About

Daniele is a Mathematical Engineer pursuing a PhD in dynamic optimization at Politecnico di Torino funded by the Politecnico itself, the Università degli Studi di Torino, the Fondazione CRT as well as other major mathematical associations: the Consorzio Interuniversitario per l’Alta Formazione in Matematica and the Istituto Nazionale di Alta Matematica. His application context deals with decision making under uncertainty in engineering/management applications. Some examples are retail management and assortment planning, robust machine learning and stochastic programming.Currently, he is working as visiting researcher at the Technical University of Munich for a collaborative research project in the area of multi-echelon perishable inventory management at the logistics and supply chain management department.Daniele earned his master's degree in mathematical engineering at Politecnico di Torino and Technische Universiteit Eindhoven summa cum laude. During his master, he was a post-graduate research assistant for the development of software for the generation of a polyhedric mesh in domains with randomly generated interfaces for F.E.M. Whilst studying, he worked as an academic tutor in various subjects, including programming and scientific computing and numerical methods.When he is not modelling by equations, Daniele spends his time working on his trumpet playing skills and improving his expertise as a fitness trainer by courses and self-study. He earned a pre-academical certification in trumpet at the superior institute of musical studies of Caltanissetta.

The energy problem related to the pursuit of alternatives to fossil fuels is an active challenge for the entire world. Unlike other types of energy conversion technologies, such as photovoltaic plants or wind turbines, wave energy converters (WECs) have not reached a sufficient level of technological maturity and two of the most crucial issues are: the development of suitable advanced control strategies for WEC devices and the optimal design in terms of cost and extracted energy.
Daniele's current research focuses on strategies that apply a Gaussian Process Regression (GPR) optimization approach to compute the control action and surrogated techniques to robustify the design against mechanical mismatches.
He is the author of the article: Data-driven control of a Pendulum Wave Energy Converter: A Gaussian Process Regression approach, published by Ocean Engineering, a top-ranked journal in marine engineering. He also co-authored the conference proceedings Wave Energy Converter Optimal Design Under Parameter Uncertainty presented at the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering.

Assemble-to-order is a production strategy where components are manufactured under demand uncertainty and end items are assembled only after demand is realized. This strategy is commonly applied to hedge against significant uncertainties in the order of the end items, naturally leading to Two-Stage and Multi-Stage Stochastic Programming formulations.
Daniele's research aims to apply reinforcement learning strategies to reduce the Multi-Stage complexity and study different multistage multi-item models, showing how
they behave, based on the usage of the information available.
A PrePrint that deals with seasonality, bimodality, and correlations in the distribution of end items demand, where the approximation of terminal values and rolling horizon simulations are applied is available on ArXiv. The code is open-source and available on GitHub.

The new sustainable and development goals are considered a blueprint to achieve a better and more sustainable future for all. Reduction of losses and overstocking on supply chains and retail environments are central topics, even more, when the considered items have a short life (i.e. food, blood platelets). Daniele's current research deals with advanced inventory management control systems that employ a plethora of methods, ranging from simple Order-Up-To rules to deep reinforcement learning.
A study that allows for a multi-item setting with substitution between similar goods, deterministic deterioration, delivery lead times and seasonality was presented in Nantes at the 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM, with title Inventory management of vertically differentiated perishable products with stock-out based substitution. The simulation environment is available on GitHub.

The academic world is full of research that aims to optimize financial portfolios in commodity or stock markets. The information sources and the related methodologies to extract insights are countless. Moment-based methods like the well-known Markowitz's model have a large number of model parameters and, in addition to requiring considerable computational effort, raise serious questions about the reliability of these values. The current research of Daniele undertakes the building of decision support systems able to make usable the newest deep learning strategies for financial decision-makers, enhancing them with known instruments like technical and fundamental analysis, and attempting to overcome some of the inherent complexities and limitations of the most classical strategies. An academic article titled Early portfolio pruning: a scalable approach to hybrid portfolio selection where he presents a hybrid approach that combines itemset extraction with portfolio selection and where Markowitz’s model logic is adapted to deal with sets of candidate portfolios rather than with single stocks has been published in Knowledge and Information Systems by Springer Nature.

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