PhD defence
Open-world Continual Learning via Knowledge Transfer
- Y. Li
- Date
- Tuesday 27 January 2026
- Time
- Location
-
Academy Building
Rapenburg 73
2311 GJ Leiden
Supervisor(s)
Summary
This PhD research explores how artificial intelligence (AI) systems can learn continuously in an ever-changing and unpredictable world. Traditional AI models are usually trained once on fixed data and struggle when facing new situations. In contrast, this thesis investigates Open-World Continual Learning (OWCL) — a new way for AI to adapt to ongoing changes while recognizing and learning from completely new information.
The research establishes OWCL as a distinct scientific problem and provides theoretical and practical foundations for it. Through a series of models and experiments, the work shows how AI can retain what it has learned, avoid forgetting old knowledge, and flexibly integrate new insights. Three proposed models—Pro-KT, PEAK, and HoliTrans—illustrate how knowledge can be transferred and updated over time. Together they move AI closer to human-like learning: dynamic, adaptive, and aware of the unknown.
To demonstrate its real-world relevance, the thesis applies OWCL principles to fraud detection across different financial regions. The proposed system learns from changing fraud patterns and adapts to new threats without losing past experience, leading to more secure and trustworthy financial technologies.
Overall, this research contributes to building AI systems that can learn for life—continuously, responsibly, and intelligently—supporting safer and more adaptive technology for society.
PhD dissertations
Approximately one week after the defence, PhD dissertations by Leiden PhD students are available digitally through the Leiden Repository, that offers free access to these PhD dissertations. Please note that in some cases a dissertation may be under embargo temporarily and access to its full-text version will only be granted later.
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General information
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