Teaching

We provide courses related to the mathematical foundations of data science in the Master in Mathematics organized by the department of Mathematics at the University of Trento. In particular, the track Mathematics for Data Science, within the Mathematics and Statistics for Life and Social Sciences curriculum, aligns closely with the research activity of our group.

Track: Mathematics for Data Science

Students who take this track will have the opportunity to learn the theoretical and computational foundations of Mathematics for Data Science, including advanced tools in:

  • πŸ“Š Probability and Mathematical Statistics
  • πŸ€– Machine Learning and Deep Learning

The track equips students with the knowledge and skills needed to tackle challenges in modelling high dimensional and complex data sets, which are frequently encountered in environmental, biological, social, and economic fields.

πŸŽ“ Career Path

This track is ideal for students seeking a Ph.D. in Statistics, Data Science, Applied Mathematics, Machine Learning, or AI, or a job in data analysis departments in industry or research centers.


Student Testimonials

Mauritz Cartier van Dissel

Mauritz Cartier van Dissel

I chose my Master’s, and in particular the track Mathematics for Data Science, because I was interested in applying the knowledge I acquired during my Bachelor’s to data analysis, while also maintaining a theoretical perspective.
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1. What's your name and where are you from? My name is Mauritz, and I come from a small village in Val di Non, a valley near Trento.
2. What did you study before the master? Before my Master’s degree, I pursued a Bachelor’s in Mathematics, also in Trento.
3. Why did you choose this master? I chose my Master’s, and in particular the track Mathematics for Data Science, because I was interested in applying the knowledge I acquired during my Bachelor’s to data analysis, while also maintaining a theoretical perspective.
4. What do you consider to be the strong points of the master? For me, it provides a strong statistical foundation, introduces you to the world of data analysis, teaches you many different programming languages, offers the opportunity to follow the courses that better suit you from a variety of options, and prepares you for both a data analyst job and a PhD position.
5. What did you like about the master programme? I really enjoyed my time during my Master’s: I learnt how to apply mathematics to the solution of real problems, found a great interest in Network Science, met some very good friends, and had a wonderful time abroad during my Erasmus Traineeship program.
6. What did you do immediately after the master? Immediately after my studies, I decided that I wanted to continue to work in research and looked for open positions in the field of Network Science.
7. What are you doing right now in terms of work? I am currently enrolled as a Young Researcher at the Complexity Science Hub in Vienna, Austria, where I am studying the emergence of inequalities in Social Networks.
8. Would you recommend the Masters to someone? To whom in particular? I would definitely recommend the Master’s to anyone with a strong mathematical background and passion in programming, that is looking to apply his/her knowledge to solve real problems using data.
Gaia Colombani

Gaia Colombani

I was looking for a master's degree in data science while remaining in a mathematics department, and Trento offers this opportunity. Furthermore, it is close to the mountains and ski resorts.
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1. What's your name and where are you from? Hi, I'm Gaia and I'm from Milano, Italy.
2. What did you study before the master? I studied Mathematics at UniversitΓ  di Milano-Bicocca.
3. Why did you choose this master? I was looking for a master's degree in data science while remaining in a mathematics department, and Trento offers this opportunity. Furthermore, it is close to the mountains and ski resorts.
4. What do you consider to be the strong points of the master? The strong point of this course is the large number of courses you can choose from. Most of the professors are very enthusiastic and open to discussing with the students.
5. What did you like about the master programme? I appreciated the different exam methods and the group that was created with the other students.
6. What did you do immediately after the master? I started looking for a job.
7. What are you doing right now in terms of work? I'm working as a business analyst in a software company. I do not work with data directly, but I find this job very challenging and I like it. The world outside university is very different from that of the university. There is a lot to learn but the university prepares you for that.
8. Would you recommend the Masters to someone? To whom in particular? I would recommend this course to anyone who wants to continue their studies in mathematics, particularly with a focus on probability and statistics.
Lucia Filippozzi

Lucia Filippozzi

If you feel passionate about Mathematics but at the same time you would like to dive deeper into real-life problems and applications, then I would totally recommend this Master!
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1. What's your name and where are you from? My name is Lucia. I am Italian, I grew up in a small countryside village in the province of Verona, but I lived for five years in Trento during my Bachelor's and Master’s degrees.
2. What did you study before the master? Before pursuing my Master's degree, I studied mathematics in the Bachelor's program at the University of Trento.
3. Why did you choose this master? I was looking for something that combined mathematical formalism with the potential for real-world applications. When I discovered the existence of this specific program - in particular, the track in Mathematics for Data Science -, it seemed like the perfect fit. And it was!
4. What do you consider to be the strong points of the master? I believe one of the strongest points of the Master is the wide range of courses one can choose from. Whether your interest lies more in biomedical applications, mathematical finance, complex biological systems, or data analysis, by selecting the appropriate track within this program, you would have the opportunity to delve into each of these fields. Another key strength, in my opinion, is the relatively small class sizes of this master’s program. This means that you get the opportunity to directly interact with professors, creating excellent chances to build connections and really delve into their expertise and research areas.
5. What did you like about the master programme? It's challenging for me to answer this question, mainly because at that time, during my years in the master's program, I would have simply said that I enjoyed the environment and the opportunity to interact with people that felt passionate about the same things I did. However, right now, I feel more capable of offering a broader perspective, and I would say that after having worked in research, I actually believe that the education provided in Trento has been truly remarkable. Some courses were designed not to give you detailed knowledge but, in my opinion, to equip you with as much general information as possible. This way, if you ever find yourself studying or getting passionate about a specific research field, you have all the tools to delve deeper into what you want. Other courses were more technical and specific. Furthermore, when I took some courses, many exams were in the form of homework - projects with tight deadlines. At first, I wasn't thrilled about it, but I must say that they taught me a lot: they helped me to approach the subject with a different perspective, more focused on real-world problems, and prepared me for handling multiple deadlines, a common occurrence in a professional research setting. Interacting with people from other countries or different Italian universities, I also realized that receiving this kind of education was not that common and I really feel this made a difference in my job as a researcher in academia.
6. What did you do immediately after the master? During the last semester of my Master degree, I had the opportunity to do an internship in a research center in Bilbao (BCAM - Basque Center for Applied Mathematics) to conduct a research project in Machine Learning. Right after this, while I was still writing my master's thesis, I started working in the same organization as a full-time researcher.
7. What are you doing right now in terms of work? Currently, I am working as a full-time researcher at BCAM - Basque Center for Applied Mathematics (in Bilbao - Spain) in the Machine Learning group. But soon, I will start a PhD at the University of Trento in collaboration with FBK and BCAM, as well.
8. Would you recommend the Masters to someone? To whom in particular? If you feel passionate about Mathematics but at the same time you would like to dive deeper into real-life problems and applications, then I would totally recommend this Master! I am confident you will enjoy it and have the chance to build great connections with your fellow students, as well as some researchers and professors. Hope to see you around!

Master theses

2025

Tommaso Rondani

πŸŽ“ Thesis: Joint relational event modelling of alien species invasions

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Ernst Wit

2025

Alessia Moretti

πŸŽ“ Thesis: Investigating the curse of dimensionality in Bayesian cluster analysis: A study of Gaussian mixtures with diagonal covariance matrix

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Sara Wade

2025

Gaia Benedetti

πŸŽ“ Thesis: Functional generalized linear mixed models: theory and application in epileptic data analysis

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Annalisa Barla

2025

Paul-Henrik Heilmann

πŸŽ“ Thesis: Robust estimators for generalized linear models with density power divergence: computational aspects and algorithms

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Andreas Prohl

2024

Silvia Menchetti

πŸŽ“ Thesis: Enhancing predictive modeling performance with omics data augmentation through generative adversarial networks and diffusion models

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Marco Chierici

2024

Ester Riccardi

πŸŽ“ Thesis: Introducing causality in relational event models

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Ernst Wit

2024

Laura Bisoffi

πŸŽ“ Thesis: The emergence of zealotry in opinion dynamics

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Lucilla Alvarez-Zuzek, Oriol Artime

2024

Francesco Frattini

πŸŽ“ Thesis: Copula graphical models with node degree heterogeneity

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti

2024

Lorenzo Zuccato

πŸŽ“ Thesis: Bayesian nonparametric clustering for preference learning

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Valeria Vitelli

2024

Anna Galli

πŸŽ“ Thesis: Statistical properties of the spatial sign autocorrelation coefficient for stationary alpha-mixing processes

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Alexander Durre

2024

Aurora Vindimian

πŸŽ“ Thesis: Analyzing and modelling the temporal network of telegram's chats

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Riccardo Gallotti, Thomas Louf

2024

Seyed Mohsen Moosavi

πŸŽ“ Thesis: Some applications of ergodic theory in deep neural networks

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2024

Elisa Muratore

πŸŽ“ Thesis: Marked temporal point processes for simulating and capturing coordinated behaviour campaigns: a model for enhancing disinformation detection

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Riccardo Gallotti, Letizia Iannucci, Mikko Kivela

2024

Valentina Fusaro

πŸŽ“ Thesis: Exploring multimessenger signals: a joint Bayesian framework for neutron stars mergers

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Sebastiano Bernuzzi, Albino Perego

2024

Chiara Maggiolini Cacciamani

πŸŽ“ Thesis: Robust penalized estimators for high-dimensional generalized linear models with density power divergence

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Abhik Ghosh

2024

Nicola Potenza

πŸŽ“ Thesis: An overview on non parametric Bayesian statistics and its application to artificial neural networks

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2023

Alice Polinelli

πŸŽ“ Thesis: Causal regularization and its extension to generalized linear models

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Ernst Wit

2023

Veronica Poda

πŸŽ“ Thesis: Comparing modularity-based approaches for community detection on hypergraphs

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Catherine Matias

2023

Marco Borriero

πŸŽ“ Thesis: Gaussian graphical models for partially observed functional data

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Luigi Augugliaro

2023

Silvia Guerrini

πŸŽ“ Thesis: Dynamic network insights: a novel model for disentangling homophily and triadic closure in longitudinal data

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Tiago peixoto

2023

Alessandro Cortese

πŸŽ“ Thesis: The COVID-19 infodemic on twitter: exploring patterns and dynamics across countries

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Riccardo Gallotti

2023

Sara Maria Norina Zuffi

πŸŽ“ Thesis: Imbalances in the Italian electricity market: analysis and prediction with ML-based methods

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti

2023

Melania Lembo

πŸŽ“ Thesis: Nested case-control sampling for baseline hazard estimation in relational event models

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Ernst Wit

2023

Luca Cibinel

πŸŽ“ Thesis: Penalised likelihood inference for gaussian covariance graph models

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Alberto Roverato

2023

Giada Dal Col

πŸŽ“ Thesis: Analytical properties and statistical inference in ranking dynamics

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Oriol Artime, Riccardo Gallotti

2023

Claudiu Acsinte

πŸŽ“ Thesis: An application of information geometry in machine learning: the Helmholtz machine case

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Luigi Malago'

2023

Federica Forzanini

πŸŽ“ Thesis: Data resampling strategies for robust and reproducible machine learning models in biomedical domain: an application on medical imaging data of amyotrophic lateral sclerosis (als) patients

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Annalisa Barla

2022

Mauritz Cartier van Dissel

πŸŽ“ Thesis: State-space models for multivariate electricity load forecasting

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Santiago Mazuelas

2022

Gaia Colombani

πŸŽ“ Thesis: First-passage properties of growing self-avoiding random walks with and without resetting

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Oriol Artime, Giulia Bertagnolli

2022

Marco Sigala

πŸŽ“ Thesis: Parallel heuristic optimization algorithms for waste collection problems

πŸ‘©β€πŸ« Supervisors: Veronica Vinciotti, Francesca Cipollini

2022

Lucia Filippozzi

πŸŽ“ Thesis: Minimax risk classifier with noisy labels

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Santiago Mazuelas

2022

Yuri Baldo

πŸŽ“ Thesis: Temporal-difference learning methods with eligibility traces

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Luigi Amedeo Bianchi

2022

Anna Bertani

πŸŽ“ Thesis: Characterization of social behavior dynamics in online social network during exceptional events

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Manlio De domenico

2022

Giorgia Rossato

πŸŽ“ Thesis: Toward functional data analysis on torus

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2022

Chiara Avigo

πŸŽ“ Thesis: Variational inference methods for bayesian neural networks: towards uncertainty estimation of neural networks predictions

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2022

Sebastian Raimondo

πŸŽ“ Thesis: Network models for large-scale human mobility

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Manlio De domenico

2022

Giacomo Francisci

πŸŽ“ Thesis: Local depth functions and applications to clustering

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2022

Tobia Filosi

πŸŽ“ Thesis: Tree-ring based reconstruction of paleoclimate via Bayesian state space models

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2021

Giuseppe Saccardi

πŸŽ“ Thesis: Additive covariance modeling via unconstrained parametrization

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Matteo Fasiolo

2021

Irene Bianconi

πŸŽ“ Thesis: Importance sampling with application in sparse linear models

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Nicolas Chopin

2021

Clara Lupo

πŸŽ“ Thesis: Multi-dimensional count data. a Bayesian nonparametric model for multi-dimensional count data

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Antonio Canale

2021

Francesca Onorato

πŸŽ“ Thesis: Segmentation strategies for end-to-end simultaneous speech translation

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Matteo Negri, Elisa Ricci, Marco Turchi

2021

Massimiliano Datres

πŸŽ“ Thesis: The mathematical foundation of the probabilistic compressed sensing approach

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2021

Andrea Fox

πŸŽ“ Thesis: Bayesian methods for tabular reinforcement learning

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2021

Andrea Panarotto

πŸŽ“ Thesis: Hamiltonian Monte Carlo for random sampling and generative models applications

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2021

Carlo Pietropoli

πŸŽ“ Thesis: Hidden Markov models for football analytics

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Roberta Sirovich

2021

Elena Sabbioni

πŸŽ“ Thesis: Comparing groups with high-dimensional semi-continuous data: the case of micro-RNA activation in human blastocysts

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Alessio Farcomeni

2021

Angela Fabian

πŸŽ“ Thesis: An application of continuous time Bayesian networks: a model for structured Poisson processes

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2020

Giorgia Casaccio

πŸŽ“ Thesis: Statistical data depth based measures for networks: dispersion and core-periphery structures

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Manlio De domenico

2020

Mattia Brazzale

πŸŽ“ Thesis: Modelling and statistical analysis of silicon-based photonic quantum random number generators

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Sonia Mazzucchi

2020

Stefano Arrigoni

πŸŽ“ Thesis: Markov chain for disability insurance models

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2020

Francesco Cabras

πŸŽ“ Thesis: Electricity bills: analysis of the differences in price for european households

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Luigi Amedeo Bianchi

2019

Ivan Marino

πŸŽ“ Thesis: Mixture models for grouped data

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2019

Michele Pedrazzoli

πŸŽ“ Thesis: Adversarial examples from a robust statistics point of view

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2018

Giulia Bertagnolli

πŸŽ“ Thesis: Complex networks and statistical data depths

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Massimiliano De Franceschi

2017

Viola Nervini

πŸŽ“ Thesis: Wavelets and convolutional neural networks for food quality control

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Giuseppe Jurman

2017

Giovanni Saraceno

πŸŽ“ Thesis: Deep learning and memorizing of spectro-temporal data (music) in the spatio-temporal brain

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Cesare Furlanello, Nikola Kasabov

2017

Giulia Gangi

πŸŽ“ Thesis: Deep CNN for grape detection

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2016

Lucia Trastulla

πŸŽ“ Thesis: Techniques of integration for high-throughput omics data

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Giuseppe Jurman, Alessandro Zandona'

2016

Veronica Vena

πŸŽ“ Thesis: Singular spectrum analysis: an application to air pollution data

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2016

Manlio Valenti

πŸŽ“ Thesis: On the notion of Kolmogorov complexity and the computability of learning agents

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli

2016

Ylenia Giarratano

πŸŽ“ Thesis: Phylogenetic convolutional neural networks in metagenomics

πŸ‘©β€πŸ« Supervisors: Claudio Agostinelli, Giuseppe Jurman