input-output-hk/ouroboros-leios
Feb 2025 – Feb 2025
1 Month active
Languages Used
PythonRust
Technical Skills
Data ModelingNetwork AnalysisOptimization AlgorithmsProbabilistic ModelingPython ScriptingRust Programming
Roland Kuhn
PROFILE
Roland Kuhn
Roland Kuhn developed a Delta-Q diffusion analysis module for the input-output-hk/ouroboros-leios topology checker, replacing basic hop-count methods with a probabilistic model for information diffusion. He refactored latency calculations to support ΔQ modeling, enhancing the accuracy and maintainability of network analysis. By integrating optimization algorithms and new ΔQSD analysis modules, Roland enabled the system to fit models directly to topology data, supporting more reliable assessments of network resilience. His work, implemented in Python and Rust, demonstrated depth in data modeling and probabilistic modeling, resulting in a more robust and data-driven approach to evaluating and optimizing network performance.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,095
Activity Months1
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered Delta-Q Diffusion Analysis for the Ouroboros-Leios topology checker, introducing a probabilistic diffusion model to replace simple hop-count representations. Refactored latency calculations to support ΔQ modeling, added new ΔQSD analysis modules, and integrated optimization libraries to fit models to topology data. This work enhances accuracy of information diffusion modeling, enabling more reliable topology assessments and data-driven optimization decisions, thereby improving network resilience and performance.
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered Delta-Q Diffusion Analysis for the Ouroboros-Leios topology checker, introducing a probabilistic diffusion model to replace simple hop-count representations. Refactored latency calculations to support ΔQ modeling, added new ΔQSD analysis modules, and integrated optimization libraries to fit models to topology data. This work enhances accuracy of information diffusion modeling, enabling more reliable topology assessments and data-driven optimization decisions, thereby improving network resilience and performance.
February 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonRust
Technical Skills
Data ModelingNetwork AnalysisOptimization AlgorithmsProbabilistic ModelingPython ScriptingRust Programming
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