Architecting interpretable, production‑grade ML pipelines with research‑level reproducibility and deployment‑level reliability.
| Core Competence | Technologies | Evidence |
|---|---|---|
| End‑to‑End AI Pipelines | PyTorch · scikit‑learn · LangChain · TensorFlow | Docker hashes · CI logs |
| Explainability & Fairness | SHAP · Grad‑CAM · Fairlearn · Captum | PDF reports · Jupyter artefacts |
| Edge & Cloud Deployment | Spring Boot · FastAPI · TensorRT · AWS CDK · GCP · Azure Functions | Live demo endpoints |
| Verification Discipline | 82%+ coverage · static analysis · regression test harnesses | GitHub Actions · badges |
| DataOps & MLOps | DVC · MLflow · Airflow · Great Expectations | Auto-tracked experiments & validation |
Project Aletheia – a universal, cryptographically-auditable framework for fair online decision-making uniting ergodic control, convex geometry, stochastic processes, and zero-knowledge proofs, applied to housing and healthcare allocation.
Objectives 2026
• Prove an Ergodic Allocation Law ensuring almost-sure fairness for multi-resource scheduling (housing units, hospital beds) under adversarial arrivals
• Achieve O(√n log d) minimax regret with polylog-time mirror descent projections onto fairness polytopes
• Establish an Entropy–Fairness Duality Theorem with Banach–Mazur distortion ≤ O(log d) across convex bodies
• Ship ZK Allocation Checkpoints (≤ 50 ms verification, ≤ 2 MB proof size) enforcing GDPR/AI-Act compliance
• Deploy a reproducible Rust/Python simulator for Berlin–Munich–Hamburg housing allocation with real datasets + fairness-drift dashboard
• Publish formal Lean/Coq proofs, open-source code, and dataset cards
- Theorem-First Science – All guarantees (ergodicity, entropy duality, regret bounds) proven formally before coding
- Verifiable by Design – Every allocation decision emits a Halo2/PLONK ZK-certificate bound to logs and DP noise
- Geometry-Aware Optimization – Bregman divergences tuned to fairness polytopes; polylog-time oracle projections
- Stochastic-Control Guarantees – Lyapunov drift ensures ε-bounded violations across millions of allocations
- EU-Ready – Explicit AI-Act templates for DPIA, auditability, transparency, oversight
- Reproducible & Open – One-click CI, seeded runs, dataset cards, GitHub with >80 % coverage
siddquiaqib@gmail.com • linkedin.com/in/aqib-siddiqui-b954021b9 • leetcode.com/u/aqib_siddiqui_121201/
