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Curriculum Vitae (English)
(pdf, en, 825 KB, 27/02/25)
Curriculum Vitae (Italiano)
(pdf, it, 825 KB, 27/02/25)
Modules running in the period selected: 43.
Click on the module to see the timetable and course details.
Di seguito sono elencati gli eventi e gli insegnamenti di Terza Missione collegati al docente:
| Topic | Description | Research area |
|---|---|---|
| Intelligent agents | Design and development of autonomous entities that can sense, model and interact with the environment in which they operate. These area focuses on the interaction and integration of solution technques for several research topics such as automated planning and reasoning, reinforcement learning, statistical learning and reasoning in face of uncertainty. |
Artificial Intelligence
Distributed artificial intelligence |
| Stochastic partial differential equations and their applications | The research about (SPDEs and their applications spans a wide range of topics. With respect to theoretical contributions, we focus on fundamental aspects such as existence, uniqueness of solutions, invariant measures, and asymptotic expansions, with equations driven by general Lévy-type noises. Concerning applications, we consider mathematical finance problems exploiting SPDEs' methods to address challenges like option pricing under stochastic volatility, counterparty risk evaluation, and optimal execution strategies, often employing FBSPDEs and jump-diffusion models. Moreover, we consider control and optimization applications in memory-dependent systems, mean-field games, and stochastic control to manage uncertainty through dynamic programming and energy shaping. We also use SPDE techniques for electricity price forecasting, wind energy modelling, and control in robotics and teleoperation, emphasizing stochastic passivity and developing an innovative stochastic approach to port-Hamiltonian systems. Interdisciplinary applications extend to biomedicine, network dynamics, and interacting particle systems, showcasing the versatility of these mathematical tools in addressing complex problems in heterogeneous fields. |
Mathematical methods and models
Stochastic analysis |
| Mean field games and applications | In this research field, non-cooperative dynamic games with a large number of players are considered. With appropriate symmetry assumptions, the N-player game can be approximated by a limit game for a representative player, called a mean-field game. Such games are used to model the collective behavior of large communities. The research concerns theoretical aspects - existence and uniqueness of equilibria, convergence of the N-player problem… -, computational aspects - algorithms for finding approximate equilibria - and applications to different areas, in particular Social Sciences and Machine Learning. |
Quantitative Methods for Economics
Game theory, economics, social and behavioral sciences |
| Stochastic data-driven forecasting | Stochastic Data-Driven Forecasting focuses on integrating stochastic analysis with data-driven methods to enhance predictive accuracy in systems governed by random processes. By utilizing stochastic models, such as stochastic differential equations and time series with noise components, and calibrating them through machine learning on observed data, this field aims to yield robust probabilistic forecasts. Applications include dynamic systems in finance, climatology, and energy, where accurate uncertainty quantification is essential for predictive reliability and risk evaluation. |
Information Systems and Data Analytics
Stochastic Differential Equations |
| Problem solving in the context of artificial intelligence | The research fields covered by the Artificial Intelligence (AI) and Machine Learning we are interested in span various applications across finance, energy, and control systems. In finance, hybrid neural networks and deep learning are applied to forecasting, risk management, and investment optimization, including stock price prediction and volatility analysis. Energy systems benefit from AI-driven models for load forecasting, electricity price prediction, and renewable energy management. Stochastic control methods, enhanced by neural networks, address optimization challenges in dynamic and uncertain environments. Advanced neural architectures, such as recurrent networks and multitask learning, improve time series forecasting and domain-specific predictions. Interdisciplinary applications include biomedical engineering, where AI aids in analysing nanofluids, and robotics, where neural networks support motion control under stochastic dynamics. These studies emphasize the integration of AI to solve complex, high-impact problems. |
Mathematical methods and models
Stochastic analysis |
| Multi agent systems | Design and development of multiagent systems, where intelligent agents can interact among them, with the environment and with humans. This area focuses on the interaction and integration of solution techniques related to multiagent planning, statistical learning, multi-agent reinforcement learning and game theory. |
Artificial Intelligence
Distributed artificial intelligence |
| Office | Collegial Body |
|---|---|
| member | Faculty Board of Interuniversity PhD in Mathematics - Department Computer Science |
| member | Computer Science Teaching Committee - Department Computer Science |
| member | Computer Science Teaching Committe - Department Computer Science |
| Comitato scientifico del corso di aggiornamento in informatica ambientale - Department Computer Science | |
| member | Computer Science Department Council - Department Computer Science |
| componente senza diritto di voto | PhD School Board |
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