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Research in Computer Science
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Security and privacy.
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 , C CS
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
Learn more about Digital Assembly
Learn more about Seattle Open Peer-to-Peer Computing
Big Data Management, Analysis, and Visualization
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.
Learn more about RevEx and download the demo
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
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
Prof. Ergan is also the head of the Future Building Informatics and Visualization Lab (biLab).
Game Engineering and Computational Intelligence
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
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
Algorithms and Foundations
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.
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
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The computing and information revolution is transforming society. Cornell Computer Science is a leader in this transformation, producing cutting-edge research in many important areas. The excellence of Cornell faculty and students, and their drive to discover and collaborate, ensure our leadership will continue to grow.
The contributions of Cornell Computer Science to research and education are widely recognized, as shown by two Turing Awards, two Von Neumann medals, two MacArthur "genius" awards, and dozens of NSF Career awards our faculty have received, among numerous other signs of success and influence.
To explore current computer science research at Cornell, follow links at the left or below.
Knowledge representation, machine learning, NLP and IR, reasoning, robotics, search, vision
Statistical genetics, sequence analysis, structure analysis, genome assembly, protein classification, gene networks, molecular dynamics
Computer Architecture & VLSI
Processor architecture, networking, asynchronous VLSI, distributed computing
Database systems, data-driven games, learning for database systems, voice interfaces, computational fact checking, data mining
Interactive rendering, global illumination, measurement, simulation, sound, perception
HCI, interface design, computational social science, education, computing and society
Artificial intelligence, algorithms
Programming language design and implementation, optimizing compilers, type theory, formal verification
Perception, control, learning, aerial robots, bio-inspired robots, household robots
Numerical analysis, computational geometry, physically based animation
Secure network services, language-based security, mobile code, privacy, policies, verifiable systems
The software engineering group at Cornell is interested in all aspects of research for helping developers produce high quality software.
Operating systems, distributed computing, networking, and security
The theory of computing is the study of efficient computation, models of computational processes, and their limits.
Autonomous and cyber-physical systems.
Subareas: Real-time and Embedded Systems, Sensor Systems, Mobile Computing, Control Theory and Systems, Formal Methods, Automated Verification and Certification Faculty: Alterovitz , Anderson , Chakraborty , Duggirala , Nirjon , Smith
More on Autonomous and Cyber-Physical Systems
Bioinformatics and computational biology.
Subareas: Computational Genetics, Computational Immunology, Proteomics, Statistical Genetics, Single-Cell Bioinformatics Faculty: Ahalt , Krishnamurthy , Marron , McMillan , Prins , Snoeyink , Stanley
More on Bioinformatics and Computational Biology
Computational Immunology: Advancements in high-throughput flow and mass cytometry technologies have enabled the ability to study the immune system at an unparalleled depth. Understanding immunological adaptations to particular diseases and in aging and development offers unique opportunities to develop novel diagnostic tests or to propose specialized treatments or lifestyle interventions to optimize human health. Using single-cell flow and mass cytometry data collected across multiple individuals, our goal is to develop new computational techniques to identify and link heterogeneity in the cellular landscape to external variables of interest, such as, a clinical phenotype or diagnosis. Recent advances in imaging cytometry also enable taking images of tissues and studying the spatial organization of immune cells. Application areas of interest include pregnancy, HIV, neuroimmunology, and T-cell biology. Relevant People Natalie Stanley ; Collaborating Departments: Microbiology and Immunology , Computational Medicine Program , Department of Anesthesia
Development and Differentiation, and Metagenomics: We use novel measurement techniques as well as machine learning methods in understanding the interplay between these areas, with the aim of discovering the forces that shape the immune system throughout life. The overarching goal is to apply the insights from such analyses to propose new treatments for cancers.
Single-Cell Bioinformatics: Cellular heterogeneity, or the synergy of diverse and specialized cell-types drive a range of biological phenomena. Several technologies exist for measuring various properties (e.g. gene expression, protein expression) in individual cells, which allows for their comprehensive characterization and analysis in clinical or biological applications. Single-cell measurements can be studied in vitro to understand the etiology of disease. For example, in hypoxia of heart muscle cells, the cells become scar tissue and lose their muscle function. This process can be studied by looking at single cell transcriptomes to determine the order of events. Further, it can be possible to “reprogram” this sequence of events to avoid the adverse outcome. See some of our recent work in reprogramming scar tissue cells to recover some heat muscle cell functionality.
Technologies and Data Science Problems: Single-cell datasets produced with technologies, such as single-cell RNA sequencing (scRNA-seq) or flow and mass cytometry reveal a unique data structure where there are several high-dimensional single-cell measurements per profiled sample, which need to be efficiently integrated.
Flow and Mass Cytometry : Flow and mass cytometry are high-throughput single-cell proteomics technologies for systematic analysis of the immune system. Often applied for the analysis of human blood and tissue samples, the produced datasets can collectively contain millions of cells. We focus on developing new computational techniques for representing, dissecting, and mining this large volume of cells to identify immunological adaptations in disease and development.
Relevant People: Jan Prins , Leonard McMillan , Natalie Stanley
Subareas: Accelerators, Clockless Logic, Energy-efficient Computing, Security Faculty: Porter , Singh , Sturton
More on Computer Architecture
Energy-Efficient Systems: With the explosive growth in mobile devices, there has been a push towards increasing energy efficiency of computation for longer battery life. Reducing power consumption is also important for desktop computing to alleviate challenges of heat removal and power delivery. A special focus in our department has been on the development of energy-efficient graphics hardware. Another area of future interest is energy-harvesting systems, which are ultra-low-power systems that operate on energy scavenged from the environment.
Asynchronous or Clockless Computing: Asynchronous VLSI design is poised to play a key role in the design of the next generation of microelectronic chips. By dispensing with global clocks and instead using flexible handshaking between components, asynchronous design offers the benefits of lower power consumption, greater ease of integration of multiple cores, and greater robustness to manufacturing and runtime variation. Our researchers work on all aspects of asynchronous design, including circuits, architectures, and CAD tools. A key area of interest is application to network-on-a-chip for integration of multiple heterogeneous cores.
Subareas: Animation & Simulation, Graphics Hardware, Modeling, Rendering, Tracking, Virtual Environments, Visualization Faculty: Alterovitz , Chakravarthula , Fuchs , Reed , Sengupta , Singh , Snoeyink , Daniel Szafir , Danielle Szafir , Whitton
More on Computer Graphics
Computer-supported collaborative work.
Subareas: Architecture of Collaborative Systems, Collaborative Software Engineering, Collaborative Virtual Environments, Mobile Collaboration, Telepresence Faculty: Dewan , Fuchs , Mayer-Patel , Pozefsky , Stotts , Whitton
More on Computer-Supported Collaborative Work
Subareas: Geometric Vision, Language & Vision, Recognition Faculty: Ahalt , Bansal , A. Berg , Bertasius , Frahm , Niethammer , Sengupta
More on Computer Vision
The 3D Computer Vision group in the Department of Computer Science, led by Prof. Jan-Michael Frahm, conducts research in the areas of geometric computer vision and 3D reconstruction, as well as real-time and active computer vision. The Recognition group, led by Prof. Alexander Berg, develops algorithms for object detection, image understanding, and situated recognition in the real world, and studies large-scale machine learning.
The goal of the research being done by the 3D Computer Vision group is to develop fully automated systems for accurate and rapid 3D reconstruction of urban environments from photo collections and videos. The focus includes modeling the dynamic and transient scene objects to bring the models “alive”. Beyond pure reconstruction, the group has research thrusts on large-scale geo-location of terrestrial images. For many applications, 3D models are more descriptive and compact than the frames of the original video. For example, in a 3D model of a city, users can see a very large area at once, realize the spatial arrangement of the buildings at a single glance, and navigate freely to the parts that most interest them, something that would be more difficult and time-consuming using the original video. The 3D Computer Vision group further investigates in collaboration with Prof. Fabian Monrose the impact of modern computer vision methods onto data privacy and computer security.
The goal of the Recognition group is to develop algorithms to enable computers to extract semantic information from still image, depth, and video data. This includes understanding high-level scene categories (e.g., city, beach, forest, classroom), segmenting and identifying individual objects (cars, people, buildings, etc.), as well as identifying materials (glass, metal, wood, etc.) and surface properties (e.g., horizontal vs. vertical surfaces). The Recognition group is also developing efficient methods for large-scale recognition both on the internet and in the real world. The latter focus, on situated recognition algorithms, contributes to developing better systems — such as robots — for interacting in the physical world.
Subareas: Geometric Modeling & Computation, Solid Modeling Faculty: Snoeyink
More on Geometric Computing
Subareas: Parallel Algorithms, Cyberinfrastructure, GPUs & Other Computational Accelerators, Performance Analysis, Programming & Memory Models for Parallel Computing, Scientific Computing Faculty: Ahalt , Prins
More on High-Performance Computing
Subareas: Assistive Technology, Haptics, Human Factors Analysis, Sound & Audio Display, User-Interface Toolkits, Virtual Environments Faculty: Dewan , Nirjon , Porter , Pozefsky , Srivastava , Stotts , Daniel Szafir , Whitton
More on Human-Computer Interaction
Wearable devices, such as smart watches and smart glasses, and other common sensors are increasingly facilitating new modes of interaction with modern computers—making the goal of ubiquitous computing realizable. A major research direction in HCI at UNC is exploring design techniques and system support to more easily extend desktop and phone applications onto devices with widely varying form factors and interaction modes.
Machine Learning and Data Science
Subareas: Data Integration, Internet of Things, Knowledge Discovery, Machine Learning, Scientific Data Management, Visual Analytics Faculty: Ahalt , Bansal , A. Berg , Bertasius , Chaturvedi , Krishnamurthy , McMillan , Niethammer , Nirjon , Oliva , Prins, Raffel , Sengupta , Srivastava
More on Machine Learning and Data Science
Machine Learning: The problems we study combine vast amounts and disparate types of measurements with equally complex prior knowledge, posing unique challenges for machine learning. Our interests include both modeling paradigms, such as Bayesian nonparametric methods, and inference methodologies, such as MCMC, variational methods and convex optimization. We also work on structured, interpretable, and generalizable deep learning models. Other topics of focus include multi-task learning, reinforcement learning, and transfer learning.
Medical Image Analysis
Subareas: Biomechanical Modeling, Diffusion Imaging, Image-guided Interventions, Segmentation, Shape Analysis, Registration Faculty: Alterovitz , Marron , Niethammer , Oguz , Pizer , Styner
More on Medical Image Analysis
Natural language processing.
Subareas: Language Generation, Multimodal and Grounded NLP (with Vision and Robotics), Question Answering and Dialogue Faculty: Bansal , A. Berg , Chaturvedi , Srivastava
More on Natural Language Processing
Subareas: Distributed Systems, Internet Measurements, Multimedia Systems, Multimedia Transport, Network Protocols Faculty: Aikat , Dewan , Jeffay , Kaur , Mayer-Patel , Monrose , Nirjon , Pozefsky , Smith
More on Networking
Subareas: File Systems, Virtualization, Concurrency, Software Support for Secure Hardware Faculty: Anderson , B. Berg , Jeffay , Porter , Smith
More on Operating Systems
This area has substantial overlap with a number of other research areas, including cyber-physical systems, real-time systems, mobile systems, networking, architecture, human-computer interaction, and security.
Faculty: Anderson , Jeffay , Nirjon
More on Real-Time Systems
Subareas: Assistive Robotics, Manipulation, Medical Robotics, Motion Planning & Control, Robot Learning, Robot Perception (see: Computer Vision) Faculty: Alterovitz , Bansal , Snoeyink , Daniel Szafir
More on Robotics
Subareas: Cloud Computing Security, Cryptography, Hardware Security, Mobile Device Security, Network Security Faculty: Aikat , Eskandarian , Monrose , Porter , Sturton
More on Security
Network security: Today’s Internet infrastructure is a common target of attack and the vehicle for numerous unwanted activities in network applications (e.g., spam, phishing). We are conducting research to evaluate the extent of these vulnerabilities and to develop defenses against them. This includes research on both protecting the Internet infrastructure from attack and designing defenses within the context of network applications.
Cloud computing security: An undeniable trend in computing is increased use of “clouds”, i.e., facilities to which customers outsource data and processing. Because these facilities are shared, however, a customer’s data and processing may reside with those of competitors or attackers, and so privacy and integrity of the customer’s activities are paramount. We are developing technologies to better protect data and processing in such threatening environments.
Subareas: Agile Methods, Aspect-oriented Programming, Collaborative Development, Design Patterns & Analysis, Model Federations for Systems Science Faculty: Ahalt , Dewan , Porter , Pozefsky , Stotts
More on Software
Subareas: Algorithms, Automated Theorem Proving, Formal Methods Faculty: Anderson , B. Berg , Duggirala , Eskandarian , Snoeyink
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.
Finally, a particular concern of computer science throughout its history is the unique societal impact that accompanies computer science research and technological advancements. With the emergence of the Internet in the 1980s, for example, software developers needed to address important issues related to information security, personal privacy ...
NLP research in the UNC Department of Computer Science (Prof. Bansal’s group) focuses on human-like language generation and question-answering/dialogue, multimodal, grounded, and embodied semantics (i.e., language with vision and speech, for robotics), and interpretable and structured deep and structured learning models.