Research Areas

Algorithms & Theory

Three key issues for an algorithm are: Is it correct? How efficient is it? Can one do better? Our strong and diverse group seeks provable answers to these questions. It focuses on problems and questions in the following areas: complexity theory, cryptography, computational geometry, computational algebra, randomness (in algorithm design and average case analysis) and algorithmic game theory.

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Computational Biology

Computational Biology uses mathematical, statistical and algorithmic techniques to solve problems arising in biological research. The faculty working in this area collaborate with biologists and applied mathematicians on a broad range of problems in genomics, proteomics, molecular modeling, systems biology. The CS Department, along with a number of other departments and schools (Biology, Chemistry, Mathematics, Neuroscience, Sackler Institute of Biomedical Sciences, Mt. Sinai School of Medicine) participates in the interdisciplinary Computational Biology Program.

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  • Bioinformatics and Genomes

Formal Methods & Verification

The long-term goal of the formal methods group is to increase the reliability of hardware and software systems by providing tools and techniques for the analysis of these systems. In formal analysis, a mathematical model of a system is developed, which can then be used to prove properties of the system or to discover bugs in the system when the proof fails. The activities and interests of the formal methods group cover a broad spectrum, from the study of mathematical foundations in programming languages and logic, to the implementation of verification tools and the application of these tools for proving the correctness of computer systems.

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Graphics, Vision & User Interfaces

Researchers in Computer Graphics work on computational and mathematical techniques for creating and manipulating computer representations of real and virtual objects and making images of such objects. The main directions of computer graphics research at NYU include animation, geometric modeling, physically-based simulation and computational photography.

The area of Computer Vision is concerned with algorithms and theory necessary to extract information from visual data (images, video, range scans, stereo images, 3D MRI and CAT scan data etc). There is a growing overlap between computer vision and graphics research, as the data acquired from images and video is increasingly used in computer graphics applications.

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Machine Learning

Machine learning is concerned with developing of mathematical foundations and algorithm design needed for computers to learn, that is, to adapt their responses based on information extracted from data. For example, learning algorithms may allow a robot to navigate an unknown environment, improving its performance as it acquires more and more data, or a voice-controlled system to improve its recognition of a person's speech after analysis of a sufficient number of samples. Machine learning techniques draw on many fundamental areas from statistics to theoretical computer science, and are used in a broad variety applications: robotics, speech analysis, health care, finance, computer games, handwriting recognition to name just a few.

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Natural Language, Speech Processing, & Knowledge Representation

The amount of text which is available in electronic form is growing at an explosive rate. In addition to the web, large quantities of text are being collected for medical, legal, commercial, and scientific applications. But the tools for getting the information we need out of this text are still quite primitive. Our research groups in natural language processing are building systems to to extract specific information from large text collections, and to present it in the user's preferred language. A closely related area, speech processing, deals with coding, synthesis and extraction of information from speech signals.

Natural language processing has a long history at NYU. The Linguistic String Project was one of the pioneers in natural language processing research in the United States. The Proteus Project focuses on automatically learning the linguistic knowledge needed for information extraction and machine translation. It has developed extraction systems in English and Japanese, and a series of language-independent translation models. It also conducts a wide range of basic research, and develops large-scale dictionaries and other resources for natural language processing.

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Networks, Operating & Distributed Systems

Systems and networking research explores how to structure the basic software running on individual computers and how to coordinate between different computers. Significant challenges include how to support increasing numbers of processors in modern computer systems, leverage the many embedded and mobile computing devices, and build services that scale to a global audience.

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Scientific Computing

Scientific Computing has a long tradition at the Courant Institute, which was founded by Richard Courant at the dawn of the computer era. Computers were invented in the late 1940's and early 1950's for exactly one purpose: solving hard scientific and engineering problems which required too much numerical computation to do by hand. Now, virtually all branches of science and engineering rely heavily on computing. Several areas of scientific computing, especially linear algebra and optimization, are important in data science, machine learning and physics-based graphics. Many faculty at Courant, both in the Computer Science and Mathematics Departments, have strong interests in Scientific Computing, both in specific application areas and in general techniques and analysis that have broad applicability.

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