The AFR research tracks all lie at the intersection of Artificial Intelligence, Data Analytics, and Software Analytics in the context of FinTech. Given this FinTech context, all tracks have three overarching themes in common:
- Human-centered: AFR emphasizes artificial intelligence methods and techniques that in which humans are in the lead, from all stakeholder perspectives.
- Data-driven: AFR recognizes that data and data management is key to AI-success, emphasizing data that is Findable, Accessible, Interoperable, and Reusable (FAIR).
- Software-defined: AFR acknowledges that modern banking is software defined, seeking to leverage AI to advance its software development practices, and making these practices ready for the AI-based systems of the future.
These themes recur in each of the tracks, yet in different proportions, with some tracks having a strong focus on the software side, and others a stronger focus on the human side.
Track 1: Software Analytics (Georgios Gousios)
Track 2: Data Integration (Christoph Lofi)
Track 3: Serverless Machine Learning (Asterios Katsifodimos)
Track 4: Deploying ML Models at Scale (Jan Rellermeyer, Luís Cruz)
Track 5: Continuous Experimentation (Arie van Deursen)
Track 6: AI-Based Quality, Testing, and Security (Annibale Panichella)
Track 7: Interactive Agents and Intelligent Orchestration (Catholijn Jonker)
Track 8: Natural Language Processing and Information Retrieval (Claudia Hauff)
Track 9: User Experience and Personalization
Track 10: Trustworthy AI (Cynthia Liem)
Track 11: Reinforcement Learning in Regulated Domains (Frank van Harmelen)