This page archives our main scientific contributions developed under the principles of Trustworthy Algorithms. Each work documented here reflects a deliberate design process where technical, socio-technical, and institutional properties are not secondary considerations, but foundational engineering constraints integrated from problem formulation through deployment:
2026 | An empirical evaluation of a domain-specific language for maintenance scheduling optimization
- Venue: Science of Computer Programming (Elsevier)
- Link: DOI: 10.1016/j.scico.2026.103533
- Entry: Beyond complex code: why usability is vital for fleet optimization
- Properties:
[Accessibility][Efficiency][Robustness]
2025 | Enhancing software-defined perimeters with integrated identity solutions and threat detection for robust zero trust security
- Venue: International Journal of Information Security (Springer)
- Link: DOI: 10.1007/s10207-025-01099-9
- Entry: The end of implicit trust: why modern networks demand continuous algorithmic verification
- Properties:
[Observability][Privacy][Robustness][Security]
2024 | Securesdp: a novel software-defined perimeter implementation for enhanced network security and scalability
- Venue: International Journal of Information Security (Springer)
- Link: DOI: 10.1007/s10207-024-00863-7
- Entry: Beyond the black cloud: why true zero trust requires dynamic component hardening
- Properties:
[Robustness][Security]
2024 | Interpretability of rectangle packing solutions with Monte Carlo tree search
- Venue: Journal of Heuristics (Springer)
- Link: DOI: 10.1007/s10732-024-09525-2
- Entry: MCTS for rectangle packing: why industrial optimization demands interpretable algorithms
- Properties:
[Accountability][Interpretability][Observability]
2024 | Apollon: A robust defense system against Adversarial Machine Learning attacks in Intrusion Detection Systems
- Venue: Computers & Security (Elsevier)
- Link: DOI: 10.1016/j.cose.2023.103546
- Entry: Apollon: why intrusion detection systems demand robust and secure algorithms
- Properties:
[Robustness][Security]
2023 | Bearing Fault Diagnosis With Envelope Analysis and Machine Learning Approaches Using CWRU Dataset
- Venue: IEEE Access (IEEE)
- Link: DOI: 10.1109/ACCESS.2023.3283466
- Entry: Bearing fault diagnosis: why predictive maintenance demands interpretable features
- Properties:
[Accountability][Efficiency][Interpretability][Observability]
2020 | JGraphs: A Toolset to Work with Monte-Carlo Tree Search-Based Algorithms
- Venue: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (World Scientific)
- Link: DOI: 10.1142/S0218488520400115
- Entry: JGraphs: why interpretable AI demands algorithmic observability tools
- Properties:
[Accountability][Interpretability][Observability]