Workshop
2nd Workshop on Artificial Intelligence for Technology Transfer -
WAITT
2025
10 - 12 June, 2025 - Bilbao, Spain
In conjunction with the 20th International Conference on Software Technologies - ICSOFT 2025
* CANCELLED *
CO-CHAIRS
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Giuseppe Pirlo
Department of Computer Science, University of Bari
Italy
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Brief Bio
Giuseppe Pirlo received the degree (cum laude) in computer science from the Department of Computer Science, University of Bari, Italy, in 1986. Since 1986, he has been carrying out research in the field of computer science and neuroscience, signal processing, handwriting processing, automatic signature verification, biometrics, pattern recognition, and statistical data processing. Since 1991, he has been an Assistant Professor with the Department of Computer Science, University of Bari, where he is currently a Full Professor. He developed several scientific projects and authored more than 250 articles on international journals, scientific books, and proceedings. He is a member of the Governing Board of Consorzio Interuniversitario Nazionale per l’Informatica (CINI), the Governing Board of the Societa Italiana di e-Learning, the e-learning Committee of the University of Bari, the Gruppo Italiano Ricercatori in Pattern Recognition, the International Association of Pattern Recognition, the Stati Generali dell’Innovazione, and the Gruppo Ingegneria Informatica. He was the General Chair of the International Workshop on Emerging Aspects in Handwriting Signature Processing, Naples, in 2013, and the International Workshop on Image-Based Smart City Applications, Genoa, in 2015; and the General Co-Chair of the International Conference on Frontiers in Handwriting Recognition, Bari, in 2012. He was an Editor of the Special Issue Handwriting Recognition and Other PR Applications of the Pattern Recognition journal in 2014 and the Special Issue Handwriting Biometrics of the IET Biometrics journal in 2014. He was a Guest Editor of the Special Issue of Journal of e-Learning and Knowledge Society: Steps Toward the Digital Agenda: Open Data to Open Knowledge (Je-LKS) in 2014.
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Stefano Galantucci
Department of Computer Science, University of Bari
Italy
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Brief Bio
Stefano Galantucci is a Research Fellow at the Computer Science Department, University of Bari Aldo Moro, Italy. In 2024 he received a PhD degree in Computer Science, defending a thesis in cybersecurity concerning innovative methodologies for Firewalls and Intrusion Detection/Prevention Systems. He also holds a MSc degree in Cybersecurity with full marks and honors. He has published various scientific papers in international journals and conferences on topics covering the research areas of cybersecurity, network and application security, cryptography, and biometrics. He is also an adjunct lecturer teaching Cryptography at the Master's Degree in Cybersecurity at the University of Bari Aldo Moro. He is a member of multiple examination boards at the University of Bari Aldo Moro, including Biometric Systems (MSc in Cybersecurity) and Models and Methods for Application Security (BSc in Computer Science). He is a Reviewer for many Journals, including IEEE Transactions on Information Forensics and Security, and Computers & Security.
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Debora Montano
Faculty of Medicine and Surgery, University of Modena and Reggio Emilia - UNIMORE / Department of Agricultural Sciences, Food, Natural Resources and Engineering (DAFNE), University of Foggia
Italy
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Brief Bio
Debora Montano is a PhD student at the University of Modena and Reggio Emilia & University of Foggia. She has a master's degree in Statistics and Actuarial Sciences from the University of Sannio, Benevento, Italy. She researched for 3 years at the Computer Science Department of the University of Sannio and after at the Regional Center Information Communication Technology (CeRICT srl), where she worked as a Data Scientist and Analyst for Machine Learning, Big Data processing activities and Artificial Intelligence (AI) solutions. Currently, Debora's research fields are Medical IoT security, Medical software quality, and empirical studies for health AI solutions. She has published several articles based on Machine Learning and Deep Learning techniques applied to the research fields of her interest for international conferences and journals. She has also worked as a reviewer for many international conferences.
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Chiara Verdone
Department of Agricultural Sciences, Food, Natural Resources and Engineering (DAFNE), University of Foggia
Italy
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Brief Bio
Chiara Verdone is a Post-Doc Researcher at the Department of Agricultural Science, Food, Natural Resources and Engineering, University of Foggia, in Foggia, Italy. She attended the PhD program at the University of Sannio, Italy. She received her Master's degree in Computer Engineering in 2021. Her current research interests mainly concern data analytics and artificial intelligence in the health field, particularly in the diagnosis, progress, and treatment of different types. In fact, she has published several papers based on Machine Learning and Deep Learning techniques applied to the domain in question for international conferences and journals. She has also been a reviewer for several conferences.
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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.
TOPICS OF INTEREST
Topics of interest include, but are not limited to:
- Databases Development for TT
- Data Driven TT
- Digital Twin of TT
- Digitalization of TT Processes
- Intellectual Property Analytics
- Knowledge management for TT processes
- Leveraging ML/AI for TT processes framing and optimisation
- Patent Mining
- TT Modelling
IMPORTANT DATES
Paper Submission:
April 7, 2025 (expired)
Authors Notification:
April 22, 2025 (expired)
Camera Ready and Registration:
May 2, 2025
WORKSHOP PROGRAM COMMITTEE
Available soon.
PAPER SUBMISSION
Prospective authors are invited to submit papers in any of the topics listed above.
Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
Please also check the Guidelines.
Papers must be submitted electronically via the web-based submission system using the appropriated button on this page.
PUBLICATIONS
After thorough reviewing by the workshop program committee, all accepted papers will be published in the workshop 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 (http://www.scitepress.org/DigitalLibrary/).
SCITEPRESS is a member of CrossRef (http://www.crossref.org/) and every paper is given a DOI (Digital Object Identifier).