Welcome

Welcome to NYU's Computer Science Department, part of the world-famous Courant Institute of Mathematical Sciences. Our department has considerably expanded over the past few years, adding many outstanding faculty with diverse research interests. We are proud of our strong research and educational connections to other departments and schools at NYU, including the departments of Mathematics, Chemistry, Physics, and Biology; the Center for Neural Science; the Stern School of Business; the Tisch School of the Arts; the Wagner School of Public Service; and the NYU School of Medicine.

Our undergraduate majors and MS students have numerous interesting and well-paying employment opportunities at major corporations in New York City and vicinity. Our PhD graduates are employed in a broad spectrum of academic and industrial research positions.

  News and Highlights  

ACM Distinguished Scientist

Clark Barrett has been named a 2014 Distinguished Scientist by the ACM. Congratulations!

2015 IEEE Donald G. Fink Award

Theodore Rappaport and his students have been awarded the 2015 IEEE Donald G. Fink Award for the outstanding survey, review, or tutorial paper in IEEE publications. Congratulations!

Technical Achievement Award

Claudio Silva has been awarded the 2014 Visualization Technical Achievement Award in recognition of seminal advances in geometric computing. Congratulations!

Silver Professorship

NYU has awarded a Silver Professorship to Patrick Cousot. Congratulations!

Best Paper Award

"Finding Minimum Type Error Sources" by doctoral students Zvonimir Pavlinovic and Tim King and Prof. Thomas Wies has won the Best Paper Award at OOPSLA 2014 (Object Oriented Programming, Systems, Language, and Applications). Congratulations!

NSF CAREER Awards

David Sontag and Thomas Wies have each received an NSF CAREER award for their projects "Exact Algorithms for Learning Latent Structures" and "Abstracting Programs for Automated Debugging". Congratulations!

<<More News>>


Learning and modeling the circuits that operate life: The Bonneau lab aims to learn large biological networks directly from genomics data (genomics =3D very scalable biology experiments). Our recent work, as part of collaborative teams of systems biologists and computational biologists, has recently resulted in genome-wide models that are capable of simulating the functioning of the genome in real time (Bonneau, et. al, 2006, Cell). Dr. Bonneau's lab develops new algorithms that attempt to learn the regulatory networks (their topology and dynamical parameters) that are at the core of biological systems. This work was featured in a 2008 Discover Article, where Dr. Bonneau was selected as one of the top 20 scientists under 40. This work is collaborative work that relies on NYU's local expertise in Machine Learning, Modeling complex systems and their dynamics, and Genomics.


With Ph.D. student Eugene Weinstein and Google researcher Pedro Moreno, Mehryar Mohri is working on audio fingerprinting techniques that enable computers to recognize songs. This work represents songs in terms of "music phonemes", elementary units of music sound that are learned from data, and uses weighted finite-state transducers to construct a compact and efficient index of a large database of songs. The image depicts an example of such a transducer. As a result, songs can be recognized quickly and accurately when only a recording of a short "audio snippet" is available and even when the recording is distorted. The group has created a working system with a database of 15,000 songs. Moreover, it has proven new bounds on the size of the indexing finite automata used that guarantee the compactness of this representation as the number of songs indexed increases and suggests that their techniques scale to much larger song data sets.

Links: Example


The NYU Movement Group (http://movement.nyu.edu) conducts research on human motion analysis and synthesis. The group was recently awarded $1,472,000 from the Office of Naval Research for a 3-year project to study human motion styles. This new project, called GreenDot, investigates vision and machine learning techniques in order to detect human body language in video footage. The goal of the project is to train a computer to recognize a person based on his or her motions, and to identify the person's emotional state, cultural background, and other attributes. The project's current focus is analyzing the body language of national and international public figures.





  Events  

Check the Colloquia for more scheduled talks.

Check the CIMS Weekly Bulletin for more events.



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