A researcher who ships.
I am a researcher and ML/AI executive — a PhD astrophysicist by training, now working across forecasting, mathematical optimization and causal AI. Alongside a research collaboration with Stanford ICME and System X, my work spans 30+ applied research projects across industrial, financial and regulatory domains.
Four disciplines, one translation problem.
Forecasting & Mathematical Optimization
Time-series and ensemble methods, causal inference, and self-developed optimization algorithms for pricing, procurement and hedging decisions.
RAG, LLM & Causal Intelligence
Retrieval-Augmented Generation systems that convert unstructured news into queryable, causal market knowledge.
AI & R&D Strategy
Conceiving and prioritising enterprise AI initiatives, and building research-to-production pipelines with a focus on scalability and ROI.
Enterprise Architecture & Governance
Cloud and MLOps infrastructure, regulatory modelling (Basel/EBA, EU AI Act), and the interface between mathematical research and C-level stakeholders.
Every principle links to the project where you can see it in practice.
- 01
Start from the decision
A forecast is only as good as the choice it improves. Work begins with the decision it has to support — buy now or wait, reroute or hold, allocate or not.
Seen in ForecastLens
- 02
Validate against known truth
Every method is checked on data where the answer is known before it touches real data: synthetic presets, independent numerical schemes, explicit causal ground truth.
Seen in diffusim
- 03
Prefer structure you can audit
Causal graphs with provenance on every edge, domain-neutral contracts and interpretable operators — so a result can be traced, questioned and trusted.
Seen in chronocause
- 04
Break it on purpose
Leak-safe walk-forward backtests, operator ablations, regime-by-regime scoring — and negative results documented instead of hidden.
Seen in Supply Chain Digital Twin
- 05
Report what it is worth
Results in the units decision-makers use: savings per unit, replans avoided, interval coverage that actually matches its promise.
Seen in Cat Pathfinder Optimizer
The short version.
- Training
- PhD, Astrophysics
- Research collaboration
- Stanford ICME & System X, since 2022
- Intellectual property
- Granted patent LU103146 — ML-based demand forecasting
- Practice
- 10+ years across banking, aerospace and industrials