svalutoferro
univrit
Curriculum Vitae
(pdf, it, 156 KB, 24/01/25)
Curriculum Vitae_EN
(pdf, en, 157 KB, 24/01/25)
Sara Svaluto-Ferro è una Ricercatrice a Tempo Determinato (RTDB) presso il dipartimento di Scienze Economiche dell'Università di Verona.
Ha conseguito la laurea specialistica in Matematica e il dottorato di ricerca in Matematica Finanziaria presso il politecnico federale di Zurigo (ETH).
Successivamente ha ottenuto un contratto di post-dottorato di 3 anni presso l'Università di Vienna dove ha lavorato nel gruppo di prof. Cuchiero.
I suoi interessi di ricerca includono l'analisi stocastica finito e infinito dimensionale di processi continui o con salti, la ricerca di strutture universali con particolare interesse per i processi detti signature e la finanza computazionale.
Modules running in the period selected: 11.
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 |
|---|---|---|
| MSC 60G20 - Generalized stochastic processes | The field of Generalized stochastic processes extends classical stochastic process theory to include distributional objects which cannot be described as classical functions but only in a weak sense. These processes are defined as random distributions and generalize concepts such as Brownian motion and Lévy processes allowing the treatment of phenomena with singularities or extreme irregularities. Generalized stochastic processes have applications in theoretical physics (quantum fields) mathematical finance (volatility models with singular noise) signal processing and diffusion models in functional spaces. |
Quantitative Methods for Economics
Stochastic processes |
| MSC 60L10 - Signatures and data streams | The field of Signatures and data streams in Rough Analysis focuses on mathematical structures that efficiently capture the information contained in signals or temporal data. Signatures are fundamental tools in Rough Path Theory providing a compact and information-rich numerical representation of data trajectories regardless of their irregularity. By using signatures one can characterize the temporal evolution of a signal without losing essential information facilitating analysis prediction and machine learning on continuous data streams. This approach has applications in finance (for price modeling) natural language processing biomedicine and many other areas where data are sequential or temporal in nature. |
Quantitative Methods for Economics
Probability theory and stochastic processes |
| MSC 60L70 - Algebraic structures and computation | The field of Algebraic structures and computation in Rough Analysis explores the algebraic structures underlying irregular stochastic processes and develops computational methods for their analysis and application. A central role is played by signature algebras which provide an efficient representation of data trajectories via series of characteristic functions and Hopf algebras fundamental for the symbolic manipulation of series expansions in rough models. This research area has direct implications for numerical computations in rough path theory machine learning on sequential data and the development of efficient algorithms for analyzing complex signals in finance biology and physics. |
Quantitative Methods for Economics
Probability theory and stochastic processes |
| MSC 91B70 - Stochastic models | Stochastic modelling in economics and finance, focusing on dynamic systems that evolve over time under uncertainty; development of probabilistic frameworks for market behavior, risk assessment, and decision-making; applications of stochastic processes and stochastic control to model temporal variations and optimize strategies. |
Quantitative Methods for Economics
Mathematical economics |
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