Research in Data/Information

Information encompasses technical notions of information and subsumes the generation, storage, processing, communication, and analysis of data. The generation of  data can be by humans directly (examples are video, images, audio, text, or shorter blurbs like tweets), by humans indirectly by their actions (examples are health data from wearables, telemetry from vehicles, human usage of web pages, or their capture of natural phenomenon), or by machines (control signals from smart phones or smart devices, positioning and localization, sensors, or computers). Storage, processing and retrieval ease the process of discovering information by humans or machines. Communication of information may be at the personal level (e.g., between devices that belong to a human being to the human), local areas (e.g., in small areas like houses, buildings, malls, campuses, communities), or wide area (across states, in between countries, typically through the Internet). Analyses of various data includes methods and techniques for data fusion, integration, credence, trust, provenance, privacy, and security. Machine learning and artificial intelligence are recent powerful techniques for analyzing data toward better decisions.

Faculty Members Involved in Research in Data Analytics & Information Analysis:

  • Peter Brusilovsky (adaptive Web systems, social Web, adaptive hypermedia, etc.),
  • Morgan Frank (Computational Social Science, Labor economics and analytics),
  • James Joshi (Access control, information and cloud security)
  • Daqing He (information retrieval and interactive retrieval-system design, user-modeling and adaptive Web-search system design and analysis, computational-linguistics and natural-language processing), 
  • Hassan Karimi (mobile computing, navigation, location-based services, geoinformatics, location-aware social networking, geospatial information systems, computational geometry, grid/distributed/parallel computing, and spatial databases), 
  • Yu-Ru Lin (human and social dynamics, computational approaches for mining and visualizing large-scale, time-varying, heterogeneous, multi-relational, and semi-structured data),
  • Balaji Palanisamy (Blockchain and distributed ledgers, information privacy)
  • Konstantinos Pelechrinis (sports and urban analytics, mathematical foundations of communications networks, and graph mining of the Internet), 
  • Vladimir Zadorozhny (networked information systems, complex adaptive systems, heterogeneous data fusion, wireless and sensor data management, query optimization in distributed environments, scalable architectures for wide-area environments with heterogeneous information servers).