Research Areas from the Computer Science Main Page

Robotic BioTelemetry aims to develop new algorithms and systems to quantitatively measure natural habitats and animal activities via remotely controlled networked robotic cameras. This project will develop new non-intrusive biotelemetry methods based on emerging advances in high-resolution networked robotic cameras and long range wireless networking. Robotic BioTelemetry will allow scientists to remotely identify and measure in real-time important variables such as quantity, size, and behavior characteristics without disturbing the animal in its natural setting, thus giving us a more accurate picture of some of Nature's most elusive creatures. For more information please visit this website.
Our research on mouse brain networks has the potential to transform the way we think about computation. Network function is built on architecture, both connectivity and neuron morphology, and it is the architecture of the mouse brain as a template for all mammalian brains that constitutes the subject of our investigation. In the long term, findings from our research will lead to a theory of parallel and distributed information processing that bridges both computer and brain networks. Such brain network models could help greatly in building the next generation of IT infrastructure: one less brittle, better adapted to providing relevant and timely information, and better able to model and interact with its environment. For more information please visit Brain Networks Lab website.
The Real Time Distributed Systems Lab is focused on modeling and designs of systems. The objective of modeling is to capture the system (dynamic) behaviors in the most accurate yet compact forms, so that they can be used for optimization of the system design and operation. Started from the stochastic modeling paradigm, our modeling work is expanded to real-time data based modeling paradigm using proper control theories, and signal processing techniques. We are particularly interested in attacking issues that require cross cutting solution techniques, where high level behaviors would be characterized by stochastic models and low level features would be captured by multi-resolution filtering techniques. For more information please visit the RTDS website.
The Laboratory for Distributed Information Systems conducts research on the theory and applications of distributed information systems. Currently we are focusing on data storage in memories and networks. Under development is a new data storage technology for next-generation flash memories with substantially improved longevity, reliability and efficiency. Results of this research include developing the theoretical foundation for a new data-storage technology and designing new hardware architecture for flash memories. For more information please visit the DIS website.
The High Performance Computing Laboratory's research interests lie in the general area of Computer Architecture and High Performance Computing. Specifically, our work has focused on providing network support for Chip Multiprocessor and high performance cluster systems.The issues dealt with in our past/ongoing research include high performance cluster interconnect design, energy optimization techniques in clusters, and secure cluster design. In addition, many of the issues will be re-examined in the context of on-chip networks, which are increasingly finding their way into commercial markets and could be used as cluster nodes in the future. For more information please visit the HPC Lab website.
The Interface Ecology Lab invents new media and information by exploring new relationships between human beings and technology. We instantiate the ecosystems approach, with a focus on connecting sensation, computation, media, networks, ontologies, methodologies, and cultures. We develop digital systems, environments, components, and compositions with the intention of elevating the role of human expression in experiences with technology. For more information please visit the Interface Ecology Lab website.
The Pattern Recognition and Intelligent Sensor Machines (PRISM) Lab focuses on the interface between signal processing, pattern recognition, neural computation, robotics and sensor systems. Our interest is in understanding how sensory systems (man-made or biological) perceive, interact with, learn from and adapt to their environments under a number of modalities, including chemical, acoustic, visual, and physiological. In the process, we draw motivation from multiple disciplines, from neurobiology to perceptual psychology. For more information please visit the PRISM website.
STAPL (the Standard Template Adaptive Parallel Library) is a framework for developing parallel programs in C++. It is designed to work on both shared and distributed memory parallel computers. Its core is a library of ISO Standard C++ components with interfaces similar to the (sequential) ISO C++ standard library. STAPL includes a run-time system, design rules for extending the provided library code, and optimization tools. Its goal is to allow the user to work at a high level of abstraction and hide many details specific to parallel programming, to allow a high degree of productivity, portability, and performance. For more information please visit the STAPL homepage.
Textal is an automated program for determining protein structures based on data from X-ray crystallography experiments. Textal uses feature extraction and pattern recognition methods to automate interpretation of electron density maps and generate an atomic model of the protein. This innovative program will help reduce the workload for crystallographers, as the traditional model-building process is error prone and time consuming. For more information please visit the Textal homepage.

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