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External Collaborations and Partnerships: Industry, Nonprofits, Governments, and Academia

AI/ML/BD are areas of research that are critically dependent on collaborations with industry, nonprofits, governments, as well as other universities. Many of these collaborations lead to successful technology transfer and adoption of research ideas into real-world products. The pathways for these collaborations are diverse, and may include:

  • The Center serves as a clearinghouse of partner-inspired real-world problems that supplies to not only research projects but also capstone and senior design projects, independent study, and REU.  
  • The Center offers free consultation to the community which may lead to sustaining and deeper collaborations.
  • The Center supports external organizations in conducting various activities such as workshops, Hackathons, and summer camps.

CARIDA members have been engaged in various collaborations. A few are listed below. All efforts will be made to nurture and sustain existing engagements, as well as to seek out new collaborative engagements. The eventual goal is to develop a “branding” of the Center as a one-stop hub for organizations seeking know-how and expertise in tackling their domain-specific data-intensive problems.

Example External Collaborations and Partnerships

  • AT&T: Dr. Gautam Das has recently completed a funded project in collaboration with AT&T on developing highly scalable communication network traffic flow matrix computation. The results have been published at several premier conferences and journals, including an invited paper in the Research Highlights of the Communications of the ACM, 2021.
  • UT Southwestern and University of Maryland Medical Schools: Dr. Gautam Das is collaborating with researchers at the medical schools of UT Southwestern and University of Maryland, respectively, on developing affordable diagnostics for obstructive sleep apnea in children. This interdisciplinary project leverages the combined background of the PIs that spans theoretical expertise in physiologic signal analysis, clinical management of sleep disorders, surgical outcomes as well as Big Data Analytics over large clinical databases.
  • NSF Convergence Accelerator Project on Credible Open Knowledge Network: Dr. Chengkai Li recently led a 20-organization international team of researchers and partners from academia, governments and industry, including computer scientists and researchers in psychology, cybersecurity, healthcare, journalism, and communication, in conducting an NSF Convergence Accelerator project on Credible Open Knowledge Network. In a world where decision making is heavily driven by data, lack of information credibility leads to a populace misled and divided by widespread misinformation and algorithmic decisions that are irrelevant or even catastrophic. The project aims to build a framework and tools to assist software developers and domain experts to ensure the credibility of their decision-making software powered by open knowledge networks (OKN).