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Department of Economics, Management and Statistics  DEMS

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PhD in Economics, Statistics and Data Science

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The four-year PhD in Economics, Statistics and Data Science (ECOSTATDATA)  provides the most effective response to the important challenges which nowadays doctoral programmes in the areas of economics, statistics and data analytics, both in Italy and Europe, have to cope with: i) high qualification of the faculty, in terms of teaching abilities and publication records; ii) capability of attracting high quality students; iii) interdisciplinarity; iv) internationalization; v) relations with the non-academic job market; vi) placement of students who have successfully discussed their dissertations.

ECOSTATDATA builds upon the fruitful collaboration among economists, statisticians and data scientists from the Department of Economics, Management and Statistics (DEMS) and the Department of Statistics and Quantitative Methods of the University of Milano-Bicocca (UniMiB), which has started twenty years ago within the BSc in Statistics and Economics, as well as the MSc in Statistics and Economics and is going on with the more recent MSc in Data Science.

Coordinator : Prof.  Matteo Manera

Deputy Coordinator : Prof.  Giorgio Vittadini

NEW!!! Call for applications 2024-2025 (XL cycle)

A.A. 2024-2025 (cycle XL)

Call for Applications

DEMS - University of Milano-Bicocca, Italy

The Department of Economics, Management and Statistics (DEMS) of the University of Milano-Bicocca invites applications to its PhD Programme in Economics, Statistics and Data Science (ECOSTATDATA) for the academic year 2024-25 (XL cycle).

The PhD Programme is articulated in  three curricula , Economics (ECO), Statistics (STAT) and Big Data & Analytics for Business (BIDAB). The length of the PhD Programme is  four years , starting in late October 2024 (the precise starting date will be announced in due course on the PhD website).

The Call of Applications 2024-2025 offers at least 10 fully-funded scholarships .

The selection procedure is regulated by the official Call for Applications (Bando di Concorso), which will be published in the Doctoral School’s and in the PhD programme websites  on April 12, 2024 , with deadline on May 14, 2024.

The official Call for Applications contains detailed information on: i) the documents which each candidate has to submit; ii) structure, contents and timing (May 27, 2024 - June 21, 2024) of the entrance examination; iii) description of the projects related to the scholarships and positions offered.

The official Call for Applications will be published  here .

The PhD programme (in a nutshell...)

Introduction.

ECOSTATDATA belongs to the PhD School of UniMiB, it is affiliated to DEMS, it lasts four years and it is articulated in three curricula, the original two curricula Economics (ECO) and Statistics (STAT), and, starting from cycle XXXVII (academic year 2021-2022), the “new” curriculum Big Data & Analytics for Business (BiDAB) .

The first-year teaching activities are mainly devoted to structured courses (tool courses), which are compulsory. Some of these courses are fixed and specific to each curriculum, some are in common between the three curricula, some other courses are chosen by students within each curriculum.

The second-year teaching activities take the form of less structured courses (elective courses or reading groups).

In general, the first-year courses are offered by “internal” teachers, while second-year courses are often open to the collaboration of foreign instructors (visiting scholars).

The curriculum Economics (ECO)

This curriculum is indicated to students with a strong background in quantitative economics and provides advanced training in econometrics, microeconometrics, time series analysis, microeconomics and macroeconomics.

The curriculum Statistics (STAT)

This curriculum is designed for students with a strong background in statistics, both methodological and applied , and provides advanced training in probability, stochastic processes, statistical inference, Bayesian statistics, statistical learning, statistical modelling, computational statistics and data analysis.

The “new” curriculum Big Data & Analytics for Business (BiDAB)

This curriculum starts from cycle XXXVII (academic year 2021-2022) , and provides students with rigorous training in data management and programming, with focus on: the analysis of large amounts of structured and unstructured data (natural language); the main paradigms of big data and data visualization, based on the use of innovative techniques of machine learning, text and web mining.

“Flexible” and “training” profiles

By means of appropriate sequences of courses, suggested and monitored by the Programme Committee and the supervisors, students are able to build up “flexible” profiles, which are mainly addressed to scientific research, both in universities or in non-academic institutions, at national or international level.

ECOSTATDATA facilitates the interaction between economic, statistical and data management skills by proposing innovative “training” profiles, which are  mainly addressed to the non-academic job market. The “training” profiles aim at:

• offering to the non-academic job market high-level skills which are not currently available;
• attracting students who are interested in ECOSTATDATA as a way to gain new and advanced skills to be immediately spent into that segment of the job market which is not academically- or research-oriented;
• eliciting the collaboration of high-quality national multi-national companies, which are active in human capital investment and are ready to use the instruments of the executive doctorate, the apprenticeship contracts as well as the direct financing of a PhD scholarship on specific research projects.

Length of the programme

The current length of many PhD programmes in economics, statistics and data science in Italy, including the PhD in Economics DEFAP-Bicocca and in Statistics and Mathematical Finance of UniMiB, is three years. This length is insufficient to guarantee that the PhD theses meet the quality standards achieved by the best European PhD programmes. For this reason, ECOSTATDATA lasts four years . This duration is in line with the recent choices of some of the best Italian PhD programmes in economics, statistics and data science, as well as the PhD programmes in this area offered by the most prestigious European academic institutions.

Interdisciplinarity

ECOSTATDATA fosters interdisciplinary research activities, by favouring co-tutorships between economists, statisticians and data scientists, as well as through the “flexible” and “training” profiles.

Relations with the non-academic job market

ECOSTATDATA is particularly active in collaborating with national, multi-national, high-quality and innovation-oriented companies. In particular, ECOSTATDATA is able to: i) offer high-level skills which are not currently available on the non-academic job market; ii) attract students who are interested in ECOSTATDATA as a way to gain new and advanced skills to be immediately spent into that segment of the job market which is not academically- or research-oriented; iii) elicit the collaboration of high-quality national multi-national companies, which are active in human capital investment and are ready to use the modern instruments of the executive doctorate, the apprenticeship contracts as well as the direct financing of PhD scholarships on specific research projects.

Internationalization

The international experience which has flourished within the PhD in Economics DEFAP-Bicocca and the PhD in Statistics and Mathematical Finance of UniMiB, together with the professional networks developed by many faculty members, guarantees that ECOSTATDATA is particularly active in collaborating with prestigious foreign universities, in terms of both students and faculty members exchange programs and joint degrees.

ECOSTATDATA is managed by two bodies:

• the Programme Committee (PC), that is the executive and decision-making board composed by full professors, associate professors and researcher of UniMiB and from other renowned Italian and foreign universities and research institutions;
• the Advisory Board (AB), which collaborates with the PC to organize the teaching and research activities of the programme, is headed by the programme Coordinator and is formed by a limited number of professors and researchers who are representative of the three curricula.

Teaching activities

The teaching activities proposed by ECOSTATDATA are organized during the first two years and differ for each curriculum, although some courses are common. Some economics courses at the first and the second year within the curriculum Economics can be offered jointly with the PhD programme in Economics and Finance of the Catholic University of Milano.

First- year courses

• Curriculum Economics (selected courses)

Mathematics; Computational Statistics I; Econometrics; Microeconometrics; Time Series Analysis; Microeconomics; Macroeconomics; Research Methods; Finance.

• Curriculum Statistics (selected courses)

Mathematical Analysis, Numerical Optimization, Probability, Stochastic Processes, Bayesian Statistics, Statistical Inference, Statistical Learning, Computational statistics II, Statistical Modelling, R for Data Science, Data Management.

• Curriculum Big Data & Analytics for Business (selected courses)

Databases for Structured/Unstructured Data (SQL); Programming in Python; Data Quality and Cleaning for Big Data; Architecture for Big Data Processing; Machine Learning; Cloud & Distributed Algorithm; Data Mining; Natural Language processing and Understanding; Human-Centered AI; Social Media Analysis; Semantic Web; Deep Learning and Computer Vision for Business; Data Visualization & Visual Analysis.

Second-year courses

Second-year courses are mainly “reading groups”, that are built upon the research interests of both instructors and students, and are  articulated into one/two introductory lecture/s and a series of meetings where students critically discuss the readings assigned by the instructor during the initial lecture.

The second-year courses are generally offered during the first part of the second year, in order forstudents to be full-time dedicated to their dissertations as early as possible.

Within each curriculum, a careful selection of courses, monitored by the PC and the student’s supervisor, allows each student to identify a “flexible” profile, which coherent with his/her research interests.

Generally, structured courses have written exams, while the exams associated with the reading groups are more flexible (e.g. written projects and/or oral presentations). The organization of the exams (i.e. form, number of questions, etc.) is decided by the PC and communicated to students at the beginning of each course. 

Monitoring the quality of teaching

The PC runs every year a systematic evaluation of the quality of the courses offered by the PhD programme, by submitting to each student of a given course a detailed questionnaire. Data from the questionnaires are elaborated statistically, sent to each instructor, and discussed within the PC, in order to identify potential problems and solutions.

Admissions to the second year and to years after the second

Admission to the second year is based on the performance of each student in the first-year exams, including the number of “fail” and the number of “resits” each student has been given. Admissions to the third and the fourth years are based on the progresses of the research work. Rules on admission to the second and subsequent years, as well as all the other rules regulating the teaching and research activities of ECOSTAT are formalized by the PC and communicated to each student after enrollment.

Research activities

The Programme Committee (PC) approves the (minimum) number of papers which form a typical PhD dissertation, namely 2. These papers have to be self-contained, independent and potentially publishable on high-quality internationally refereed journals.

Supervision

In order to facilitate students in identifying a sound research project and a suitable supervisor, within the first part of the year the PC organizes a presentation of the research groups which are active among the PC and the Advisory Board (AB) members. Supervisors are asked to systematically monitor the progresses made by their supervisees and periodically report to the PC about the proceedings of their dissertations.

PhD students, especially from the second year, are strongly invited to attend the department seminars organized on a weekly basis at UniMiB. Students of both curricula are also invited to present the progress of their research work in specific seminars, which are part of the student’s evaluation process and, if possible, are jointly organized in order to enhance cross-fertilization between economists, statisticians and data scientists. 

Admission to third and fourth year

Admission to the third and fourth year is formalized by the PC, based on the evaluation of the student’s research work. Admission to the third year takes also into account the performance of each student in the second-year exams.

Admission to external evaluation

Fourth-year students should present, by the end of the year, the final version of their dissertation in front of the PC. If possible, each presentation will be assigned a discussant. The admission to the external reviewers is formalized by the PC, based on the overall evaluation of the PhD thesis.

Thesis discussion

Based on the reports of the external reviewers, students are admitted to the discussion in front of the Evaluation Committee either with minor or major revisions. Students who have successfully defended their dissertation are awarded by the Evaluation Committee the title of “PhD in Economics and Statistics” (students enrolled in cycles XXXIV, XXXV and XXXVI) or the title of “PhD in Economics, Statistics and Data Science” (students enrolled from cycle XXXVII). Students can request to (and obtain from) the Administrative Offices of UniMiB an official document reporting the specific curriculum they have been enrolled in.

ECOSTATDATA takes care of the optimal placement of its students. On this respect, the Programme Committee is very active in: i) providing students with systematic and detailed information on the job market, domestic and international, academic and non-academic; ii) advising and assisting students who intend to apply for academic positions abroad.

Programme committee

Research groups.

The research activities which characterize the PhD programme in Economics, Statistics and Data Science (ECOSTATDATA) are carried out by an active and lively community of junior and senior researchers.

Within DEMS, researchers are organized in clusters , among which the most relevant for ECOSTATDATA are:

- Business, economic and social statistics (coordinator: Prof. Pelagatti)

- Empirical microeconomics and microeconometrics (coordinator: Prof. Manera)

- Experimental and behavioural economics (coordinator: Prof. Stanca)

- Macroeconomics and macroeconometrics (coordinator: Prof. Morana)

- Microeconomics: theory and applications (coordinator: Prof. Gilli)

- Statistics (coordinator: Prof. Ongaro)

- Strategy, organization and innovation (coordinator: Prof. Torrisi)

Detailed information about people involved in each cluster can be found here .

The other two main groups of researchers supporting the programme are affiliated to the Department of Statistics and Quantitative Methods (DiSMeQ) of UniMiB and to the Department of Statistics (DiSTAT), Catholic University of Milano.

Detailed information about the research activities carried on by the DiSMeQ members can be found here .

Detailed information about the research activities carried on by the DiSTAT members can be found here .

Ex-alumni - XXXIV cycle

Supervisor(s): Prof. Silvia Biffignandi, University of Bergamo

Ex-alumni - XXXV cycle

Supervisor(s): Prof. Francesca Greselin, University of Milano-Bicocca; Prof. Ricardas Zitikis, University of Western Ontario, CA

Milano PhD Workshop 2024

The ECOSTATDATA PhD students are happy to announce the second edition of the Milano PhD Workshop , that will be held at the premises of the University of Milano-Bicocca, September 23-27, 2024.

The event is jointly organized with the PhD students in economics of the major universities in the Milanese area.

The program of the event is under construction and will be available shortly.

For details you can contact the local organizers:

PhD students' seminar series 2023-2024

We are very happy to announce this new initiative: the  ECOSTATDATA PhD Seminar Series!

This initiative aims to create a friendly environment where all PhD students at DEMS have the opportunity to present their own research or research proposal to obtain constructive feedback from peers and senior researchers.

Regular reminders before each presentation will be sent, and we really hope you will join this initiative. Your presence and support will be key to make this a success!

The Organizers 

@Angelica Bertucci  

@Ludovica De Carolis  

@Matteo Ferraro  

@Gregorio Ghetti  

@Lorena Popescu  

March 28, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker: Andrea Sorrentino

April 18, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Francesco Ferlaino

Field: Macroeconomics

May 09, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker:  Luca Danese

Field: Bayesian Nonparametrics

May 16, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Angelica Bertucci

May 23, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Matteo Ferraro

May 30, 2024 Aula Seminari (U7 - 2104) 12:00

Speaker:  Lucia Tommasiello

June 6, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Mattia Longhi

June 13, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker:  Claudia Sartirana

June 20, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker 1:  Ludovica De Carolis

Speaker 2:  Jiefeng Bi

Field: Bayesian Statistics

Past events (selected) 2018-2024

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca, joint with the Italian Society of Econometrics (SIdE), the Free University of Bolzano, the Fondazione Eni Enrico Mattei (FEEM), the International Association of Applied Econometrics (IAAE) and the Rimini Center for Economic Analysis (RCEA), have organized the 4th Italian Workshop on Econometrics and Empirical Economics (IWEEE 2024) - Climate and Energy Econometrics , at the Free University of Bolzano, during the period January 25-26, 2024. 

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Center for European Studies (CefES) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Bayesian Structural VAR, held by Prof. Fabio Canova , BI Norwegian Business School, during the period November 9-14, 2023. 

The PhD in Economics, Statistics and Data Science (ECOSTATDATA) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by  Prof. Botond Szabo , Bocconi University, during the period October 5-27, 2021.

The PhD in Economics, Statistics and Data Science (ECOSTATDATA) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by Prof. Omiros Papaspiliopoulos , Bocconi University , during the period October 5-27, 2021. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca and the Fondazione Eni Enrico Mattei (FEEM), Milano, have organized the summer school on Frontiers of Energy Econometrics , at the Como Lake School of Advanced Studies, during the period September 13-17, 2021. Detailed information on the programme and the application procedure can be found on the summer school website:  https://toee.lakecomoschool.org/

The PhD in Economics and Statistics (ECOSTAT) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by Prof. Rajen Shah , University of Cambridge, during the period October 5-30, 2020. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here.

The PhD in Economics and Statistics (ECOSTAT) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized and hosted the course Statistical Learning and Big Data, held by Prof.  Sharon Rosset , Tel Aviv University, during the period October 7-18 2019. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here .

The PhD programme in Economics and Statistics (ECOSTAT) has sponsored the 1 st  CefES International Conference on European Studies, to be held at the University of Milano-Bicocca, Building U6, on June 10th-11 th  2019. Details on this event can be found  here .

The PhD programme in Economics and Statistics (ECOSTAT) has sponsored the International Conference on Econometric Models of Climate Change, held at the University of Milano-Bicocca on August 29th-30 th  2019. Details on this event can be found  here .

Within the Seminar Series DEMS-ECOSTAT,  Prof. Peter M Robinson  (LSE),  has presented the paper titled “Long-range dependent curve time series” (joint with Degui Li and Han Lin Shang). Prof. Robinson is one of the most famous econometricians worldwide and has been in the editorial boards of the most influential journals in econometrics and statistics, from Econometrica to the Journal of Econometrics, from the Journal of the American Statistical Association to the Annals of Statistics. Peter Robinson’s presentation is available  here , while his paper is available  here . This  event  has been held on February 14th 2019, 12.00am, at the Aula del Consiglio, U7, fourth floor, Piazza dell’Ateneo Nuovo 1, 20126 - Milano.

Within the celebrative events of the Twentieth Anniversary of the University of Milano-Bicocca, the Department of Economics, Management and Statistics, in collaboration with the School for Graduate Studies, has organized the International Conference on  The Mathematics of Subjective Probability .  This event was held on September  3rd-5th  2018, at Room U4/2, Piazza della Scienza 1, 20126 - Milano.

Within the celebrative events of its Twentieth Anniversary, the University of Milano-Bicocca, in collaboration with its School for Graduate Studies, has organized the  Lectio Magistralis of Prof. Robert Engle  (NYU University), winner of the 2003 Nobel Memorial Prize in Economic Sciences, on “A Financial Approach to Environmental Risk”. This event was held on June 22nd 2018, 10.00am, at the Auditorium Guido Martinotti U12, Via Vizzola 5, 20126 - Milano.

The Center for European Studies (CefES-DEMS-UNIMIB), the PhD program in Economics and Statistics (ECOSTAT-UNIMIB), and the  Department of Economics, Management and Statistics  (DEMS-UNIMIB) have organized the one-day international conference on  Economic and Financial Implications of Climatic Change .  Two plenary sessions  on the economic and financial implications of climatic change have been organized on June 22 nd  2018, following Prof. Robert Engle’s talk, from 11.30am to 4.45pm, at the Auditorium Guido Martinotti U12, Via Vizzola 5, 20126 - Milano.

XXXVII cycle - Teaching activities - Year II (terms I - II) - reading groups

Reading groups (rg) offered in academic year 2022-23 (xxxvii cycle – ii year) for the curriculum in economics (eco):.

I term (October 2022 – December 2022)

-  Social Network Theory  (Instructor: Prof. F. Panebianco, Catholic University of Milano)

-  Applications of Game Theory  (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

-  Empirical Banking  (Instructor: Prof. Elena Beccalli, Catholic University of Milano)

-  Advanced Asset Pricing and Portfolio Management  (Instructor: Prof. A. Tarelli, Catholic University of Milano)

-  Empirical Corporate Finance  (Instructor: Prof. E. Croci, Catholic University of Milano)

-  Programming in Python  (Instructor: Prof. L. Viarengo, Catholic University of Milano)

II term (January 2023 – April 2023)

-  Spatial Models  (Instructor: Prof. S. Colombo, Catholic University of Milano)

-  Financial Frictions  (Instructor: Prof. D. Delli Gatti, Catholic University of Milano)

-  The Microeconomics of International Trade  (Instructor: Prof. V. Gattai, University of Milano-Bicocca)

-  Innovation and Industrial Evolution  (Instructor: Prof. C. Garavaglia, University of Milano-Bicocca)

-  Structural VAR Models  (Instructors: Proff. V. Colombo, G. Rivolta, Catholic University of Milano)

-  Applied Health Economics and Policy  (Instructors: Proff. G. Turati, E. Cottini, L. Salmasi, Catholic University of Milano)

Note:  the RG for the curriculum ECO are offered jointly with the PhD in Economics and Finance of the Catholic University of Milano (CUM). CUM is in charge of the timetable of each RG, whose updated version can be found  here . 

The following extra-RG are offered by ECOSTATDATA in the II term:

-  Expected Utility and Decision Theory  (Instructor: Prof. G. Cassese, University of Milano-Bicocca)

-  Estimated DSGE Models  (Instructor: Prof. Alice Albonico, University of Milano-Bicocca)

-  Authority and Delegation  (Instructor: Prof. Irene Valsecchi, University of Milano-Bicocca)

Note:  the timetable of the extra-RG is available  here . 

Reading Groups (RG) offered in academic year 2022-23 (XXXVII cycle – II year) for the curriculum in Statistics (STAT):

I term (October 2022 – December 2022)

-  The Dependent Dirichlet Process and Related Models  (Instructors: Proff. F. Camerlenghi, B. Nipoti, University of Milano-Bicocca)

-  Some Issues in Statistical Modelling  (Instructor: Prof. R. Borgoni, University of Milano-Bicocca)

-  Empirical Bayes in Bayesian Inference  (instructor: Prof. S. Rizzelli, Catholic University of Milano)

-  Automated Machine Learning & Neural Architectural Search  (Instructor: Prof. A. Candelieri, University of Milano-Bicocca)

-  Deep Learning  (Instructor: Prof. M. Borrotti, University of Milano-Bicocca)

Note:  the timetable of the RG for the curriculum STAT is available  here . 

Reading Groups (RG) offered in academic year 2022-23 (XXXVII cycle – II year) for the curriculum Big Data & Analytics for Business (BIDAB):

II term (January 2023 – April 2023)

-  Databases for Structured and Unstructured Data – SQL  (POSTPONED) (Instructor: Prof. F. Mercorio, University of Milano-Bicocca)

-  Human-centered AI  (Instructor: Prof. F.M. Zanzotto, University of Roma-Tor Vergata)

Note:  the timetable of the RG for the curriculum BIDAB is available  here . 

XXXVIII cycle - Teaching activities - Year I (terms I - II - III - IV) - courses

The I term teaching activities start on 24 October 2022 and end on 23 December 2022. The I term exam session starts on 9 January 2023 and ends on 13 January 2023.

Note:  the timetable of the I term courses is available  here

The courses/modules offered during the I term for the curriculum Economics (ECO) are:

-  Computational Statistics I  (Instructor: Prof. G. Bertarelli, University of Pisa)

-  Mathematics – Linear algebra  (Instructor: Prof. N. Pecora, Catholic University of Milano)

-  Mathematics I  (Instructor: Prof. D. Visetti, University of Milano-Bicocca);

-  Mathematics II  (Instructor: Prof. F. Cavalli, University of Milano-Bicocca);

-  Mathematics III  (Instructor: Prof. M. Longo, Catholic University of Milano)

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

-  Mathematical Analysis  (Instructors: Prof. C. Zanco, University of Milano; Proff. C.A. De Bernardi, E. Miglierina, Catholic University of Milano)

-  Numerical Optimization  (Instructor: Prof. L. Mascotto, University of Milano-Bicocca) 

The courses/modules offered during term I for the curriculum Big Data & Analytics for Business (BiDAB) are:

-  Programming in Python  (Instructor: Prof. M. Cesarini, University of Milano-Bicocca)

-  Architecture for Big Data Processing  (Instructor: Prof. V. Moscato, University of Napoli)

-  Architecture for Big Data Processing Lab  (Instructor: Prof. G. Sperlì, University of Napoli)

The II term teaching activities start on 16 January 2023 and end on 5 April 2023. The II term exam session starts on 17 April 2023 and ends on 21 April 2023. 

The courses/modules offered during the II term for the curriculum Economics (ECO) are:

-  Econometrics I  (Instructor: Prof. M. Manera, University of Milano-Bicocca)

-  Econometrics I – Tutorials  (Instructor: Dr. C. Cattaneo, European Institute on Economics and the Environment)

-  Econometrics II  (Instructor: Prof. M.L. Mancusi, Catholic University of Milano)

-  Econometrics II – Tutorials  (Instructor: Dr. E. Villar, Catholic University of Milano)

-  Econometrics III  (Instructor: Prof. A. Ugolini, University of Milano-Bicocca)

-  Econometrics III - Tutorials  (Instructor: Dr. D. Valenti, Fondazione Eni Enrico Mattei)

-  Microeconomics I  (Instructor: Prof. M. Mantovani, University of Milano-Bicocca)

-  Microeconomics I – Tutorials  (Instructor: Dr. F. Campo, University of Milano-Bicocca)

-  Microeconomics II  (Instructtor: Prof. M. Gilli, University of Milano-Bicocca)

-  Microeconomics II – Tutorials  (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

-  Microeconomics III  (Instructor: Prof. L. Colombo, Catholic University of Milano)

-  Microeconomics III – Tutorials  (Instructor: Dr. D. Bosco, University of Milano-Bicocca)

-  Microeconomics IV  (Instructor: Prof. P. Bertoletti, University of Milano-Bicocca)

-  Microeconomics IV – Tutorials  (Instructor: Dr. G. Crea, University of Pavia)

Note:  the timetable of the II term courses for the curriculum ECO is available  here .

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

-  Probability I & II  (Instructor: Prof. F. Camerlenghi, University of Milano-Bicocca)

-  Stochastic Processes  (Instructor: Prof. B. Buonaguidi, Catholic University of Milano)

-  R for Data Science  (Instructor: Prof. A. Gilardi, University of Milano-Bicocca)

-  Statistical Inference I  (Instructor: Prof. A. Caponera, University of Milano-Bicocca)

Note:  the timetable of the II term courses for the curriculum STAT is available  here .

The courses/modules offered during the II term for the curriculum Big Data & Analytics for Business (BIDAB) are:

-  Probability  (Instructor: Prof. A. Di Brisco, University of Piemonte Orientale)

-  Statistical Inference I  (Instructor: Prof. R. Ascari, University of Milano-Bicocca)

Note:  the timetable of the II term courses for the curriculum BIDAB is available  here .

The III term teaching activities start on 26 April 2023 and end on 7 July 2023. The III term exam session starts on 17 July 2023 and ends on 21 July 2023. 

The courses/modules offered during the III term for the curriculum Economics (ECO) are:

- Macroeconomics I (Instructor: Prof. G. Femminis, Catholic University of Milano)

- Macroeconomics II (Instructor: Prof. A. Albonico, University of Milano-Bicocca)

- Macroeconomics III (Instructor: Prof. R. Masolo, Catholic University of Milano)

- Macroeconomics IV (Instructor: Dr. B. Barbaro, University of Milano-Bicocca)

-  Computational Statistics II  (Instructor: Prof. A. Pini, Catholic University of Milano)

-  Research Methods  (Instructors: Prof. T. Colussi, Catholic University of Milano; Prof. K. Aktas, University of Milano-Bicocca)

- Finance I – Empirical Corporate Finance (Instructor: Prof. A. Signori, Catholic University of Milano)

- Finance II – Asset Pricing Theory (Instructor: Prof. A. Sbuelz, Catholic University of Milano)

- Finance III – Banking (Instructors: Proff. M. Migliavacca, F. Pampurini, Catholic University of Milano)

Note:  the timetable of the III term courses for the curriculum ECO is available here .

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

-  Statistical Inference II  (Instructor: Prof. A. Solari, University of Milano-Bicocca)

- Bayesian Statistics (Instructors: Prof. R. Argiento, University of Bergamo; Proff. B. Nipoti, T. Rigon, University of Milano-Bicocca)

- Data Management (CANCELLED)

Note:  the timetable of the III term courses for the curriculum STAT is available here .

The courses/modules offered during the III term for the curriculum Big Data & Analytics for Business (BIDAB) are:

- Technology and Innovation Management (Instructors: Proff. S. Torrisi, L. D'Agostino, F. Di Pietro, M. Guerzoni, University of Milano-Bicocca)

- Machine Learning (Instructor: Prof. L. Malandri, University of Milano-Bicocca)

- Natural Language Understanding (CANCELLED)

-  Social Media Analytics  (Instructor: Prof. R. Boselli, University of Milano-Bicocca)

Note:  the timetable of the III term courses for the curriculum BIDAB is available here .

The IV term teaching activities start on 4 September 2023 and end on 20 October 2023. The IV term exam session starts on 23 October 2023 and ends on 27 October 2023. 

Note:  the timetable of the IV term courses is under construction and is currently shared with all the ECOSTATDATA students, who can monitor online any updates/modifications.

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

- Statistical Learning (POSTPONED)

- Statistical Modelling I (Instructor: Prof. F. Castelletti, Catholic University of Milano)

- Statistical Modelling II (Instructor: Prof. F. Greselin, University of Milano-Bicocca)

- Statistical Modelling III (Instructor: Dr. S. Verzillo, European Commission - Joint Research Center)

- Statistical Modelling IV (Instructors: Prof. F. Pennoni, University of Milano-Bicocca; Prof. F. Bartolucci, University of Perugia)

The courses/modules offered during the IV term for the curriculum Big Data & Analytics for Business (BIDAB) are:

- Statistical Inference II (Instructor: Prof. R. Ascari, University of Milano-Bicocca)

- Explainable AI for Business Value (Instructor: Prof. F. Mercorio, University of Milano-Bicocca)

- Deep Learning and Computer Vision for Business (Instructor: Prof. E. Frontoni, Polytechnic University of Marche, TBC)

XXXVIII cycle - Teaching activities - Year II (terms I - II) - reading groups

Reading groups (rgs) offered in academic year 2023-24 (xxxviii cycle – ii year) for the curriculum economics (eco):.

I term (October 2023 – December 2023) and II term (January 2024 – April 2024)

Note:  the RGs for the curriculum ECO are offered jointly with the PhD in Economics and Finance of the Catholic University of Milano. Detailed information on each RG and its timetable can be found  here . 

Reading Groups (RGs) offered in academic year 2023-24 (XXXVIII cycle – II year) for the curriculum Statistics (STAT):

I term (November 2023 – December 2023) and II term (January 2024 – April 2024)

Note:  the timetable of the RGs for the curriculum STAT is shared online (via Google Calendar) with students officially enrolled in the PhD program. 

- RG Approximate Bayesian Computational Methods (Instructor: Dr. A. Fasano, Catholic University of Milano)

- RG Automated Machine Learning & Neural Architectural Search (Instructor: Prof. A. Candelieri, University of Milano-Bicocca)

- RG Spatio-temporal Data (Instructors: Prof. R. Borgoni, Dr. P. Maranzano, University of Milano-Bicocca)

- RG Some Issues on Statistical Modelling (Instructor: Prof. R. Borgoni, University of Milano-Bicocca)

- RG Deep Learning (Instructor: Prof. M. Borrotti, University of Milano-Bicocca)

Reading Groups (RGs) offered in academic year 2023-24 (XXXVIII cycle – II year) for the curriculum Big Data & Analytics for Business (BIDAB):

I term (November 2023 - December 2023) and   II term (January 2024 – April 2024)

Note:  the timetable of the RGs for the curriculum BIDAB is shared online (via Google Calendar) with students officially enrolled in the PhD program.

- RG Natural Language Processing (Instructor: Dr. A. Seveso, University of Milano-Bicocca)

- RG Generative AI (Instructor: Dr. Navid Nobani, University of Milano-Bicocca)

XXXIX cycle - Teaching activities - Year I (terms I - II) - courses

The I term teaching activities start on 23 October 2023 and end on 22 December 2023. The I term exam session starts on 8 January 2024 and ends on 12 January 2024.

Note:  the timetable of the I term courses is shared online (via Google Calendar) with all students officially enrolled in the PhD program.

- Microeconomics I (Instructor: Prof. Marco Mantovani, University of Milano-Bicocca)

-  Mathematical Analysis I-II-III  (Instructors: Prof. J. Somaglia, Polytechnic of Milano; Proff. C.A. De Bernardi, E. Miglierina, Catholic University of Milano)

-  Architecture for Big Data Processing & Lab  (Instructors: Proff. V. Moscato and G. Sperlì, University of Napoli)

The II term teaching activities start on 15 January 2024 and end on 27 March 2024. The II term exam session starts on 8 April 2024 and ends on 12 April 2024.

Note:  the timetable of the II term courses is shared online (via Google Calendar) with all students officially enrolled in the PhD program.

- Microeconomics II (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

- Microeconomics III (Instructors: Prof. L. Colombo and Dr. M. Magnani, Catholic University of Milano)

- Microeconomics IV (Instructors: Prof. P. Bertoletti, University of Milano-Bicocca, and Dr. G. Crea, University of Pavia)

- Econometrics I (Instructors: Prof. M. Manera, University of Milano-Bicocca, and Dr. C. Cattaneo, European Institure on Economics and the Environment)

- Econometrics II (Instructors: Prof. A. Ugolini, University of Milano-Bicocca, and Dr. D. Valenti, Polytechnic of Milano)

- Econometrics III (Instructors: Prof. M.L. Mancusi and Dr. E. Villar, Catholic University of Milano)

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

- Probability I-II (Instructor: Prof. F. Camerlenghi, University of Milano-Bicocca)

- Stochastic Processes (Instructor: Prof. B. Buonaguidi, Catholic University of Milano)

- Statistical Inference I (Instructor: Dr. A. Caponera, Luiss Guido Carli University) 

- R for Data Science (Instructor: Dr. A. Gilardi, Polytechnic of Milano)

The courses/modules offered during the II term for the curriculum Big Data & Analytics for Business (BiDAB) are:

- Probability (Instructor: Prof. A. Di Brisco, University of Insubria)

-  Statistical Inference I (Instructor: Dr. R. Ascari, University of Milano-Bicocca)

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Statistical sciences.

The PhD Course in Statistics aims at training specialists in the fields of data management and analysis and leads to a wide range of career opportunities, both in academic and research institutions.

  Find out more

The doctoral programme aims to train international specialists in the fields of data management and data analysis. The programme leads to a wide range of career opportunities, both at academic level and in other highly qualified research institutions. The study project of the doctoral course is carried out according to the following steps. Start of activities. On arrival, students are informed of the regulations and the course programme for the first few months. Each student is assigned a tutor, who is available to help and guide them in their choice of research topic for the second and third year. First-year programme. The basic first-year programme comprises a core of five compulsory courses covering advanced mathematics, probability theory, computer programming, statistical theory and modelling. In addition, a number of specialised modules are offered, covering a range of more advanced topics. A side objective of the courses during the first year is to train students in group work, seminar presentations and the preparation of scientific papers. At the end of the first year, the Academic Board assesses doctoral students for admission to the second year. Admission is conditional on achieving a satisfactory level in the first year's activities. For each of the five core modules, assessment is based on a final examination. By September of the first year, students admitted to the second year propose to the Academic Board their research programme to be developed during the second and third years. Students may join local research groups or start an independent research project. The PhD programme and the Department of Statistical Sciences can provide a suitable research environment for this. Broad areas of research include: statistical methodology and its applications; statistical and econometric methods; social statistics; demography. After approval of the project, the Academic Board assigns a supervisor to each PhD student. Research programme in the second and third year. The research activity in the second and third year is the distinctive feature of the PhD programme and is aimed at achieving independent research capabilities. During the second and third years up to 12 months may be spent at a university or other highly qualified institution abroad. Students are strongly encouraged to include a period of research abroad in their programme, taking advantage of the national and international collaborative networks of the members of the Doctoral Board. The result of the research must be presented as a thesis containing original scientific results relevant to the field of statistics and its applications.

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The phd course, partner departments, duration, admission, scholarships, study programme, research areas, career opportunities.

The doctoral programme in Statistics at University of Padua was introduced in 1984, and was among the first Ph.D. programmes in Italy.

Starting in the academic year 2004/2005, the Department of Statistical Sciences of the University of Padua became the administrative home of the Ph.D. in Statistics.

The new programme replaced two previous research doctoral programmes in Statistics and in Statistics Applied to the Economic and Social Sciences.  

The Ph.D. Course aims to provide an advanced training to support both applied and theoretical research, encouraging independence of both study and research. For a short description of the Course activities see the notice .

Partner Departments in the Ph.D. programme comprise:

 -  Department of Environmental Sciences, Computer Sciences and Statistics, University Ca' Foscari of Venice

 -  Department of Economics and Statistical Sciences, University of Udine

The PhD programme in Statistics has an expected length of 3 years. About 8 students enter the programme every year. The School aims at selecting the best candidates applying from all over the country as well as from abroad. Admission is by competition which is open to people of all ages and citizenship who already have a Diploma di Laurea (vecchio ordinamento) or Laurea Specialistica/Magistrale (nuovo ordinamento), issued in Italy, or an academic qualification of equivalent level issued by a foreign university and recognized by the academic authorities, or by interuniversity agreement of cooperation and mobility.

Basic quantitative prerequisites are assumed.

Competitions are held annually, with details being published within the Spring of each year. Courses start the following October. 

For full information, see the Admission section and the page of University of Padua competition announcement . 

All positions are supported by scholarships from the University of Padua or from other institutions (awarded on the basis of qualifications and oral examination).  The gross annual amount of the scholarship is 16,243.00 euros , in accordance with the amount set by the D.M. MUR n. 247 of 23/01/2022. For periods of study abroad (up to 12 months) the scholarship value is increased by 50%. Moreover, an annual research budget equal to 10% of the gross annual amount of the scholarship is available for the 2nd and 3rd year. The China Scholarship Council (CSC) , offers n. 1 scholarship to candidates from the People's Republic of China .

See the Admission section and the page of University of Padua competition announcement  for updates. Please, follow carefully the instructions for application in the announcement. For any additional information, please contact [email protected] .

The first year is devoted to courses, both core courses on Functional Analysis, Probability Theory, Programming Methodologies for Data Analysis, Statistical Theory and Statistical Modelling as well as short, more specialised, courses on topics such as: Time Series, Survival Analysis, Nonparametric Statistics, Survey Methodology, etcetera. Lectures are given in English . Students can also follow some upper-division courses (Laurea Magistrale) at the Department of Statistical Sciences or other Departments at the University of Padova .   Attendance to all courses is compulsory .

At the end of the first year, PhD candidates propose to the Course Collegiate their own research programme to be carried over during the second and third year. They can either join local research groups at Padova or partner departments or start an independent research. The Course Collegiate assigns a supervisor to each PhD student. Original research results should be presented in the PhD thesis, to be discussed both in front of the Course Collegiate and (subject to positive evaluation by the Collegiate) in front of a Committee composed of Italian and foreign experts on the thesis topic. Intermediate results at the end of the second year are to be presented as a department seminar. During the second and third year up to 12 months can be spent at a University or other highly qualified institution abroad. In this case the grant is 50% more, for the whole period spent abroad. The thesis can be written either in Italian or in English.

For an idea of possible research subjects, see the section Alumni and the research pages of the Department of Statistical Sciences , University of Padua and of partner departments. 

Broad areas of research include:  

Statistical methodology and its applications . Methodological aspects range from statistical models to inference and computational issues. Applications may concern a variety of fields such as technology, industry, finance, biology, medicine, environmental studies, etc.

Statistical methods and applications in Economics . In particular: time series analysis, forecasting, statistical methods for labour economics and evaluation of public policies.

Social Statistics . In particular, survey methodology, models for individual ad aggregated data, segmentation techniques, multilevel models.

Demography . In particular, population structure and dynamics, statistical analysis of demographic behaviours and policies.

As a general objective, the Course aims at training specialists at an international level in the fields of data management and analysis. The programme leads to a wide range of career opportunities, both at the academic level and in other highly qualified research institutions. See the section Alumni for details on the current occupation of PhDs from The Department of Statistical Sciences starting from 1987. 

Students having completed one of the doctoral programmes in Statistics at Padua now have positions in a wide range of academic, public and private institutions, both in Italy and abroad.

Tab. 1   Some statistics related to the professional results of the research's doctors of the PhD School/Course of the Department of Statistical Sciences of the University in Padua

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PhD positions are now open

PhD positions open -->

The International School for Advanced Studies (SISSA) welcomes applications for PhD fellowships in Statistical Physics for the academic year 2022–2023.

The students are selected through an oral exam held at SISSA, Trieste (Italy) or via videoconference (Zoom). The deadline for the application is March 8th, 2022.

Further information and application forms are at the links below. The candidates are entitled to reimbursement of travel expenses. For information about the Ph.D. program, contact statphys [at] sissa [dot] it .

• PhD announcement, deadlines, and online application form
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Students' representative: Carlo Vanoni Phone: +39 040 3787 308

Webpage Admin: Giacomo Bracci Testasecca Michele Fossati Francesco Gentile Cristiano Muzzi E-Mail: [email protected]

Filippo Ascolani received his PhD in Statistics at Bocconi University in 2024 and he is starting as an Assistant Professor in the Department of Statistical Science at Duke University. Previously he obtained a B.Sc. in Mathematics and a M.Sc. in Stochastics and Data Science from University of Torino, Italy. His main research interests lie in Bayesian nonparametric inference for data displaying complex dependence structures, with an emphasis on the associated theoretical properties. Currently, he is also working on the theory of Bayesian computation and scalable Markov Chain Monte Carlo methods.

This will be one of three Lawrence D. Brown PhD Student Award winners’ talks in a special session at the 11th World Congress in Probability and Statistics in Bochum, Germany, August 12–16, 2024. (See below for how to apply for next year’s award.)

Nonparametric priors with full-range borrowing of information

Real phenomena often exhibit a high level of heterogeneity: datasets may often refer to different features, populations, or, in general, may be recorded under different experimental, though related, conditions. Such situations entail significant opportunities for borrowing information across different samples and groups of data.

It is, then, apparent that modeling the dependence structure across heterogeneous data is crucial for any statistical inference, since it directly impacts the borrowing of information. In a Bayesian framework this is done through models for partially exchangeable data, where only observations belonging to the same group can be permuted without affecting the overall distribution. Despite the extensive advances over the last two decades, most available proposals in the Bayesian nonparametric framework allow only for borrowing information by reinforcement. Indeed, the usual procedure consists of shrinking the estimates for different samples towards each other: shrinkage is justified by the fact that distributions of different, but related, populations are expected to be similar in terms of shape and/or location. However many phenomena, e.g. analysis of the returns of different financial assets or survival times and abundances of competitive species, may naturally lead to negative correlation between the samples.

In this work we derive a new class of dependent nonparametric priors that can induce correlations of any sign. We show that the constraint of positive correlation in most of the available methods is due to the sharing of atoms between random measures modelling the group-specific distributions: analogously, we show that going beyond this assumption allows for a more flexible idea of borrowing of information. This is achieved thanks to a novel concept, termed hyper-tie, that generalizes the standard notion of tie arising in exchangeable models and represents a direct and simple measure of dependence.

Our proposal is based on the normalization of Completely Random Vectors, which are random elements whose realizations are vectors of discrete finite measures. In particular, we leverage their amenable analytical tractability to derive explicit distributional properties, which allows us to tune the dependence both within and across groups. Moreover, we obtain a characterization of the posterior distribution and devise algorithms for posterior inference. We illustrate our model by analyzing the relation between stocks and bonds over the same temporal frame and in the problem of clustering multivariate responses with missing entries.

This a joint work with Beatrice Franzolini, Antonio Lijoi and Igor Prünster.

Apply for next year’s PhD Student Award

The IMS Lawrence D. Brown PhD Student Award is open for applications. The deadline is May 1, 2024 . Eligible applicants compete to be one of three speakers at an invited session as part of the IMS Annual Meeting (the 2025 Joint Statistical Meetings , in Nashville, USA, August 2–7, 2025). The award includes reimbursement for travel and meeting registration fee (up to $2,000 for each recipient).

The award was created in memory of Lawrence D. Brown (1940–2018), professor of statistics at The Wharton School, University of Pennsylvania, who was an enthusiastic and dedicated mentor to many graduate students. For application details see: https://imstat.org/ims-awards/ims-lawrence-d-brown-ph-d-student-award/

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