BIG develops frameworks for the
production by naturalists, ecologists, taxonomists, teachers and
others of tools for identifying and documenting biodiversity on the
planet.
DMG is actively investigating aspects of Machine Learning and Data Mining, such as, similarity measures, association rule mining, video mining, mining structural representations such as XML documents, statistical and neural net based learning, probabilistic information retrieval, mining gene and biological databases, classification and model estimation, decision trees, Bayesian models and methods, statistical and conceptual clustering methods, applications of clustering, mining financial data, frequent pattern mining, and text mining.
DARG seeks to develop advances in database technology that support commercially viable database internal and application improvements. Recent research topics include devising benchmarks and improving designs for data warehousing, isolation testing to provide guarantees of correctness at lower isolation levels, concurrency control algorithms, and bit-sliced indexes and arithmetic. A student from our group was involved with us in a prototype product named C-Store that has since been used as the basis of an industry start-up named Vertica.
DSSG carries out fundamental and applied research in the area of distributed
software systems. Its long-term research goal is to make
distributed software systems more autonomous, scalable,
adaptive, survivable and easier to develop. DSSG's research
efforts address the research issues that cross the boundaries
among distributed computing, software engineering and
artificial intelligence.
NISLab's primary mission is to perform cutting-edge research in emerging areas of computer networks and distributed systems, particularly in support of information systems that can scale with both network size and data size. NISLab also provides hands-on educational resources to students who are interested in advanced network technologies, and engages in multidisciplinary R&D collaborations with other academic units at UMass as well as other institutions and industries in the Boston area.
VALab studies the attentional mechanisms underlying human vision and is particularly interested in investigating how attention is controlled for efficient performance of visual tasks. The main research paradigms are eye-movement recording and computational modeling. The findings resulting from this research are applied to the construction of computer vision systems and human-computer interfaces. More specifically, there are three main areas of research that the lab focuses on: Studying Human Eye Movements, Computational Modeling of Cognitive and Perceptual Processes, and Developing Gaze-Controlled Human-Computer Interfaces.
