Information Graphs of Statistical Summaries
Structural summaries that encode statistical patterns and dependencies in interpretable graph representations.
Read moreI build reliable software, AI systems, and data platforms for organizations operating in real-world, low-connectivity, and high-stakes environments.
Current focus

Research statement
My work connects mathematical reasoning, machine learning, and software engineering to solve real problems in data, institutions, and decision systems. I focus on explainable AI, statistical rigor, and scalable architecture to build tools that are both technically strong and operationally useful.
Selected research
Structural summaries that encode statistical patterns and dependencies in interpretable graph representations.
Read moreMachine-learning pipelines that use topological features to detect economic regime transitions.
Read moreResearch notes exploring reduction techniques and additive identities in analytic and arithmetic settings.
Read moreA content infrastructure connecting publications, notes, and technical artifacts into a coherent research system.
Read morePublications
Idriss Olivier Bado
Afrika Statistika • 2025
Idriss Olivier Bado
Preprint • 2026
Projects
A pipeline combining topological data analysis and machine learning to understand economic transitions and structural regime changes.
A content and research platform architected to manage publications, notes, and professional knowledge in one coherent system.
Research areas
Arithmetic structure, modular identities, and analytic methods that connect classical number theory to computational modeling.
Geometric invariants and topological structures that reveal continuity, shape, and stability in complex systems.
Persistent homology and geometric summaries for feature extraction from complex data ecosystems.
Statistical inference, summarization, and probabilistic modeling for uncertain, high-dimensional systems.
Statistical learning and predictive modeling with a focus on structure-aware, explainable, and rigorous systems.
Data pipelines, platform design, and scalable systems for reliable scientific and operational analytics.
AI methods grounded in mathematical structure, reliability, and scientifically interpretable outputs.
Current work
Long-form mathematical writing combining technical argumentation with formal notation and structured references.
Interpretable pipelines for statistical summaries, regime detection, and topology-driven feature engineering.
Production-oriented systems that connect mathematical ideas to reliable, auditable software artifacts.
Professional snapshot
Professional profile