The four-year program combines rigorous coursework with early and continuous research training. Students progressively move from advanced methodological training to fully independent research. The program is articulated in three curricula.

Economics (ECO)

The Economics curriculum is designed for students with a strong quantitative background in economics. It provides rigorous training in microeconomics, macroeconomics and econometrics. The program develops advanced theoretical and empirical skills, preparing students for frontier research in economics.

Statistics (STAT)

The Statistics curriculum is intended for students with solid methodological training in statistics and quantitative methods. It offers advanced coursework in probability theory, stochastic processes, statistical inference, Bayesian statistics, statistical learning, statistical modelling, computational statistics and data analysis. The program equips students with strong analytical and computational tools for research in theoretical and applied statistics, as well as in interdisciplinary quantitative fields.

Big Data & Analytics for Business (BiDAB)

The Big Data & Analytics for Business curriculum integrates statistical, computational and economic approaches to data science. It provides rigorous training in data management and programming, the analysis of large-scale structured and unstructured data—including natural language data—big data architectures, data visualization, machine learning, and text and web mining.

Coursework

During the first year, students complete graduate-level courses in economics, statistics and data science, depending on the curriculum. During the second year, students deepen their expertise in specialized Reading Groups. The coursework provides strong theoretical foundations and state-of-the-art quantitative tools, preparing students for frontier research.

Research and Dissertation

Research activities start early in the program. Under the guidance of a supervisor, students develop an original research agenda and actively participate in the department’s research environment. PhD students are strongly encouraged to attend the department’s weekly seminars and to present their work in dedicated PhD seminars.

The PhD dissertation consists of at least two original, self-contained papers that make a significant contribution to the scientific literature and are suitable for submission to leading international peer-reviewed journals.

Evaluation and Progression

Admission to the third and fourth year is determined by exam performance and research progress. Upon completion of the dissertation, students present their work for external evaluation. After receiving the reviewers’ reports, candidates are admitted to the final defense before the Evaluation Committee

Successful candidates are awarded the PhD title in accordance with the regulations of the University of Milano-Bicocca.

The Call of Applications 2026-2027 offers 12 fully-funded scholarships.

The selection procedure is regulated by the official Call for Applications (Bando di Concorso), which will be published in March.

The official Call for Applications contains detailed information on:

• the documents which each candidate has to submit;
• structure, contents and timing of the entrance examination.

The official Call for Applications will be available here.

PhD in Economics, Statistics and Data Science

PhD Candidates - Cycle XXXVII

Curriculum ECONOMICS

• Mr. Valerio DIONISI

  • PhD thesis: Macroeconomic Perspectives on Sectoral Composition: Networks and Inequality
  • Tutor: Prof. Alice Albonico, University of Milano-Bicocca
  • Supervisor: Prof. Andrea Colciago, University of Milano-Bicocca and De Nedernaldsche Bank
  • Discussion: 05/03/2026 - 15:30 | Room U6-11

• Mr. Matteo FERRARO

  • PhD thesis: Essays in Development Economics
  • Supervisors: Prof. Andrea Guariso, University of Milano-Bicocca; Prof. Selene Ghisolfi, Catholic University of Milano
  • Discussion: 25/02/2026 - 10:30 | Room U4/7

• Mr. Mattia LONGHI

  • PhD thesis: No Greater Treasure than a True Friend? Favoritism in the Allocation of Official and Private Flows
  • Supervisor: Prof. Silvia Marchesi, University of Milano-Bicocca
  • Discussion: 20/02/2026 - 14:00 | Room U6/22

• Mrs. Caterina MORELLI

  • PhD thesis: Sustainability by Proximity: How Geographic Closeness Drives Corporate and Regional Carbon Emissions
  • Supervisors: Prof. Paolo Maranzano, University of Milano-Bicocca; Prof. Philipp Otto, University of Glasgow
  • Discussion: 16/02/2026 - 14:30 | Room U7-15

• Mr. Andrea SORRENTINO

  • PhD thesis: Essays on Group Contests
  • Tutor: Prof. Marco Mantovani, University of Milano-Bicocca
  • Supervisor: Prof. Mario Gilli, University of Milano-Bicocca
  • Discussion: 03/03/2026 - 11:00 | Room U6/41

Curriculum STATISTICS

• Mr. Alessandro COLOMBI

  • PhD thesis: Bayesian Learning of Laten Discrete Structures: Graphs, Clusters and Features Allocation
  • Supervisors: Prof. Raffaele Argiento, University of Bergamo; Prof. Lucia Paci, Catholic University of Milano
  • Discussion : 03/03/2026 - 10:30 | Room U6-2112

• Mr. Luca DANESE

  • PhD thesis: Bayesian Methods for Change Point Analysis
  • Supervisors: Proff. Andrea Ongaro and Riccardo Corradin, University of Milano-Bicocca
  • Discussion : 26/02/2026 - 14:30 | Room U6-10

• Mr. Lorenzo GHILOTTI

  • PhD thesis:  Feature Allocation Models in Bayesian Statistics
  • Supervisor: Prof. Federico Camerlenghi, University of Milano-Bicocca
  • Discussion : 05/03/2026 - 10:00 | Room U6-39  

• Mrs. Chiara MAGNANI

  • PhD thesis:  Distribution-free Outlier Detection
  • Supervisor: Prof. Aldo Solari, University of Venezia – Ca’ Foscari
  • Discussion: 03/03/2026 - 14:00 | Room U6-2112

• Mr. Luca PRESICCE

  • PhD thesis:  Bayesian Transfer Learning Approaches for Large-scale Spatiotemporal Problems
  • Supervisors: Prof. Tommaso Rigon, University of Milano-Bicocca; Prof. Sudipto Banerjee, University of California at Los Angeles
  • Discussion: 26/02/2026 - 16:00 | Room U6-11

Curriculum BIG DATA & ANALYTICS FOR BUSINESS

• Mr. Simone D’AMICO

  • PhD thesis: Syntesis of Vector Space Models for Finance and Labour Market Analysis
  • Tutor: Prof. Matteo Pelagatti, University of Milano-Bicocca
  • Supervisors: Prof. Giancarlo Sperlì, University of Napoli – Federico II; Prof. Fabio Mercorio, University of Milano-Bicocca
  • Discussion: 12/03/2026 - 14:00 | Room U6-31

• Mrs. Alessia DE SANTO

  • PhD thesis:  Novel Approaches of Taxonomy Enrichment via Distributional Semantics
  • Tutor: Prof. Matteo Pelagatti, University of Milano-Bicocca
  • Supervisors: Prof. Emilio Colombo, Catholic University of Milano; Prof. Fabio Mercorio, University of Milano-Bicocca
  • Discussion: 12/03/2026 - 11:30 | Room U6-31

• Mrs. Arianna MIOLA

  • PhD thesis: Transparent and Reliable AI for the Financial Domain
  • Supervisors: Drs. Andrè Panisson, Alan Perotti, CENTAI Institute
  • Discussion: 11/03/2026 - 14:30 | Room U4-10

• Mr. Filippo PALLUCCHINI

  • PhD thesis: Three Shades of Alignment: from Distributional Semantics to Trustworthy and Interpretable Large Language Models
  • Supervisor: Prof. Fabio Mercorio, University of Milano-Bicocca
  • Discussion: 12/03/2026 - 10:00 | Room U6-31

• Mr. Andrea PONTI

  • PhD thesis:  Multi-task Learning in Black-box Optimization
  • Tutor: Prof. Fabio Mercorio, University of Milano-Bicocca
  • Supervisor: Prof. Antonio Candelieri, University of Milano-Bicocca
  • Discussion: 11/03/2026 - 16:00 | Room U4-10

All Economics courses are shared with the PhD Program in Economics and Finance of Università Cattolica di Milano.

First Year

Term I

Teaching activities start on October 22, 2025 and end on December 19, 2025. Exams start on January 7, 2026 and end on January 14, 2026. The courses/modules offered during term I are:

• Mathematics – Linear algebra (Instructor: A. Mainini, Catholic University of Milano)
• Mathematics I (Instructor: D. Visetti, University of Milano-Bicocca);
• Mathematics II (Instructor:  F. Cavalli, University of Milano-Bicocca);
• Mathematics III (Instructor: M. Longo, Catholic University of Milano)
• Microeconomics I (Instructor: M. Mantovani, University of Milano-Bicocca)

Term II

Teaching activities start on January 19, 2026 and end on April 1, 2026. Exams start on April 13, 2026 and end on April 17, 2026. The courses/modules offered during term II are:

• Microeconomics II (Instructor: M. Gilli, University of Milano-Bicocca)
• Microeconomics III  (Instructors: L. Colombo, M. Magnani, Catholic University of Milano)
• Microeconomics IV (Instructors: P. Bertoletti, D. Bosco, University of Milano-Bicocca)
• Econometrics I (Instructors: M. Manera, University of Milano-Bicocca; C. Cattaneo, European Institute of Economics and the Environment)
• Econometrics II (Instructors: A. Ugolini, University of Milano-Bicocca; D. Valenti, Polytechnic University of Milano)
• Econometrics III (Instructors: M.L. Mancusi, D. Vigani, Catholic University of Milano)

Term III

Teaching activities start on April 22, 2026 and end on July 3, 2026. Exams start on July 13, 2026 and end on July 17, 2026. The courses/modules offered during term III are:

• Computational Statistics II (Instructor: Prof. A. Pini, Catholic University of Milano)
• Macroeconomics I (Instructor: Prof. G. Femminis, Catholic University of Milano)
• Macroeconomics II (Instructors: Prof. A. Albonico, University of Milano-Bicocca)
• Macroeconomics III (Instructors: Dr. R.Masolo , Catholic University of Milano)
• Macroeconomics IV (Instructors: Dr. Membretti, Commissione europea)
• Research Methods (Instructors: Dr. S. Ghisolfi and Prof. M.Ovidi, Catholic University of Milano)

Second Year

Term I (October 2025 – December 2025) and term II (January 2026 – April 2026)

Reading Groups are offered jointly with the PhD in Economics and Finance of the Catholic University of Milano. Detailed information on each RG can be found here. 

First Year

Term I

Teaching activities start on October 22, 2025 and end on December 19, 2025. Exams start on January 7, 2026 and end on January 14, 2026. 

The courses/modules offered during term I for the curriculum Statistics (STAT) are:

• Mathematical Analysis I (Instructor: F. Battistoni, Catholic University of Milano)
• Mathematical Analysis II (Instructor: C.A. De Bernardi, Catholic University of Milano)
• Mathematical Analysis III (Instructor: E. Miglierina, Catholic University of Milano)

Term II

Teaching activities start on January 19, 2026 and end on April 1, 2026. Exams start on April 13, 2026 and end on April 17, 2026. The courses/modules offered during term II for the curriculum Statistics (STAT) are:

• Probability I (Instructor: P.G. Bissiri, University of Milano-Bicocca)
• Probability II (Instructor: F. Camerlenghi, University of Milano-Bicocca)
• Stochastic Processes (Instructor: M. Beraha, University of Milano-Bicocca)
• Statistical Inference I (Instructor: S. Leorato, University of Milano)

Term III

Teaching activities start on April 22, 2026 and end on July 3, 2026. Exams start on July 13, 2026 and end on July 17, 2026. The courses/modules offered during the III term for the curriculum Statistics (STAT) are:

• Statistical Inference I (Instructor: Samantha Leorato  University of Milano) 
• Statistical Inference II (Instructor: Prof. Dr. T. Rigon, University of Milano-Bicocca) 
• Bayesian Statistics I (Instructor: Prof. B. Nipoti, University of Milano-Bicocca)
• Bayesian Statistics II  (Instructor: Dr. R. Corradin , University of Milano-Bicocca) 
• Bayesian Statistics III (Instructor: Prof. R. Argiento, University of Bergamo)

Term IV

• Statistical Modelling I (Instructor: Prof. F. Castelletti, Catholic University of Milano) 
• Statistical Modelling II (Instructors: Proff. D. Spinelli and G. Zaccaria, University of Milano-Bicocca)
• Statistical Modelling III  (Instructor: Dr. L. Brusa, University of Milano-Bicocca) 
• Statistical Modelling IV (Instructor: Prof. F. Bartolucci, University of Perugia)

Second Year

Reading Groups (RGs) offered in academic year 2025-26 (XL cycle – II year) for the curriculum Statistics (STAT):

Term I

• RG Approximate Bayesian Computational Methods (Instructor:  A. Fasano, University of Torino)
• RG Probabilistic Preference Learning (Instructor: V. Vitelli, University of Oslo)

Term II

• RG Automated Machine Learning and Neural Architectural Search (Instructor: A. Candelieri, University of Milano-Bicocca)
• RG Some Issues on Statistical Modelling (Instructors: R. Borgoni, P. Maranzano, University of Milano-Bicocca)
• RG Spatio-temporal Data (Instructors: P. Maranzano, R. Borgoni, University of Milano-Bicocca)

First Year

Term I

Teaching activities start on October 22, 2025 and end on December 19, 2025. Exams start on January 7, 2026 and ends on January 14, 2026 The courses/modules offered during term I are:

• Programming in Python (Instructor: M. Cesarini, University of Milano-Bicocca)
• Architecture for Big Data Processing (Instructor: G. Sperlì, University of Napoli)
• Architecture for Big Data Processing Lab (Instructor: A. Galli, University of Napoli)

Term II

Teaching activities start on January 19, 2026 and end on April 1, 2026. Exams start on April 13, 2026 and end on April 17, 2026. The courses/modules offered during term II are:

• Probability (Instructor: A. Di Brisco, University of Piemonte Orientale)
• Statistical Inference I (Instructor: A. Giampino, University of Milano-Bicocca)

Term III

Teaching activities start on April 22, 2026 and end on July 3, 2026. Exams start on July 13, 2026 and end on July 17, 2026.

• Statistical Inference I (Instructor: Dr. A. Giampino  University of Milano-Bicocca)
• Statistical Inference II (Instructor: Dr. R. Ascari, University of Milano-Bicocca)
• Social Media Analytics (Instructor: Dr. R. Boselli, University of Milano-Bicocca)
• Machine Learning (Instructor: Dr. L. Malandri, University of Milano-Bicocca)
• Natural Language Processing (Instructor: Prof. E. Cambria, Nanyang Technological University, Singapore

Term IV

The IV term teaching activities (curricula STAT and BIDAB only) start on September 7, 2026 and end on October 23, 2026. The IV term exams are scheduled at the end of each module.

• • Statistical Modelling I (Instructor: Prof. F. Castelletti, Catholic University of Milano; see Timetable STAT)
  • Explainable AI for Business Value (Instructor: Proff. F. Mercorio, University of Milano-Bicocca)
  • Deep Learning & Computer Vision for Business (Instructors: Proff. L. Stacchio, University of Macerata)

Second Year

Term I

• RG Natural Language Processing (Instructor:  A. Seveso, University of Milano-Bicocca)
• RG Generative AI (Instructor: Navid Nobani, University of Milano-Bicocca)

Coordinator: Prof. Gino Alessandro Gancia

ECOSTATDATA is managed by two bodies:

• the Programme Committee is the executive and decision-making body of the PhD program, composed of faculty from the University of Milano-Bicocca and from other leading Italian and international universities and research institutions;
• the Advisory Board collaborates in the organization of teaching and research activities ans is composed of a limited number of professors and researchers representing the three curricula.