Lead Data Scientist · AI Engineer · Professor · Mathematical Engineer
Working between the United Kingdom and Portugal · Python (typed, NumPy-first) · production AI + reproducible research

"Knowledge is knowing a tomato is a fruit; wisdom is not putting it in a fruit salad." — Miles Kington

I build production systems that turn complex data into reliable decisions, and reproducible research pipelines that turn open data into auditable evidence. My work spans end-to-end AI systems (LLMs, RAG, and agents), forecasting and anomaly detection, and a growing body of econometric and dynamical-systems research on inequality, wealth, and public policy. When a method I need is missing from the Python ecosystem, I build it, test it against the reference implementation, and publish it. Across all of it the constants are the same: lean models, robust software practice, and results you can reproduce and defend.

• Production AI & LLM systems
  RAG pipelines, agentic workflows, structured outputs, evaluation loops, and audit-friendly narrative reporting — designed for reliability, observability, and CI from the start.
• Data science & statistical modelling
  The full toolkit: supervised and unsupervised learning, causal inference and experimentation, survival analysis, Bayesian modelling, and robust/heavy-tailed statistics.
  With a working emphasis on the parts most people skip — uncertainty quantification and calibration, class imbalance, cost-sensitive decision thresholds, interpretability and fairness, leakage and drift checks, and honest model selection under real-world noise.
• Forecasting & anomaly detection
  Classical and foundation-model time series (SARIMAX/Prophet through Chronos and masked-patch transformers), conformal prediction intervals, change-point and rare-event detection, extreme-value methods for pre-failure signals, and drift monitoring for operational and sensor-driven systems.
• Econometrics & reproducible policy research
  Panel and causal models, event studies, synthetic control, configurational methods (csQCA/fsQCA), and Monte Carlo simulation over open economic data (Eurostat, OECD, AMECO, WID, INE/PORDATA, FRED) — including replication and extension of published results under explicit provenance labels.
• Optimisation & decision systems
  Turning forecasts into decisions: MILP unit commitment for hybrid power systems, perishable inventory and replenishment policy, project scheduling under financial and fuzzy uncertainty, and cost-weighted operating thresholds.
• Dynamical systems & scientific computing
  Attractor dynamics, dynamical-systems econometrics, extreme-value and rare-event methods, physics-informed neural networks and PDE solvers, and exact algorithmic solvers.
• Data & ML engineering
  Contract-linked ingestion, dataset curation, streaming and lakehouse patterns, and reproducible project scaffolding.

→ Full stack and model families on the Methods tab.

• Production RAG and agentic systems with evaluation, observability, and CI baked in
• Reproducible econometric research on inequality, wealth concentration, and public policy — including dynamical-systems models of rentier equilibria and crisis dynamics
• Portuguese and European economic history and public finance: minimum wages against productivity and inflation since 1974, the public-debt interest burden, and external growth linkages in 1960–1973
• Configurational methods in Python — a native, typed csQCA/fsQCA implementation validated against the reference R package, and a survey-design-aware fsQCA study of firm innovation across the EU-27
• Replication-grade empirical work: rebuilding published results with provenance labels, frozen artifacts, and adversarial release gates rather than transcribed tables
• Robust forecasting, anomaly detection, and drift control for operational data
• Reusable research infrastructure: contract-linked ETL, GitHub Actions tooling, and reproducibility contracts across multi-paper programmes

Seven projects that cover the range. The full catalogue — around 70 repositories across AI, ML engineering, deep learning, data engineering, statistics, economics, optimisation, algorithms, and tooling — is on the Projects tab.

• feedback-intelligence-agent — Production-style RAG system: a customer feedback intelligence agent with FastAPI, evaluation, observability, and CI.
• ragops-lab — Evaluation-first RAG and LLMOps platform for production-grade document QA: tracing, regression testing, and cost-aware experimentation.
• clinic-forecasting-platform — Healthcare demand-forecasting and staffing platform: a 13-model benchmark (SARIMAX, Prophet, gradient boosting, Nixtla, Chronos) with conformal intervals, rolling-origin backtesting, and FastAPI serving.
• transaction-risk-lakehouse — Production-style PySpark lakehouse for transaction-risk modelling, fraud detection, and temporal model validation.
• setqca — Native, typed Python implementation of crisp-set and fuzzy-set QCA with exact Boolean minimisation — not an R wrapper. Conservative, parsimonious, and intermediate solutions match the reference R QCA package on the canonical Lipset datasets, with the single divergence documented rather than hidden; published on PyPI with a DOI.
• poverty_neoliberalism_research_program — Agent-first scaffold for a ten-paper empirical programme on poverty, wages, taxes, and asset power in the US and UK since 1950, sharing one pipeline and reproducibility contract across all papers.
• bmssp ⭐ — Deterministic Single-Source Shortest Paths solver for directed graphs with non-negative weights, using a BMSSP-style divide-and-conquer design (typed, tested).

Live dashboards: Portugal Economic Indicators · NASDAQ Stock Analytics

I research inequality and political economy, applied econometrics and empirical finance, configurational methods, dynamical systems, production AI, time series and anomaly detection, and survival analysis — mostly organised as multi-paper programmes with a shared pipeline and reproducibility contract. I teach mathematics and data subjects at ESMAD (Instituto Politécnico do Porto) and run seminars on MLOps, streaming analytics, experimentation, and forecasting.

→ Research & Collaboration for research themes and what I am open to working on.
→ Teaching for courses, workshops, and course repositories.

When reaching out, include a short note on your use case, constraints, and timeline so we can assess fit quickly.

• Production AI: shipped RAG and agent systems (feedback-intelligence-agent, ragops-lab, ai-incident-analysis-agent) — with evaluation, tracing, observability, and CI treated as first-class.
• ML & data engineering: built serving, MLOps, and lakehouse/streaming platforms (fastapi-ml-platform, feature-store-lab, transaction-risk-lakehouse, pyflink-fraud-detection-streaming, llm-data-platform) spanning inference, drift monitoring, feature parity, and contract-linked ingestion.
• Research programmes at scale: launched a cluster of reproducible econometric and dynamical-systems projects on inequality, wealth, and policy, several sharing a single pipeline and reproducibility contract across many papers — extended this half into Portuguese economic history and public finance (portugal-minimum-wage-inflation, portugal-public-debt-interest, portugal-external-growth-1960-1973) replication-grade empirical asset pricing (short-rate-anomaly-regimes), and methodological work on simulated-likelihood asymptotics for multidimensional diffusions (sml_diffusions_paper).
• Published research software: setqca on PyPI — a native csQCA/fsQCA implementation validated against the reference R package — alongside gen_surv and typed, tested solvers and libraries (bmssp, min_ratio_cycle, heavytails, dynamical_systems_econometrics), with several repositories archived under Zenodo DOIs.
• Decision & optimisation systems: built simulators and policy layers that end in a decision, not a metric (energy-system-simulator unit commitment, perishable-inventory-decision-lab replenishment policy, scania-aps-cost cost-weighted maintenance thresholds).
• Scientific & deep-learning research: ran reproducible architecture and PDE labs with baselines, ablations, and audited claims (modern-neural-networks-agent-repo, anomaly-transformer-lab, pinn, pinn-rk, torch-namo-optim).
• Reusable infrastructure: built developer tooling (smart-todo-action, git-actions-collection, repo-task-tracker, article-reminders) to keep a large, multi-repo research output reproducible and maintainable.

Contribution activity across public and private work: