Research in Computer Science

A stable, safe, and resilient cyberspace is vital for our economic and societal wellbeing. This concentration helps students learn how to fortify cyber networks, combat threats, and foster “white hat” hacking. Researching systems allows for students to improve real-world systems to make them stronger and securer. This also includes data-driven analysis of privacy and social networks. After graduation, our students often work either in private corporations or in governments.

Labs: OSIRIS, CCS

Sample research projects:

Damon McCoy, one of the department's newest faculty members, researched counterfeit pharmacy affiliate networks. Online sales of counterfeit or unauthorized products drive a robust underground advertising industry that includes email spam, “black hat” search engine optimization, forum abuse and so on. Virtually everyone has encountered enticements to purchase drugs, prescription-free, from an online “Canadian Pharmacy.” However, even though such sites are clearly economically motivated, the shape of the underlying business enterprise is not well understood precisely because it is “underground.”

Learn more about the business of online pharmaceutical affiliate programs

Working in digital forensics and recovery, Nasir Memon, CSE department professor, with a team of Ph.D. students, founded the Digital Assembly company. This company works in products that recover digital photos that are fragmented or deleted.

Learn more about Digital Assembly

Justin Cappos has been developing an open peer-to-peer computing system, Seattle. Nearly 100 students participated in its creation. The purpose of this project is to make cloud computing available to everyone. It's free and provides access to computers worldwide. This project was funded by the National Science Foundation.

Learn more about Seattle Open Peer-to-Peer Computing

People increasingly make important life decisions based on large amounts of data, using online tools. Professor Oded Nov works with students and collaborators to explore how novel computational tools and user interface design informed by social science can help people make sense and reason better about such data and their personal implications. In a series of studies, Nov and his doctoral student Junius Gunaratne showed how design informed by research in psychology and economics can help consumers make informed decisions and outperform users of traditional financial user interfaces. In another series of studies, Nov and his doctoral student Martina Balestra and their collaborators showed how design interventions affect users' understanding of and level of engagement with their personal genomic data. In another line of research, Nov and Professor Maurizio Porfiri study how design interventions help impact the behavior of contributors to citizen science projects.

The organization and governance of large volumes of data. This concentration allows for retaining data obtained from a large number of sources — from a large city, to individuals, and anywhere in between — and ensures a high level of data quality for analytical purposes. The visualization of such data elegantly brings structure and simplicity to it.

Labs: CUSP

Sample research projects:

Enrico Bertini, in conjunction with Ph.D. students Christian Felix Da Silva and Anshul Pandey, developed RevEx. A collaboration with ProPublica, this tool visualizes Yelp data and has the ability to single out reviews under specific parameters or keywords. The tool can sort through millions of reviews at one time and visualize trends.

Learn more about RevEx and download the demo

Guido Gerig, department chair, focuses on clinical neuroimaging to assist in studying such areas as early childhood brain development, children with autism, and infants at risk of schizophrenia. His methodology includes image processing, registration, atlas building, segmentation, shape analysis, and statistical analysis.

In this related paper, Gerig studies the early developing brain by displaying the longitudinal MRI scans of the same subject's brain at various ages, from two weeks to two years.

Learn more about Prof. Gerig's study

Assistant Professor Rumi Chunara works at the intersection of Big Data and Public Health, using information gleaned from social media sites like Facebook and Twitter to predict epidemics, track obesity rates on a local level, and much more.

In Prof. Chunara's research on US obesity rates, for example, Facebook is used to cross-measure user interests and obesity prevalence within certain metroplitan populations. Activity-related interests across the US and sedentary-related interests across NYC were significantly associated with obesity prevalence.

Learn more about Chunara's study

Semiha Ergan, an affiliate professor of the Computer Science and Engineering Department, is responsible for a project that performs data analysis on highly sensed buildings for understanding patterns in building performance. The data deals with HVAC systems and energy use in such buildings to assist in building management strategies.

Prof. Ergan is also the head of the Future Building Informatics and Visualization Lab (biLab).

For students who are interested in learning game programming and taking part in game development and design. Computer graphics, human-computer interaction, artificial intelligence, and allied computational fields all play a role in this burgeoning industry. Art and engineering intersect to create innovative game environments that captivate players.

Labs: Game Innovation Lab, MAGNET

Sample research projects:

Professor Julian Togelius specializes in artificial intelligence, and has programmed AI agents that play several existing video games. In the clip above, an AI agent plays through Super Mario Bros.

Learn more about Professor Julian Togelius's project

The theoretical study of computer science allows us to better understand the capabilities and the limitations of exactly what problems computers can solve, and when they can solve those problems efficiently. New theory helps pave the way for algorithmic breakthroughs that engineers can build on to create new solutions and technology. At NYU Tandon, the Algorithms and Foundations group is composed of researchers interested in applying mathematical and theoretical tools to a variety of disciplines in computer science, from machine learning, to computational science, to geometry, to computational biology, and beyond.

Sample research projects:

Christopher Musco and doctoral student Raphael A. Meyer wrote a paper titled “Hutch++: Optimal Stochastic Trace Estimation” that introduces an new randomized algorithm for implicit trace estimation, a linear algebra problem with applications ranging from computational chemistry, to understanding social networks and deep neural networks. Their method is the first to improve on the popular Hutchinson’s method for the problem, which was introduced over 30 years ago.
Read the paper

Lisa Hellerstein is the co-author of "The Stochastic Score Classification Problem."
This paper presents approximation algorithms for evaluating a symmetric Boolean function in a stochastic environment. The algorithms address problems where the goal is to determine the order in which to perform a sequence of tests, so as to minimize expected testing cost.
Read the paper