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:

📊 Probability and Mathematical Statistics
🤖 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.

⚙️ Prerequisites for Admission

Knowledge of probability theory and mathematical statistics is essential. Please check the syllabus of the courses below (Bachelor’s degree in Italian) to ensure that all the topics are covered at the requested level of depth. Students missing these prerequisites will have to include here the aforementioned courses.:

Calcolo delle probabilità II
Statistica Matematica

We also encourage students to take courses on functional analysis.

⚙️ Prerequisites for Admission

Knowledge of probability theory and mathematical statistics is essential for this track. Please check the syllabus of the courses below (from the Bachelor’s degree in Mathematics at the University of Trento ) to ensure that all the topics are covered at the requested level of depth:

Calcolo delle probabilità II
Statistica Matematica

We also encourage students to take a course on functional analysis.

🎓 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.

🎓 Career Path

Student Testimonials

Davide Berasi

I particularly appreciated the quality of the lectures and the strong academic preparation. l learned many mathematical and statistical tools for analyzing and interpreting complex data, and I discovered the fascinating world of deep learning.

1. What's your name and where are you from? Hi! My name is Davide and I am from a small mountain village near Trento.

2. What did you study before the master? I completed my Bachelor’s degree in Mathematics at the University of Trento.

3. Why did you choose this master? After completing my Bachelor’s degree, pursuing a Master in Mathematics felt like the natural continuation of my studies. I chose the track Mathematics for Data Science for two main reasons. First, alongside mathematics, I have always been interested in computer science and statistics, and this track allowed me to study topics in both these areas. Second, Data Science offered a wide range of future career opportunities, ranging from industry jobs to research positions, while still allowing me to enrich my mathematical background.

4. What do you consider to be the strong points of the master? In my opinion, one of the strongest aspects of the Mathematics for Data Science track is its versatility. The program provides students with solid mathematical, statistical, and computational skills that are highly requested across many sectors, including technology, finance, banking, and insurance. This broad preparation opens the door to diverse and rewarding career paths.

5. What did you like about the master programme? I really liked the two years of the master. I studied topics I was interested in, I had the opportunity to spend a period abroad as an Erasmus student, and I met very good friends. For the academic side, I particularly appreciated the quality of the lectures and the strong academic preparation. l learned many mathematical and statistical tools for analyzing and interpreting complex data, and I discovered the fascinating world of deep learning.

6. What did you do immediately after the master? After graduation, I worked for one year as a researcher at Federazione Bruno Kessler, a research centre in Trento. Following this experience, I began a PhD at the Multimedia and Human Understanding Group at the University of Trento.

7. What are you doing right now in terms of work? In my PhD, I do research on Vision Language Models, a class of AI systems that combine visual understanding with language capabilities. For example, these systems can describe the content of an image, ground an object description in a picture, or answer questions about a video. In particular, my work focuses on investigating and interpreting the internal mechanisms and hidden interpretable behaviours of these models.

8. Would you recommend the Masters to someone? To whom in particular? I am very happy with my choice of the Mathematics for Data Science track and I would definitely recommend it. I believe it is especially well-suited for students with a good mathematical background who are interested in expanding their skills in statistics, data analysis, and computational methods. This master teaches you both theoretical foundations and practical skills that are highly requested in many industries, while also offering excellent preparation for research careers.

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.

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

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.

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 Università 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

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!

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!

Ester Riccardi

What I really liked about the master was the variety of opportunities it offers beyond the standard lectures, such as short courses on specific topics, which let you explore something new in a more focused way, and also give you the chance to meet researchers and potentially start collaborations, whether for internships or thesis projects.

1. What's your name and where are you from? Hi everyone! I'm Ester Riccardi, from a little town in the province of Bergamo.

2. What did you study before the master? I studied Mathematics at the University of Trento.

3. Why did you choose this master, and the track Mathematics for Data Science in particular? I decided to stay in Trento because I really enjoyed the years I spent there during my bachelor. I felt the university environment, and especially the Department of Mathematics, was familiar and welcoming. During my bachelor, I particularly enjoyed courses in the field of statistics and probability, so the Mathematics for Data Science track was the natural choice. I also wanted to do something more applied during the master, while keeping a strong mathematical foundation.

4. What do you consider to be the strong points of the master? One of the strongest points of this master is definitely the variety of courses it offers. You can build your own path based on your interests, instead of following a fixed one. I also appreciated the possibility of choosing courses from other departments (e.g., Computer Science), which allows you to broaden your perspective and tailor the programme even more to your goals. Another crucial aspect is that many courses, in addition to the usual exam, include a project. In some cases the project is individual, while in others it is done in groups. I found this very useful because it helps you apply what you learn in a more practical way, and at the same time develop teamwork skills by collaborating and exchanging ideas with your peers.

5. What did you like about the master? What I really liked about the master was the variety of opportunities it offers beyond the standard lectures, such as short courses on specific topics, which let you explore something new in a more focused way, and also give you the chance to meet researchers and potentially start collaborations, whether for internships or thesis projects. Another aspect I really appreciated was the relationship you can build with professors. You can easily engage with them during lectures if you want to ask questions or explore ideas further, and they are always open to chat or give advice whenever you need it.

6. What did you do immediately after the master? Right after graduating in October 2024, I spent about one year working as a teacher in a high school in Bergamo. It gave me some time to think about my future and figure out whether to go for a PhD or start applying to different companies.

7. What are you doing right now in terms of work? In the summer of 2025, I got the exciting news that I've been accepted into the PhD programme in Cognitive and Brain Sciences at CIMeC (UniTN), a field that has always fascinated me. Currently I am working on Computational Neuroscience and Generative AI. Even if I'm not working directly with math and statistics, my background has been very useful, helping me approach and understand deep learning algorithms in a more grounded way.

8. Would you recommend this programme to someone? To whom in particular? For sure, I would recommend this programme to anyone who is fascinated by the applications of mathematics and statistics. But I'd also suggest it to those who haven't quite found their path yet, because this master opens up a lot of different opportunities and can help you discover what you really enjoy.

Master theses

2025

Alessia Moretti

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

👩‍🏫 Supervisors: Claudio Agostinelli, Sara Wade

🏛️ Visiting: University of Edinburgh, UK

2025

Gaia Benedetti

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

👩‍🏫 Supervisors: Claudio Agostinelli, Annalisa Barla

🏛️ Visiting: University of Genova, Italy

2025

Paul-Henrik Heilmann

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

👩‍🏫 Supervisors: Claudio Agostinelli, Andreas Prohl

🏛️ Visiting: University of Trento (Erasmus)

2025

Valentina Bertiato

🎓 Thesis: Comparative study of SARIMA, prophet, and RNNs for air pollution forecasting in Barcelona

👩‍🏫 Supervisors: Claudio Agostinelli, Josep Anton Sànchez Espigares

🏛️ Visiting: Universistat Politècnica de Catalunya, Spain

2025

Federica Salvaro

🎓 Thesis: Modelization and experiments of human decision making under high cognitive load: the case of the Knight’s Tour

👩‍🏫 Supervisors: Veronica Vinciotti, Lucilla Alvarez-Zuzek, Oriol Artime

🏛️ Visiting: University of Barcelona, Spain

2024

Silvia Menchetti

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

👩‍🏫 Supervisors: Veronica Vinciotti, Marco Chierici

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2024

Ester Riccardi

🎓 Thesis: Introducing causality in relational event models

👩‍🏫 Supervisors: Veronica Vinciotti, Ernst Wit

🏛️ Visiting: Università della Svizzera italiana, Switzerland

2024

Laura Bisoffi

🎓 Thesis: The emergence of zealotry in opinion dynamics

👩‍🏫 Supervisors: Veronica Vinciotti, Lucilla Alvarez-Zuzek, Oriol Artime

🏛️ Visiting: University of Barcelona, Spain

2024

Francesco Frattini

🎓 Thesis: Copula graphical models with node degree heterogeneity

👩‍🏫 Supervisors: Veronica Vinciotti

2024

Lorenzo Zuccato

🎓 Thesis: Bayesian nonparametric clustering for preference learning

👩‍🏫 Supervisors: Veronica Vinciotti, Valeria Vitelli

🏛️ Visiting: University of Oslo, Norway

2024

Anna Galli

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

👩‍🏫 Supervisors: Claudio Agostinelli, Alexander Dürre

🏛️ Visiting: Leiden University, Netherlands

2024

Aurora Vindimian

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

👩‍🏫 Supervisors: Claudio Agostinelli, Riccardo Gallotti, Thomas Louf

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2024

Seyed Mohsen Moosavi

🎓 Thesis: Some applications of ergodic theory in deep neural networks

👩‍🏫 Supervisors: Claudio Agostinelli

2024

Elisa Muratore

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

👩‍🏫 Supervisors: Claudio Agostinelli, Riccardo Gallotti, Letizia Iannucci, Mikko Kivela

🏛️ Visiting: Aalto University, Finland

2024

Valentina Fusaro

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

👩‍🏫 Supervisors: Claudio Agostinelli, Sebastiano Bernuzzi, Albino Perego

2024

Chiara Maggiolini Cacciamani

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

👩‍🏫 Supervisors: Claudio Agostinelli, Abhik Ghosh

🏛️ Visiting: Indian Statistical Institute, India

2024

Nicola Potenza

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

👩‍🏫 Supervisors: Claudio Agostinelli

2023

Alice Polinelli

🎓 Thesis: Causal regularization and its extension to generalized linear models

👩‍🏫 Supervisors: Veronica Vinciotti, Ernst Wit

🏛️ Visiting: Università della Svizzera italiana, Switzerland

2023

Veronica Poda

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

👩‍🏫 Supervisors: Veronica Vinciotti, Catherine Matias

🏛️ Visiting: Sorbonne Université, France

2023

Marco Borriero

🎓 Thesis: Gaussian graphical models for partially observed functional data

👩‍🏫 Supervisors: Veronica Vinciotti, Luigi Augugliaro

🏛️ Visiting: University of Palermo, Italy

2023

Silvia Guerrini

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

👩‍🏫 Supervisors: Veronica Vinciotti, Tiago Peixoto

🏛️ Visiting: Central European University, Austria

2023

Melania Lembo

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

👩‍🏫 Supervisors: Veronica Vinciotti, Ernst Wit

🏛️ Visiting: Università della Svizzera italiana, Switzerland

2023

Luca Cibinel

🎓 Thesis: Penalised likelihood inference for gaussian covariance graph models

👩‍🏫 Supervisors: Veronica Vinciotti, Alberto Roverato

🏛️ Visiting: University of Padova, Italy

2023

Giada Dal Col

🎓 Thesis: Analytical properties and statistical inference in ranking dynamics

👩‍🏫 Supervisors: Claudio Agostinelli, Oriol Artime, Riccardo Gallotti

🏛️ Visiting: University of Barcelona, Spain

2023

Claudiu Acsinte

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

👩‍🏫 Supervisors: Claudio Agostinelli, Luigi Malagò

🏛️ Visiting: Transylvanian Institute of Neuroscience, Romania

2023

Federica Forzanini

🎓 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

👩‍🏫 Supervisors: Claudio Agostinelli, Annalisa Barla

🏛️ Visiting: University of Genova, Italy

2022

Mauritz Cartier van Dissel

🎓 Thesis: State-space models for multivariate electricity load forecasting

👩‍🏫 Supervisors: Veronica Vinciotti, Santiago Mazuelas

🏛️ Visiting: Basque Center for Applied Mathematics, Spain

2022

Gaia Colombani

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

👩‍🏫 Supervisors: Veronica Vinciotti, Oriol Artime, Giulia Bertagnolli

🏛️ Visiting: University of Barcelona, Spain

2022

Marco Sigala

🎓 Thesis: Parallel heuristic optimization algorithms for waste collection problems

👩‍🏫 Supervisors: Veronica Vinciotti, Francesca Cipollini

🏛️ Visiting: aizoOn Technology Consulting, Italy

2022

Lucia Filippozzi

🎓 Thesis: Minimax risk classifier with noisy labels

👩‍🏫 Supervisors: Claudio Agostinelli, Santiago Mazuelas

🏛️ Visiting: Basque Center for Applied Mathematics, Spain

2022

Yuri Baldo

🎓 Thesis: Temporal-difference learning methods with eligibility traces

👩‍🏫 Supervisors: Claudio Agostinelli, Luigi Amedeo Bianchi

2022

Anna Bertani

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

👩‍🏫 Supervisors: Claudio Agostinelli, Manlio De Domenico

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2022

Giorgia Rossato

🎓 Thesis: Toward functional data analysis on torus

👩‍🏫 Supervisors: Claudio Agostinelli

2022

Chiara Avigo

🎓 Thesis: Variational inference methods for Bayesian neural networks: towards uncertainty estimation of neural networks predictions

👩‍🏫 Supervisors: Claudio Agostinelli

2022

Tobia Filosi

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

👩‍🏫 Supervisors: Claudio Agostinelli

2021

Giuseppe Saccardi

🎓 Thesis: Additive covariance modeling via unconstrained parametrization

👩‍🏫 Supervisors: Claudio Agostinelli, Matteo Fasiolo

🏛️ Visiting: University of Bristol, UK

2021

Irene Bianconi

🎓 Thesis: Importance sampling with application in sparse linear models

👩‍🏫 Supervisors: Claudio Agostinelli, Nicolas Chopin

🏛️ Visiting: Institut Polytechnique de Paris, France

2021

Clara Lupo

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

👩‍🏫 Supervisors: Claudio Agostinelli, Antonio Canale

🏛️ Visiting: University of Padova, Italy

2021

Francesca Onorato

🎓 Thesis: Segmentation strategies for end-to-end simultaneous speech translation

👩‍🏫 Supervisors: Claudio Agostinelli, Matteo Negri, Elisa Ricci, Marco Turchi

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2021

Massimiliano Datres

🎓 Thesis: The mathematical foundation of the probabilistic compressed sensing approach

👩‍🏫 Supervisors: Claudio Agostinelli

2021

Andrea Fox

🎓 Thesis: Bayesian methods for tabular reinforcement learning

👩‍🏫 Supervisors: Claudio Agostinelli

2021

Andrea Panarotto

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

👩‍🏫 Supervisors: Claudio Agostinelli

2021

Carlo Pietropoli

🎓 Thesis: Hidden Markov models for football analytics

👩‍🏫 Supervisors: Claudio Agostinelli, Roberta Sirovich

🏛️ Visiting: Kama.Sport, Italy

2021

Elena Sabbioni

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

👩‍🏫 Supervisors: Claudio Agostinelli, Alessio Farcomeni

🏛️ Visiting: L'altra statistica, Italy

2021

Angela Fabian

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

👩‍🏫 Supervisors: Claudio Agostinelli

2020

Giorgia Casaccio

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

👩‍🏫 Supervisors: Claudio Agostinelli, Manlio De Domenico

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2020

Mattia Brazzale

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

👩‍🏫 Supervisors: Claudio Agostinelli, Sonia Mazzucchi

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2020

Stefano Arrigoni

🎓 Thesis: Markov chain for disability insurance models

👩‍🏫 Supervisors: Claudio Agostinelli

🏛️ Visiting: Gruppo ITAS Assicurazioni, Italy

2020

Francesco Cabras

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

👩‍🏫 Supervisors: Claudio Agostinelli, Luigi Amedeo Bianchi

2019

Ivan Marino

🎓 Thesis: Mixture models for grouped data

👩‍🏫 Supervisors: Claudio Agostinelli

🏛️ Visiting: CHT Italia, Italy

2019

Michele Pedrazzoli

🎓 Thesis: Adversarial examples from a robust statistics point of view

👩‍🏫 Supervisors: Claudio Agostinelli

🏛️ Visiting: Kama.Sport, Italy

2018

Giulia Bertagnolli

🎓 Thesis: Complex networks and statistical data depths

👩‍🏫 Supervisors: Claudio Agostinelli, Manlio De Domenico

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2017

Viola Nervini

🎓 Thesis: Wavelets and convolutional neural networks for food quality control

👩‍🏫 Supervisors: Claudio Agostinelli, Giuseppe Jurman

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2017

Giovanni Saraceno

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

👩‍🏫 Supervisors: Claudio Agostinelli, Cesare Furlanello, Nikola Kasabov

🏛️ Visiting: Auckland University of Technology, New Zeland

2017

Giulia Gangi

🎓 Thesis: Deep CNN for grape detection

👩‍🏫 Supervisors: Claudio Agostinelli

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2016

Lucia Trastulla

🎓 Thesis: Techniques of integration for high-throughput omics data

👩‍🏫 Supervisors: Claudio Agostinelli, Giuseppe Jurman, Alessandro Zandonà

🏛️ Visiting: Fondazione Bruno Kessler, Italy

2016

Veronica Vena

🎓 Thesis: Singular spectrum analysis: an application to air pollution data

👩‍🏫 Supervisors: Claudio Agostinelli

2016

Manlio Valenti

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

👩‍🏫 Supervisors: Claudio Agostinelli, Marcus Hutter

🏛️ Visiting: Australian National University, Australia

2016

Ylenia Giarratano

🎓 Thesis: Phylogenetic convolutional neural networks in metagenomics

👩‍🏫 Supervisors: Claudio Agostinelli, Giuseppe Jurman

🏛️ Visiting: Fondazione Bruno Kessler, Italy