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The purpose of workshops is to provide a more interactive and focused platform for presenting and discussing new and emerging ideas. The format of paper presentations may include oral presentations, poster presentations, keynote lectures and panels. Depending on the number of presentations, workshops can be scheduled for 1 day or 2 days. All accepted papers will be published in a special section of the conference proceedings book, under an ISBN reference, and on digital support. All papers presented at the conference venue will be available at the SCITEPRESS Digital Library. SCITEPRESS is a member of CrossRef and every paper is given a DOI (Digital Object Identifier). The proceedings are submitted for indexation by SCOPUS, Google Scholar, DBLP, Semantic Scholar, EI and Web of Science / Conference Proceedings Citation Index.

WORKSHOPS LIST

WAITT 2025, 2nd Workshop on Artificial Intelligence for Technology Transfer
Chair(s): Giuseppe Pirlo, Stefano Galantucci, Debora Montano and Chiara Verdone

2nd Workshop on
Artificial Intelligence for Technology Transfer - WAITT 2025

Paper Submission: April 7, 2025
Authors Notification: April 22, 2025
Camera Ready and Registration: May 2, 2025

Co-chairs

	Giuseppe Pirlo Department of Computer Science, University of Bari Italy

	Stefano Galantucci Department of Computer Science, University of Bari Italy

	Debora Montano Department of Agricultural Science, University of Foggia Italy

	Chiara Verdone Department of Agricultural Science, University of Foggia Italy

Scope

Recently, the Technology Transfer (TT) issue has become increasingly relevant for business economies and strategic for developing countries. TT refers to the transformation process in which scientific and technological research results are translated into market-ready solutions and societal applications, accompanied by the necessary skills and procedures. TT involves diverse and complex activities as a fundamental part of the technological innovation pipeline.

However, the rapid evolution of scientific and technological research generates an overwhelming volume of results at an unprecedented speed, outpacing the capacity of traditional TT processes. To harness the enormous potential of recent research outputs, there is an urgent need for more effective and efficient TT mechanisms.

Artificial Intelligence (AI) offers significant potential to optimize TT processes across various domains and application needs. By leveraging AI and machine learning (ML) approaches, it becomes possible to enhance innovations' identification, evaluation, and commercialization, making TT processes more robust and scalable.

This workshop aims to foster advanced research and discussions on innovative AI-driven techniques and intelligent systems for TT. It will explore how data science and AI/ML can improve TT workflows, from technology scouting and IP evaluation to stakeholder collaboration and commercialization. Key themes include ethical considerations, the integration of AI in decision-making, and emerging trends in predictive analytics. The ultimate goal is to promote cutting-edge strategies that optimize TT processes, enabling faster and more impactful innovation transfer.

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