ASU Center for Assured and Scalable Data Engineering (CASCADE)

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Labs

Research Partners

JCI JCI is a partner in NSF Grant #1339835
EarlyWarning EarlyWarning is a partner in NSF Grant #1430144
Centurylink CenturyLink provides computation power for NSF Grants #1318788 and #1518939
Siemens Siemens is a partner in NSF Grant #1239093
Mayo and CASCADE partners in an NVIDIA GPU Research Center
RAI RAI is a partner of a UNITO/RAI-CRIT research agreement

 

Support for CASCADE comes from ASU OKED, Fulton Schools of Engineering, and CIDSE. CASCADE is also supported partially by NSF-IIP Grant #1464579.

 

BlockChain Lab
ASU wide Lab 

Blockchain Research Lab's mission is to advance the research and development of blockchain-based technologies for use in Business, Finance, Economics, Mathematics, Computer Science, and all other areas of potential impact.

The objective of the Blockchain Research Lab is to create an opening and welcoming environment at Arizona State University where academics, scholars, students, faculty, scientists, and entrepreneurs can come together to see this technology positively impact the world and further our knowledge of how to apply it.

Contact: Dr. Dragan Boscovic ( dragan dot boscovic at asu dot edu)

 

CIPS Lab
Dr. Hasan Davulcu, Director

Cognitive Information Processing Systems (CIPS) Lab's research focuses on developing novel data mining techniques and tools for structuring and organizing unstructured sources such as text, Web, and social network data into semantic machine-processable information. Such representations enable the creation of conceptual maps to allow users to search and browse without information overload.

  • Sociocultural Modelling
  • Social Media and Web Mining
  • Information Extraction and Database Systems
  • Behavioral Analytics for Detecting Fraud.

Lab Home

 

EMITLab
Dr. K. Selcuk Candan, Director

At ASU's EmitLab (Enterprise, Multimedia and Information Technologies Lab), we focus on research in the area of management of non-traditional, heterogeneous, and imprecise (such as multimedia, web, and scientific) data. 

  • Incremental Data Analytics,
  • Tensor Decomposition
  • Interval Values SVD
  • High Dimensional Time Series Analysis
  • Personalized PageRank
  • Deep Learning
  • High Dimensional Spatial Indexing

Lab Home

 

Support for CASCADE comes from ASU OKED, Fulton Schools of Engineering, and CIDSE. CASCADE is also supported partially by NSF-IIP Grant #1464579.