Research
Research Clusters
Computer Science Department research is organized around five clusters. These clusters build upon the excellence already present in the department and in the university. Each cluster has three to eight faculty, with each faculty member participating in an average of two clusters. The five clusters cover existing research and provide a framework for excellence as we move forward.
The Advanced Computing Systems cluster explores novel ways to enhance the functionality or performance of existing computer systems and new ways to exploit the capabilities of emerging technologies (e.g., dual-core processors, energy-aware processors, and sensors). Current projects examine a range of technologies and applications including sensor networks, wireless and wired networks, embedded systems, and distributed systems.
The Computational Biology cluster applies computer science, mathematics, and statistics to computational questions in biology. Work of the current cluster members centers around algorithm design and software development.
The Data-Centric Computing cluster integrates data semantics and application behavior to address critical challenges in the management of large scale data for data intensive applications and to achieve increased performance, accuracy, availability, and reliability. The scale of the data means that the entire data set will often not fit in main memory or even on a single disk. The overarching goal is to manage data at all points along the memory hierarchy, from main memory to disks to clusters of disks to archival tape to across the web. Examples of large-scale data relevant to this cluster include image data, multi-dimensional data, spatial data, temporal data, and spatio-temporal data from astronomic, sensor net, wireless, and pervasive applications.
The Intelligent Systems cluster is focused on developing algorithms and processes to discover what is important and meaningful in complex data. This cluster is focused on the integration of these algorithms into information processing systems that are more intelligent, flexible and robust than currently available. This includes systems that infer representations of data that are designed for effective visualization as well as collaborative feedback from users, particularly in the case of scientific data.
The Secure and Reliable Software Systems cluster develops tools and techniques for ensuring that software systems satisfy security and/or reliability properties. Security properties include that the system cannot be compromised (integrity) and that it will not divulge information except to authorized parties (privacy). Reliability properties include that it computes the right answers (correctness) and continues to function in the presence of erroneous input or hardware failures (robustness). As more and more of our daily lives become affected by software (the cars we drive, the planes we fly, the machines that dispense anesthetics---they all contain computer software), it becomes essential that we can establish methods for constructing systems that are secure and reliable. Advances in this cluster can thus have a profound impact on society.