games.
  • Parallel computer system design to support functional languages and advanced environments for modular programming. Study of architecture, performance and reliability issues.
  • Image generation and creation; realistic rendering, real-time graphics, perceptually-based algorithms, non-photorealistic rendering, image-based rendering and editing, digital photography.
  • Machine learning applied to computer vision and computer graphics. Bayesian belief propagation and its generalizations. Bayesian models of visual perception.
  • Computational and systems biology, computational functional genomics. Expression of scientific models in computational form. Machine learning.
  • Cryptography, pseudo randomness, property testing, computational number theory, multi-party computations.
  • Computer vision, medical image analysis, medical image processing, image guided surgery, activity recognition.
  • Computational imaging, machine vision. Representation of objects and space. Photogrammetry.
  • Computational geometry, especially in high-dimensional spaces; databases and information retrieval; learning theory; design and analysis of algorithms; streaming and sketching algorithms.
  • Statistical inference and machine learning. Applications to computational biology and information retrieval. Artificial intelligence.
  • Software design and specification; design methods, tools and analysis; dependability; safety-critical systems; reverse engineering; static analysis, model checking, programming languages.
  • Computer systems: operating systems, networking, programming languages, compilers, and computer architecture for distributed systems, mobile systems and parallel systems.
  • Behavior learning, visually-guided map learning for mobile robots, planning in very large stochastic domains, learning relational models.
  • Information retrieval and digital libraries; analysis of algorithms, especially for graphs and optimization problems; applications of randomization; parallel algorithms.
  • Computer networks and data communication. Congestion control, network measurements, scalability and robustness of communications systems. Differentiated services, internet pricing, routing, content distribution, self-configurable and wireless networks and network security.
  • Computational biology. Genome interpretation, comparative genomics, regulatory networks, cellular signals, developmental biology, evolutionary theory. Algorithms and machine learning applications in genomics.
  • Computer science. Hardware design and machine architecture through distributed systems and programming languages to user interfaces and office automation.
  • Theory of computing machinery, parallel computation, graph theory, algorithms, computer architecture, supercomputing, multithreading, internet computing, scalable systems, chip multiprocessing, multicare systems.
  • Programming methodology, programming languages, distributed systems, object-oriented databases.
  • Artificial intelligence; robotics and computer vision.
  • Theory of distributed and real-time computing: mathematical models, specification, algorithm and system design, performance and fault-tolerance analysis. Distributed data management, communication, synchronization. Languages and tools for abstract distributed programming. Hybrid (continuous/discrete) systems. Mobile wireless networks.
  • Databases and computer systems; query processing, distributed systems, management of streaming data, adaptive data processing, sensor networking.
  • Software education environments. Semantics of programming languages, logic of programs, concurrent programs, lambda calculus.
  • Cryptography, secure protocols, pseudo-random generation, proof systems, zero knowledge, mechanical design.
  • Human-computer interfaces, intelligent interfaces, programming by demonstration, end-user programming languages, usability, software engineering, usability and security.
  • Artificial intelligence. Robotics and machine vision. Representation of knowledge and structure of personality. Common sense reasoning, theories of emotion and consciousness.
  • 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.
  • Organization of large complex systems, artificial intelligence.
  • Program analysis, compilers, distributed computing, software engineering.
  • Cryptography. Computer/network security. Algorithms. Voting technology.
  • Sublinear time algorithms, randomized algorithms, computational complexity theory.
  • Robotics, mobile computing and information access.
  • Programming systems with a focus on software synthesis. Programming tools for parallel and high performance computing.
  • Database systems, query processing, data warehouses, federated databases, data visualization.
  • Complexity of finding ‘approximate’ solutions to combinatorial optimization problems; interplay of algebra with computer science and coding theory.
  • Artificial intelligence: learning, problem solving and programming. Computational performance models for intelligent behavior, especially modeling the behavior of engineers. Numerical models of physical systems.
  • Application of artificial intelligence techniques to medical decision making. Effective representation of knowledge. Personal health information systems, medical confidentiality.
  • Machine learning and robotics, including reinforcement learning, optimal control, legged robots, flapping-wing flight, 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.
  • Autonomous robotics for mobility and manipulation; situational awareness through sensing and inference; location-based infrastructure and applications; assistive technology for health care.
  • 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; visual recognition and classification of places and objects.
  • Computer architecture and operating systems.
  • Artificial intelligence and computational theories of human intelligence, with special emphasis on the roles of language, vision and analogical reasoning.
  • Building practical secure systems. Operating systems, hardware design, networking, and distributing systems. Programming languages and tools, security analysis and verification.

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