“The creation of mathematical/statistical models and the development of algorithms for computer simulation to obtain solutions of problems in industry is what we call industrial mathematics […] How does industrial mathematics differ from “applied mathematics”? First, you must travel to industry and talk to its scientists and engineers in order to identify their mathematical problems […] Mathematicians in industry are viewed as having highly developed skills in abstraction, analysis of underlying structures, and logical thinking; as having the best tools for formulating and solving problems. They are often viewed as consultants…”
—Avner Friedman, Pure, applied, and industrial mathematics: strength through connections. Lectures on Applied Mathematics (Munich, 1999), Springer, 2000, pp. 4–5.
My research connects applied analysis with problems in finance, engineering, and the life sciences, aiming to turn analytical insight into effective tools for modeling, prediction, and decision-making.
I am available to co-supervise internships, industrial placements, and Bachelor’s or Master’s theses in applied mathematics or engineering, in collaboration with companies, research laboratories, and public institutions. Below are some current topics of interest.
Applied Fluid Dynamics
Mathematical models of fluid flow, transport phenomena, dissipation, and mixing are used for design and optimization in biomedical engineering, aerodynamics, energy systems, environmental engineering, and industrial processes.
Financial Mathematics and Quantitative Modeling
Stochastic modeling, partial differential equations, and optimization methods are applied to problems in portfolio management, risk assessment, pricing in evolving markets, production planning, and resource allocation.
Machine Learning and Control Theory
The interface between machine learning and control theory combines data-driven efficiency with stability, robustness, and interpretability.
Biomedical and Forensic Statistics
Statistical and computational methods are applied to biomedical and forensic data, with emphasis on clinical trial analysis, disease progression modeling, imaging biomarkers, and evidence quantification.
Nicola De Nitti 