Advances in Applied Data Science: Bridging Theory and Practice

Dear Colleagues,

The rapid evolution of the modern AI/ML landscape has led to remarkable opportunities for advancement in the delivery of personalized, proactive information tailored to individual or organizational needs. However, moving recent innovations from theory into practice involves further development across areas such as modeling real-time objectives and interests, managing information scale, efficiently prioritizing relevant data, and designing effective user interfaces for evaluating tailored information in context. The second annual Summer Conference on Applied Data Science (SCADS), hosted by the Laboratory for Analytic Sciences (LAS) at North Carolina State University (NCSU) in 2023, was organized around this theme. Expanding on the insights generated at this event, this Special Issue addresses the challenge of managing the vast amount of information available to knowledge workers across several layers of the information ecosystem, and the potential for recent innovations in machine learning, multimodal analytics (combining text, audio, imagery, sensor data, etc.), recommendation systems, data triage, LLMs, and human–machine collaborative systems to advance the state of the art in this emerging area. To that end, this Special Issue is an open call for advances in theoretical models, novel methodologies, and case studies of practical applications of personalized analytics, with the ultimate goal of encouraging breakthrough innovation in tailored reporting for knowledge workers across all data-driven domains.

Dr. R. Jordan Crouser
Guest Editor

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