Subareas: Biostatistics, Computational Genetics, Proteomics, Statistical Genetics
Faculty: Ahalt, Gomez, Krishnamurthy, Marron, McMillan, Prins, SnoeyinkMore Info
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.
Molecular Structure Modeling and Analysis: Diverse biological function is encoded in the atomic structure of macro-molecules such as Proteins and RNA. Understanding the sequence to structure to function relationships allows biochemists to predict the activity of genes and rationally design genes with novel biological function. Our interests include computational geometry models for molecular structure, high performance computing for dynamic simulation, mining structure motifs for protein functional prediction, remote homology detection, protein-protein interaction, and protein-ligand interaction.
Subareas: Accelerators, Clockless Logic, Energy-efficient Computing
Faculty: Porter, SinghMore Info
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, Bishop, Fuchs, Lin, Manocha, Singh, Snoeyink, WhittonMore Info
Subareas: Architecture of Collaborative Systems, Collaborative Software Engineering, Collaborative Virtual Environments, Mobile Collaboration, Telepresence
Faculty: Dewan, Fuchs, Mayer-Patel, Pozefsky, Stotts, WhittonMore Info
Subareas: Geometric Vision, Language & Vision, Recognition
Faculty: Ahalt, A. Berg, T. Berg, FrahmMore Info
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 Language and Vision group, led by Prof. Tamara Berg, focuses on exploring the relationship between people, language, and pictures.
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.
The goal of the Language and Vision group is to develop a better understanding of the relationship between people, their visual data, and the language they use to describe that data. In particular, this includes developing methods to: describe images or video using natural language, predict how a person will refer to specific objects in complex real-world scenes, and answer natural language questions about images. The group also works on problems related to understanding what our pictures reveal about ourselves. Tasks include clothing and style recognition and are applied to clothing recognition and other e-commerce-related problems.
Subareas: Geometric Modeling & Computation, Solid Modeling
Faculty: Lin, Manocha, SnoeyinkMore Info
Subareas: Parallel Algorithms,
Cyberinfrastructure, GPUs & Other Computational Accelerators, Performance Analysis, Programming & Memory Models for Parallel Computing, Scientific Computing
Faculty: Ahalt, Lin, PrinsMore Info
Application of HPC principles and techniques for real-time physically based simulations and for large-scale scientific computing problems. Examples include simulation of physical, visual, and acoustic properties of spaces and materials, and computational fluid dynamics problems to understand mechanisms of flying and swimming in organisms from tiny insects to giant whales.
Subareas: Assistive Technology, Haptics, Human Factors Analysis, Sound & Audio Display, User-Interface Toolkits, Virtual Environments
Faculty: Bishop, Dewan, Lin, Nirjon, Porter, Pozefsky, Stotts, WhittonMore Info
Additional work in this area includes developing accessible educational tools for people with disabilities.
Subareas: Data Integration, Knowledge Discovery, Machine Learning, Scientific Data Management, Visual Analytics
Faculty: Ahalt, Bansal, A. Berg, T. Berg, Krishnamurthy, McMillan, Nirjon, Oliva, PrinsMore Info
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.
Subareas: Biomechanical Modeling, Diffusion Imaging, Image-guided Interventions, Segmentation, Shape Analysis, Registration
Faculty: Alterovitz, Lin, Marron, Niethammer, Oguz, Pizer, StynerMore Info
Subareas: Language Generation, Multimodal and Grounded NLP (with Vision and Robotics), Question Answering and Dialogue
Faculty: Bansal, A. Berg, T. BergMore Info
Subareas: Distributed Systems, Internet Measurements, Multimedia Systems, Multimedia Transport, Network Protocols
Faculty: Aikat, Dewan, Jeffay, Kaur, Mayer-Patel, Nirjon, Pozefsky, Smith, ReiterMore Info
Faculty: Anderson, Jeffay, NirjonMore Info
Subareas: Human-Robot Interaction, Kinematics & Dynamics, Manipulation, Medical Robotics, Planning & Algorithms, Robot Perception (see: Computer Vision)
Faculty: Alterovitz, Lin, Manocha, SnoeyinkMore Info
At UNC, we are creating new algorithms to address fundamental computational challenges in robotics, including motion planning in complex environments, efficiently modeling robot kinematics and dynamics, and providing new interfaces for human-robot interaction. We bring a broad range of expertise to these problems, including geometric computing, probabilistic methods, physically-based simulation, many-core CPU and GPU parallelization, machine learning, computational haptics, and computer vision. We apply the new algorithms and methods we develop to a variety of applications involving both physical robots as well as virtual agents. Current applications include robot-assisted medical procedures, surgery training, design prototyping, intelligent transportation systems, nano-scale manipulation, computer animation, multi-robot/agent interactions, and personal assistant robots.
Subareas: Cloud Computing Security, Mobile Device Security, Network Security
Faculty: Aikat, Monrose, Porter, Reiter, SturtonMore Info
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.
Mobile device security: Mobile devices like smartphones pose interesting challenges and opportunities in the area of computer security. Challenges arise because as mobile devices evolve toward fully functional computers with platforms like Android, they become targets for exploits that are now common for personal computers and potentially new exploits arising from the usage modes they enable. That said, as the first truly ubiquitous mobile computer, they offer new opportunities for security functionality, as well, e.g., for user authentication. We are conducting research to address the threats facing mobile devices and to harness the new opportunities they offer.
Subareas: Agile Methods, Aspect-oriented Programming, Collaborative Development, Design Patterns & Analysis, Model Federations for Systems Science
Faculty: Ahalt, Dewan, Porter, Pozefsky, StottsMore Info
Subareas: Algorithms, Automated Theorem Proving
Faculty: Anderson, Plaisted, SnoeyinkMore Info