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  

Sam Marateck

Prof. Sam Marateck passed away on January 14, after an illness of several months.

Sam taught for decades in the Computer Science department. He was a particularly well-loved teacher, deeply engaged with his many students. 

He will be fondly remembered and sadly missed. 

Link

ACM Fellow

Dennis Shasha has been named a Fellow of the ACM. Congratulations! Link

NYU, Facebook to collaborate in new research lab for artificial intelligence and machine learning.

Facebook will be creating a new research lab focused on artificial intelligence and machine learning, with a branch at Astor Place, and with close ties to the Center for Data Science and to the Computer Science Department. Yann LeCun has been appointed as founding director of the lab.

Link: https://www.facebook.com/yann.lecun/posts/10151728212367143

Longuet-Higgins Prize

Rob Fergus has won the IEEE CVPR (Computer Vision and Pattern Recognition) Longuet-Higgins prize for his 2003 paper "Object Class Recognition by Unsupervised Scale-Invariant Learning" with Pietro Perona and Andrew Zisserman. The prize is awarded for "fundamental contributions in computer vision." Link

New initiative in data-intensive science and discovery

New York University has launched a new multi-million dollar collaboration, with Berkeley and U. Washington, to enable university researchers to harness the full potential of the data-rich world that characterizes all fields of science and discovery. At NYU, the initiative will be led by Yann LeCun. This partnership will spur collaborations within and across the three campuses and other partners pursuing similar data-intensive science goals.

The new five-year, $37.8 million initiative, with support from the Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation, was announced today November 12, at a meeting sponsored by the White House Office of Science and Technology Policy (OSTP)

A televised annoucement from the White House Office of Science and Technology Policy can be viewed at http://live.science360.gov tomorrow November 13 at 2:00 PM. Link

Is Code the Most Important Language?

Professor Evan Korth discusses coding and programming languages in an episode of the PBS video series Off Book. 

Link

<<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), under the direction of Chris Bregler, 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  

January 27, 2014
11:30AM Warren Weaver hall 1302
On Bitcoin and Red Balloons
Moshe Babaioff

February 07, 2014
11:30AM Warren Weaver Hall 1302
TBA 2-7
Doug James

February 10, 2014
11:30AM Warren Weaver Hall 1302
TBA 2-10
Zheng Zhang

February 19, 2014
11:30AM Warren Weaver Hall 1302
TBA 2-19
TBA

Check the Colloquia for more scheduled talks.

Check the CIMS Weekly Bulletin for more events.



top | contact webmaster@cs.nyu.edu