Paolo Dai Pra

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foto, October 2, 2019
Position
Full Professor
Academic sector
MATH-03/B - Probability and Mathematical Statistics
Research sector (ERC-2024)
PE1_13 - Probability

Office
Ca' Vignal 2,  Floor 2,  Room 9
Telephone
+39 045 802 7093
E-mail
paolo|daipra*univr|it <== Replace | with . and * with @ to have the right email address.

Office Hours

Orario di ricevimento: Lunedì ore 15:30 o su appuntamento. 

Curriculum

Paolo Dai Pra si occupa di Processi Stocastici e loro applicazoni alle Scienze Fisiche, Biologiche e Sociali. La maggior parte delle sue pubblicazioni recenti riguardano le seguenti tematiche:

1. Sistemi complessi nelle scieze biologiche e sociali
2. Proprietà geometriche e di scala dei processi stocastici
3. Convergenza all'equilibrio e disuguaglianze funzionali per sistemi di particelle interagenti
4. Giochi a campo medio

I principali risultati ottenuti comprendono:
- un principio di Grandi Deviazioni per sistemi disordinati;
- un teorema di dualità fra problemi di controllo stocastico e giochi dinamici;
- la prima dimostrazione della scala diffusiva del gap spettrale per processi zero-range;
- una parziale estensione a processi di salto della teoria di Bakry-Emery per diffusioni;
- l'introduzione di dinamiche dissipative con interazione a campo medio, e la dimostrazione del comportamento periodico collettivo in alcuni casi speciali.

 

Modules

Modules running in the period selected: 34.
Click on the module to see the timetable and course details.

Course Name Total credits Online Teacher credits Modules offered by this teacher
Master's degree in Data Science Probability for Data Science (2025/2026)   9    5 
Master's degree in Mathematics Statistical learning (2025/2026)   6    3  (Part I)
Bachelor's degree in Human Centered Medical System Engineering Mathematical Analysis II: applications and mathematical methods (2024/2025)   12  eLearning 2  METODI MATEMATICI E APPLICAZIONI
Bachelor's degree in Applied Mathematics Probability and Statistics (2024/2025)   9  eLearning 3  PROBABILITA'
2  ELEMENTI DI STATISTICA
Master's degree in Data Science Probability for Data Science (2024/2025)   9  eLearning 5 
Master's degree in Mathematics Statistical learning (2024/2025)   6  eLearning 3  PART I
Bachelor's degree in Applied Mathematics Stochastic systems (2024/2025)   6  eLearning 2 
Bachelor's degree in Applied Mathematics Probability and Statistics (2023/2024)   9  eLearning 4  PROBABILITA'
3  ELEMENTI DI STATISTICA
Master's degree in Data Science Probability for Data Science (2023/2024)   12  eLearning 6 
Master's degree in Mathematics Statistical learning (2023/2024)   6  eLearning 3  PART I
Bachelor's degree in Applied Mathematics Stochastic systems (2023/2024)   6  eLearning 2 
Master's degree in Mathematics Mathematics mini courses (2022/2023)   0     
Bachelor's degree in Applied Mathematics Probability and Statistics (2022/2023)   9  eLearning 2  PROBABILITA'
2  ELEMENTI DI STATISTICA
Bachelor's degree in Computer Science Probability and Statistics (2022/2023)   6  eLearning 2  (Teoria)
Master's degree in Data Science Probability for Data Science (2022/2023)   12  eLearning 6 
Master's degree in Mathematics Statistical learning (2022/2023)   6  eLearning 3  PART I
Bachelor's degree in Applied Mathematics Stochastic systems (2022/2023)   6  eLearning 2 
Bachelor's degree in Applied Mathematics Probability and Statistics (2021/2022)   9  eLearning 2  PROBABILITA'
2  ELEMENTI DI STATISTICA
Master's degree in Data Science Probability for Data Science (2021/2022)   12  eLearning 7 
Master's degree in Mathematics Statistical learning (2021/2022)   6  eLearning 3  PART I
Bachelor's degree in Applied Mathematics Stochastic systems (2021/2022)   6  eLearning 5 
Bachelor's degree in Applied Mathematics Probability and Statistics (2020/2021)   9  eLearning 6  PROBABILITA'
3  ELEMENTI DI STATISTICA
Master's degree in Data Science Probability for Data Science (2020/2021)   12  eLearning 4  (Parte I)
Master's degree in Mathematics Statistical learning (2020/2021)   6  eLearning 3  PART I
Bachelor's degree in Applied Mathematics Stochastic systems (2020/2021)   6  eLearning 5 
Bachelor's degree in Applied Mathematics Probability (2019/2020)   6  eLearning 6 
Master's degree in Mathematics Statistical learning (2019/2020)   6  eLearning 3  PART I
Bachelor's degree in Applied Mathematics Stochastic systems (2019/2020)   6  eLearning 5 
Bachelor's degree in Applied Mathematics Stochastic systems (2014/2015)   6    3  (Catene di Markov in tempo discreto)

Di seguito sono elencati gli eventi e gli insegnamenti di Terza Missione collegati al docente:

  • Eventi di Terza Missione: eventi di Public Engagement e Formazione Continua.
  • Insegnamenti di Terza Missione: insegnamenti che fanno parte di Corsi di Studio come Corsi di formazione continua, Corsi di perfezionamento e aggiornamento professionale, Corsi di perfezionamento, Master e Scuole di specializzazione.

Research groups

INdAM - Research Unit at the University of Verona
We collect here the scientific activities of the Research Unit of Istituto Nazionale di Alta Matematica INdAM at the University of Verona
Research interests
Topic Description Research area
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
Large scale interacting random systems This research field focuses on the study of complex systems composed of a large number of components that interact with each other according to probabilistic rules. The main goal is to understand how microscopic interactions give rise to the emergence of highly ordered or highly organized macroscopic collective behaviors, which are not easily predictable from the behavior of individual units. In more detail, the topics addressed include scaling limits, phase transitions, fluctuations, relaxation times, and applications to biology and social sciences. Mathematical methods and models
Stochastic analysis


Organization

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