Research Tracks
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 data human side.
The AFR tracks are listed in the table.
| Id | Track | PhD Candidate | Track leads |
|---|---|---|---|
| 1 | Software Analytics | Elvan Kula | Georgios Gousios, Arie van Deursen |
| 2 | Data Integration in Stream Processing | George Siachamis | Asterios Katsifodimos |
| 3 | AI-Based Software Quality, Testing, and Repair | Leonhard Applis | Annibale Panichella |
| 4 | Concept Drift and ML-Ops | Lorena Poenaru-Olaru | Jan Rellermeyer, Luís Cruz |
| 5 | Human-AI Decision Making | Sara Salimzadeh | Ujwal Gadiraju |
| 6 | Trustworthy AI | Patrick Altmeyer | Cynthia Liem |
| 7 | Incident Management | Eileen Kapel | Luís Cruz, Diomidis Spinellis |
| 8 | Reinforcement Learning in Regulated Domains | Floris den Hengst | Frank van Harmelen |
