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PolyPhy is a GPU oriented agent-based system for reconstructing and visualizing optimal transport networks defined over sparse data. Rooted in astronomy and inspired by nature, we have used an early prototype called Polyphorm to reconstruct the Cosmic web structure, but also to discover network-like patterns in natural language data.

This project aims at designing efficient, adaptive link level load balancers for networks that handle different kinds of traffic, in particular networks where flows are heterogeneous in terms of their round trip times.

AsterixDB is an open source parallel big-data management system. AsterixDB is a well-established Apache project that has beedddn active in research for more than 10 years. It provides a flexible data model that supports modern NoSQL applications with a powerful query processor that can scale to billions of records and terabytes of data.

CephFS is a distributed file system on top of Ceph. It is implemented as a distributed metadata service (MDS) that uses dynamic subtree balancing to trade parallelism for locality during a continually changing workloads.

DirtViz is a project to visualize data collected from sensors deployed in sensor networks. We have deployed a number of sensors measuring qualities like soil moisture, temperature, current and voltage in outdoor settings.

As storage devices get faster, data management tasks rob the host of CPU cycles and main memory bandwidth. The Eusocial project aims to create a new interface to storage devices that can leverage existing and new CPU and main memory resources to take over data management tasks like availability, recovery, and migrations.

FasTensor is a parallel execution engine for user-defined functions on multidimensional arrays. The user-defined functions follow the stencil metaphor used for scientific computing and is effective for expressing a wide range of computations for data analyses, including common aggregation operations from database management systems and advanced machine learning pipelines.

FasTensor is a parallel execution engine for user-defined functions on multidimensional arrays. The user-defined functions follow the stencil metaphor used for scientific computing and is effective for expressing a wide range of computations for data analyses, including common aggregation operations from database management systems and advanced machine learning pipelines.

HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections. The HDF5 technology suite includes: A versatile data model that can represent very complex data objects and a wide variety of metadata.