DESIGN BY STATISTICAL INFERENCE
Conceptual design is no longer just an art of intuition: it is increasingly a science of prediction. By combining statistical inference, data mining and machine learning, it becomes possible to anticipate risks, optimize parameters and rethink engineering from the very first stages. This approach reduces errors, minimizes costs, and opens new ways to design complex systems more intelligently.
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Research & Projects Undertaken:
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Ship design optimization with data-driven simulation and neural networks
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Predictive modeling of hull resistance and speed profiles
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Probabilistic analysis of structural performance (e.g. concrete structures)
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Industrial product design enhanced by machine learning
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PROJECT RISK MANAGEMENT
Risk in large-scale projects is often approached with intuition or rigid percentage rules, leading to costly misestimations. By combining predictive analytics, actuarial methods, and open-source simulations, it is possible to model uncertainty in a dynamic, data-driven way. Instead of fixed contingencies, this approach creates adaptive strategies that better capture complexity, dependencies, and extreme events—helping organizations maintain budgets while embracing uncertainty.
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Research & Projects Undertaken:
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Open-source Monte Carlo simulations for project cost forecasting
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Factor analysis, clustering, and variable selection for contingency modeling
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Regression and distribution fitting for predictive cost analysis
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Advanced risk measures beyond traditional additivity
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Copula-based simulations to capture dependence between cost drivers
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Modeling Risk and Markets
Financial markets are among the most complex systems, shaped by uncertainty, interdependence and non-linear dynamics. By integrating quantitative finance, actuarial science and machine learning, I develop algorithms that go beyond traditional risk management—capturing hidden dependencies, stress scenarios and structural shifts. The goal is not only to optimize investment strategies, but to better understand the deep mechanics of risk and value creation.
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Research & Projects Undertaken:
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Data-driven approaches to asset allocation and risk management
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Advanced financial risk models (operational, credit, market)
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Derivative pricing models with stochastic processes
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Monte Carlo and copula-based simulations for dependency modeling
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Macro factor testing and panel data analysis for trading strategies
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AI FOR HEALTH & MEDICINE
Medicine is increasingly shaped by data, from epidemiology to clinical trials. Statistical inference, causal analysis and machine learning enable us to design studies, test hypotheses and uncover hidden patterns in biomedical data. By combining rigorous statistical methods with AI, it is possible to strengthen evidence, model uncertainty and support clinical decisions with deeper insight.
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Research & Projects Undertaken:
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Clinical study design and trial simulation
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Advanced statistical modeling (survival, logistic, hierarchical, non-parametric)
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Meta-analysis and causal inference
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Machine learning applications in biomedical data
CUSTOM AI & DATA SOLUTIONS
Vast amounts of data demand flexible, transparent, and reproducible tools. I design open-source software tailored to scientists, engineers and decision-makers—building bridges between statistical theory, AI, and real-world applications. Beyond automation, the aim is to create adaptive systems that support innovation and resilience in complex environments.
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