MONIA RANALLI

A.A. 2024/2025 - II semestre

[L-8 Ingegneria Gestionale]

Le lezioni del corso di "Statistica" inizieranno il 03/03/2025 a distanza.
Tutti gli appelli di esame saranno esclusivamente in presenza.
 
Lunedì 8-11 
Martedì 8-10

 
Collegamento zoom
https://uniroma1.zoom.us/j/81779261508?pwd=MUZ3OGVxMDBYeFJCaFg5OEdoc0R0UT09
 
Meeting ID 817 7926 1508
 
Passcode 955765
 
Il materiale didattico sarà disponibile sulla pagina Moodle del corso.

 
 [L-41 SG, SES, SEFA]

 
Le lezioni del corso di "Laboratorio di software statistici" inizieranno 26/02/2025 a distanza e seguiranno l'orario seguente.
 
Mercoledì 17-19
 
Collegamento zoom
https://uniroma1.zoom.us/j/85838609792?pwd=MG4rRmc4Yk9wYi96WFMyeG84QUpEQT09
 
Meeting ID
858 3860 9792
 
Passcode
818645
 
Il materiale didattico sarà disponibile sulla pagina Moodle del corso.
Tutte le prove di esame si terranno esclusivamente in presenza.

 
[L-31 Applied Computer Science and Artificial Intelligence]

For more information on the lectures’ schedule for the "Statistics" course, please visit https://corsidilaurea.uniroma1.it/en/corso/2022/30786/home
The course will start on 27/02/2025.
 
Schedule:
Monday 17-19
Thursday 17-20
 
Link zoom
https://uniroma1.zoom.us/j/81811689013?pwd=ZGxzMEpQYUM1aWJSSHRGQWJnV21XQT09
 
Meeting ID
818 1168 9013
 
Passcode 
362601
 
All course materials will be available on the Moodle course page.
 
All the exams are only in presence.

A.A. 2023/2024 - II semestre

[LM-82 Scienze Statistiche]

Le lezioni del corso di "Analisi dei dati di sopravvivenza e longitudinali" inizieranno il 26/02/2024 a distanza.

La seconda parte del corso (dopo le vacanze di Pasqua) si terrà in presenza dal prof. Marco Alfò.

Tutti gli appelli di esame saranno esclusivamente in presenza.

Lunedì 17-19 

Martedì 17-19 

Mercoledì 8-10 

Collegamento zoom

https://uniroma1.zoom.us/j/81779261508?pwd=MUZ3OGVxMDBYeFJCaFg5OEdoc0R0UT09

Meeting ID 817 7926 1508

Passcode 955765

La prova intermedia si terrà il giorno 3 Aprile alle ore 10 in aula VII.

Il materiale didattico sarà disponibile sulla pagina Moodle del corso.

 [L-41 Statistica Gestionale

L-41 Statistica, Economia e Società]

Le lezioni del corso di "Laboratorio di software statistici" inizieranno 26/02/2024 a distanza e seguiranno l'orario seguente.

Lunedì 10.30-12 (senza pausa)

Collegamento zoom

https://uniroma1.zoom.us/j/85838609792?pwd=MG4rRmc4Yk9wYi96WFMyeG84QUpEQT09

Meeting ID

858 3860 9792

Passcode

818645

Il materiale didattico sarà disponibile sulla pagina Moodle del corso.

Tutte le prove di esame si terranno esclusivamente in presenza.

[L-31 Applied Computer Science and Artificial Intelligence]

For more information on the lectures’ schedule for the "Statistics" course, please visit https://corsidilaurea.uniroma1.it/en/corso/2022/30786/home

The course will start on 26/02/2024.

Schedule:

Monday 9-10.30 

Tuesday 9-12

Link zoom

https://uniroma1.zoom.us/j/81811689013?pwd=ZGxzMEpQYUM1aWJSSHRGQWJnV21XQT09

Meeting ID

818 1168 9013

Passcode 

362601

All course materials will be available on the Moodle course page.

All the exams are only in presence.

MONIA RANALLI
Curriculum Vitae
Place Roma
Date 22/10/2025

Part I – General Information

Full Name Monia Ranalli
E-mail monia.ranalli@uniroma1.it

Spoken Languages Italian, English (advanced)

Part II – Education

Type Year Institution Notes (Degree Experience,…)
PhD 2014 Università Sapienza Dissertation title:
New perspective on likelihood-based inference for latent and observed Gaussian mixture models. Advisor: Prof. Roberto Rocci
Research Interests: Finite Mixture Models, Ordinal Data, Composite Likelihood Methods, Monte
Carlo Likelihood
Visiting PhD student in Statistics 2014 Penn State University Jan 2014 - Nov 2014
Visiting Advisor: Prof. Bruce Lindsay.
Post-graduate studies 2012 Warwick University (UK) With Distinction
Dissertation title: Modelling skewed data.
Supervisor: Prof. Mark Steel
University graduation
MSc 2011 Università degli Studi di Roma "Tor Vergata" 110/110 cum summa laude with honors
Dissertation title: A mixture Gaussian Hidden Markov model: a flexible approach to analyze heterogeneous returns. Supervisor: Prof. Roberto Rocci
University graduation
BSc in Economics & Finance 2009 Università degli Studi di Roma "Tor Vergata" 110/110 cum summa laude
Dissertation title: A logit model to estimate the probability of default.
Supervisor: Prof. Roberto Rocci
Classical A levels 2006 Liceo Classico ”Ugo Foscolo”-Maxisperimentale BROCCA, Albano
Laziale-Rome 100/100

Part III – Appointments

IIIA – Academic Appointments
Start End Institution Position
Jul 24 Department of Statistics, Sapienza University
of Rome Member of the Internship Committee
May 23 Feb25 Sapienza University
of Rome Member of the Sapienza’s Scientific Research Committee
1/9/23 Department of Statistics, Sapienza University
of Rome Associate Professor in Statistics SECS-S/01 13/D1
Jan 21 Department of Statistics, Sapienza University
of Rome Member of the Board (Collegio dei Docenti) for the Ph.D. Programme in Statistics
19/6/20 19/6/29 MUR Scientific national habilitation (ASN) as Associate Professor of Statistics (Abilitazione Nazionale
Professore Associato)
Oct 20 Dec 22 Department of Statistics, Sapienza University
of Rome Member of the board on ”Honorous Programmes” (”Percorsi di eccellenza”) of the Degree in
”Statistics”
Nov 19 Oct 22 Sapienza University
of Rome Elected Member of the Faculty Board (Giunta di Facolt`a) as Representative of researchers of
the Department of Statistics. Sapienza University of Rome – Faculty of Information Engineering,
Informatics, and Statistics.
Nov 19 Department of Statistics, Sapienza University
of Rome Member of of the board on Quality Assessment of the Degree in ”Statistics, Economics
and Society”. Department of Statistics. Sapienza University of Rome – Faculty of Information
Engineering, Informatics, and Statistics.
Nov 19 Oct 22 Department of Statistics, Sapienza University
of Rome Elected Member of the Department Board (Giunta di Dipartimento) as Representative of
researchers. Sapienza University of Rome – Department of Statistics.
2/9/19 1/9/22 Department of Statistics, Sapienza University
of Rome Tenure Track Assistant Professor in Statistics
Ricercatore SECS-S/01, Legge 240/10 tipo B.
Maternity Leave 26/11/2021 – 26/04/2022
3/4/18 2/4/21 Dipartimento di Economia e Finanza - Università degli Studi di Roma “Tor Vergata” RTDA SECS-S/01 13/D1
3/4/17 31/3/18 Dipartimento di Scienze Politiche – Università Roma Tre Assegno di Ricerca SECS-S/01 13/D1
1/10/15 30/9/16 Dipartimento di Scienze Politiche – Università Roma Tre Assegno di Ricerca SECS-S/01 13/D1
12/1/15 11/1/16 Dipartimento di Statistica – Penn State University (USA) Post-Doc SECS-S/01 13/D1

Other
20/08/25 22/01/26 Maternity Leave
26/11/21 25/04/22 Maternity Leave

Part IV – Teaching experience as Instructor

Year Institution Lecture/Course
Spring 25 Department of Statistics, University of Sapienza, Rome R, course in Italian at Master in Data Intelligence and Decision Strategies (15 hours)
Spring 25 Department of
Informatics, University of Sapienza, Rome Statistics, course in English at Bachelor Degree in Applied Computer Science and Artificial Intelligence (60 hours)
Spring 25 Department of Statistics, University of Sapienza, Rome Lab of Statistical Software, course in Italian at Bachelor Degree in Statistics (27 hours).
Spring 25 Department of Engineering, University of Sapienza, Rome Statistics, course in Italian at Bachelor Degree in Management Engineering (60 hours)
Spring 24 Department of
Informatics, University of Sapienza, Rome Statistics, course in English at Bachelor Degree in Applied Computer Science and Artificial Intelligence (60 hours)
Spring 24 Department of
Statistics, University of Sapienza, Rome Analysis of Longitudinal and survival data, course in Italian at Master Degree in Statistics – Longitudinal data Module (32 hours).
Spring 24 Department of Statistics, University of Sapienza, Rome Lab of Statistical Software, course in Italian at Bachelor Degree in Statistics (27 hours).
Spring 23 Department of Informatics, University of Sapienza, Rome Statistics, course in English at Bachelor Degree in Applied Computer Science and Artificial Intelligence (60 hours).
Spring 23 Department of Statistics, University of Sapienza, Rome Analysis of Longitudinal and survival data, course in Italian at Master Degree in Statistics – Longitudinal data Module (32 hours).
Spring 23 Department of Statistics, University of Sapienza, Rome Lab of Inferential Statistics, course in Italian at Bachelor Degree in Statistics (27 hours).
Fall 22 Department of
Statistics, University of Sapienza, Rome Statistical Theory (Module II), PhD course in English (24 hours, 3CFU).
Spring 21 Department of
Statistics, University of Sapienza, Rome Statistical Theory (Module II), PhD course in English (24 hours, 3CFU).
Spring 21 Department of Engineering, University of Sapienza, Rome Statistics, course in Italian at Bachelor Degree in Management Engineering (60 hours)
Spring 21 Department of Statistics, University of Sapienza, Rome Lab of Statistical Software, course in Italian at Bachelor Degree in Statistics (27 hours).
Dec 20 Department of Statistics,
University of Sapienza, Rome Some applications using R Markdown and R Sweave, contribution in Italian within the
Honorous Programmes of the Degree in Statistics (4 hours).
Fall 20 Department of Economics, University of Tor Vergata, Rome Statistics, course in English at Master in Development Economics and International
Co-operation (MESCI).
Spring 20 Department of Engineering, University of Sapienza, Rome Statistics, course in Italian at Bachelor Degree in Management Engineering (60 hours).
Spring 20 Department of
Statistics, University of Sapienza, Rome Composite Likelihood Methods - PhD course in English (8 hours).
Spring 20 Department of Economics and
Finance, University of Tor Vergata, Rome Latent Variable Models - PhD course in English (3 CFU).
Fall 19 Department of Statistics, University of Sapienza, Rome Lab of Statistical Software, course in Italian at Bachelor Degree in Statistics (36 hours).
Fall 19 Department of Economics, University of Tor Vergata, Rome Statistics, course in English at Master in Development Economics and International
Co-operation (MESCI).
Spring 19 Department of Economics
and Finance, University of Tor Vergata, Rome Statistical learning - Undergraduate course in English (2 CFU).
Spring 19 Department of Economics and
Finance, University of Tor Vergata, Rome Latent Variable Models - PhD course in English (3 CFU).
Fa 2018 Università degli Studi di Roma Tor Vergata – Department of Economics and Finance Statistical Tools for Decision-Making (7 CFU; Descriptive statistics, Introduction to Probability and Inferential Statistics). Undergraduate course in English.
Su 2018 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Introduction to Statistics (20 h). Preparatory course in English at Master of Science in European Economy and Business Law.
Sp 2018 Università Roma Tre– Department of Political Sciences Statistics. Undergraduate course in Italian (8 CFU)
Fa 2017 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Statistical Tools for Decision-Making (3 CFU; Descriptive statistics). Undergraduate course in English.
Su 2017 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Introduction to Statistics. Preparatory course in English at Master of Science in European Economy and Business Law. (12 h)
Sp 2017 Università Roma Tre– Department of Political Sciences Statistics. Undergraduate course in Italian (8 CFU)
Sp 2016 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Marketing Analytics Lab. Course in English at Master of Science in Big Data in Business (Co-Instructor with Prof. Roberto Rocci)
Fa 2015 Penn State University (USA) – Department of Statistics STAT462 Applied Regression Analysis. Undergraduate Course in English.

Part IV – Teaching experience as Teaching Assistant
Year Institution Lecture/Course
Spring 19 Department of
Management and Law, University of Tor Vergata, Rome SAS Labs for Business Statistics, Graduate course in English.
Course Instructor: Prof. Simone
Borra
Jan-Feb 19 University of Tor Vergata, Rome Lab activities in English for Unsupervised learning (MSc in Big Data in Business)
Course Instructor: Prof. Roberto Rocci
Fa 2018 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Statistical Tools for Decision-Making. Undergraduate course in English. Course instructors: Dr. Monia Ranalli (7 CFU); Prof. Roberto Rocci (2 CFU)
Fa 2018 LUISS – Department of Political Sciences Statistics. Undergraduate course in English. Instructor: Prof. Roberto Rocci (8 CFU)
Sp 2018 LUISS – Department of Economics and Finance Statistics. Undergraduate course in English. Instructor: Dr. Alessio Troiani (8 CFU)
Sp 2018 Università degli Studi di Roma Tor Vergata– Department of Management and Law SAS Labs for Business Statistics. Graduate course in English. Course Instructor: Prof. Simone Borra (6 CFU)
Fa 2017 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Statistics. Course in English at Master in Development Economics and International Cooperation. Course instructor: Prof. Roberto Rocci
Fa 2017 LUISS – Department of Political Sciences Statistics. Undergraduate course in English. Instructor: Prof. Roberto Rocci (8 CFU)
Fa 2017 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Statistical Tools for Decision-Making. Undergraduate course in English. Course instructors: Dr. Monia Ranalli (3 CFU); Prof. Roberto Rocci (6 CFU)
Sp 2017 LUISS – Department of Economics and Finance Statistics. Undergraduate course in English. Instructor: Prof. Brunero Liseo (8 CFU)
Sp 2017 Università degli Studi di Roma Tor Vergata– Department of Management and Law SAS Labs for Business Statistics. Graduate course in English. Course Instructor: Prof. Simone Borra (6 CFU)
Fa 2016 LUISS – Department of Political Sciences Statistics. Undergraduate course in English. Instructor: Prof. Roberto Rocci (8 CFU)
Fa 2016 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Statistics. Course in English at Master in Development Economics and International Cooperation. Course instructor: Prof. Roberto Rocci
Sp 2016 LUISS – Department of Economics and Finance Statistics. Undergraduate course in English. Instructor: Prof. Yosef Rinott (8 CFU)
Sp 2016 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Lab activities for Unsupervised Learning. Graduate course in English for MSc in Big Data in Business. Instructor: Prof. Roberto Rocci
Fa 2013 Università degli Studi di Roma Tor Vergata– Department of Economics and Finance Statistics. Undergraduate course in Italian. Prof. Stefano Antonio Gattone (9 CFU)

Part V - Society memberberships, Awards and Honors

Year Title
2017 Grant of 500 Euro for accommodation and traveling to attend 2017 CRoNoS Winter Course on Copula-based modeling with R at Birkbeck University of London. (Course Instructors: Prof. Marius Hofert, University of Waterloo, Canada, and Prof. Ivan Kojadinovic, University of Pau, France.)
2016 Best Ph.D. Thesis in Statistics in Italy, awarded by Italian Statistical Societ
2015 Honorable Mention at 2015 Shiny Contest organized by Department of Statistics - Penn State - for Snapshot of Statistical Inference: Point Estimation, Interval Estimation, Hypothesis Testing (in collaboration with Debmalya Nandy).
2011 Departmental Bursary for an MSc in Statistics at the University of Warwick
2010 Part-time collaboration at Faculty of Economics, Univerisity of Tor Vergata, Rome Sep 2009-Mar
2010
2011 PhD Scholarship, Department of Statistics, Sapienza University
Society Memberships: Member of SIS (Italian Statistical Society),Member of ASA (American Statistical Society), Member of MM (Mixture models), a specialized team of CMStatistics (Computational and Methodological Statistics), Member of the Classification Society.

Part VI - Funding Information [grants as PI-principal investigator or I-investigator]

Year Title Program Grant value
2025 Latent variable models and dimensionality reduction methods for complex data Partecipation to PRIN2022 Project No. 20224CRB9E, CUP B53C24006310006 (PI Paolo Giordani)
2021 Clustering rows and columns in a categorical and mixed-type data matrix Partecipation to Research Project, Sapienza University.
Principal investigator:
Martella, F.
2020 New statistical learning
methods for the model-based unsupervised classification of complex data Partecipation to Research Project, Sapienza University.
Principal investigator:
Rocci, R.
2019 Clustering methods for
complex, high-dimensional, data Partecipation to Research Project, Sapienza University.
Principal investigator: Alfò, M.
2018 Environmental processes and human activities: capturing their interactions via statistical methods (EphaStat) Partecipation to Research Project (local PI: Jona Lasinio, G)
2014 Dynamic Latent Mixture Model for longitudinal ordinal data Co-Principal Investigator (with Marino, M.F.), Avvio alla ricerca grant (Young Investigator grant), Sapienza University. 2000
2014 Biclustering using a fuzzy approach Partecipation to Research Project (PI Ferraro, MB)
2013 I disegni a due stadi nelle
prove cliniche: nuove prospettive metodologiche Partecipation to Research Project, Sapienza University.
Principal investigator: Gubbiotti, S.

Part VII – Research Activities

Keywords Brief Description
Mixture Models - Finite mixture models (and some extensions) used mainly for a clustering purpose
- Methodological aspects mainly focused on finite mixture models (the unboundedness of the likelihood for mixture of Gaussians, identifiability issues of mixture models based on an URV approach along with composite likelihood function
- Estimation methods based on composite likelihood and Monte Carlo likelihood
- Different real data applications (ChIP-seq data, marine currents, wildfires, social and behavioral data, time series, three-way data)
Composite Likelihood
Latent Variable Models
EM algorithm
Ordinal data

Part VIII – Summary of Scientific Achievements

Total Citations 185
Hirsch (H) index 8

Part IX– Publications

1) Ranalli, M., Rocci, R. (2025). “Composite Selection Criteria for the Number of Components of a Finite Mixture Model”. In: Giordano, G., La Rocca, M., Niglio, M., Restaino, M., Vichi, M. (eds) Statistical Models and Learning Methods for Complex Data. CLADAG 2023. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-031-84702-8_18 ISBN 978-3-031-84701-1
2) Ranalli M., Rocci R., Maruotti A. (2025). “Composite Likelihood Inference for Simultaneous Clustering and Dimensionality Reduction in the Chinese Longitudinal Healthy Longevity Survey”. In IES 2025 - Statistical Methods for Evaluation and Quality ISBN 978 88 5495 849 4.
3) Diterlizzi A., Tropea A., De Luca E., Guerriero C., Merola A., Notaristefano G., Moricone A., Policriti M.A., Ranalli M., Samasiuk A., Ghi T., Lanzone A., Apa R. (2025). “Use of progestin-only drospirenone-based pills in hyperandrogenic women with polycystic ovary syndrome”. Archives of Gynecology and Obstetrics, 1-7.
4) Ranalli M., Rocci, R. (2025,). “Clustering Three-Way Ordinal Data on Reduced Spaces”. In Scientific Meeting of the Italian Statistical Society (pp. 503-508). Cham: Springer Nature Switzerland.
5) Rocci, R,, Vichi M., and Ranalli M.. (2025). "Mixture models for simultaneous classification and reduction of three-way data." Computational Statistics, 40(1), 469-507.
6) Ranalli, M., Rocci, R. (2024). “Composite likelihood methods for parsimonious model-based clustering of mixed-type data”. Advances in Data Analysis and Classification, 18(2), 381-407.
7) Notaristefano, G., Ponziani, F. R., Ranalli, M., Diterlizzi, A., Policriti, M. A., Stella, L., ... & Apa, R. (2024). “Functional hypothalamic amenorrhea: gut microbiota composition and the effects of exogenous estrogen administration”. American Journal of Physiology-Endocrinology and Metabolism, 326(2), E166-E177.
8) Ranalli, M., & Rocci, R. (2024, August). “Clustering Ordinal Data Via Parsimonious Models”. In International Conference on Soft Methods in Probability and Statistics (pp. 380-387). Cham: Springer Nature Switzerland.
9) Diterlizzi A., Tropea A., Angelini E., Cestrone V., Fasciani R., Merola A., Notaristefano G., Policriti M.A., Polimeno T., Ranalli M., Savastano M.C., Tannous G., Versace V., Rizzo S., Scambia G., Lanzone A., Apa R.. (2024) "Chorio-retinal vessel density in women affected by functional hypothalamic amenorrhea: a monocentric observational cross-sectional study to evaluate the impact of hypoestrogenism on chorio-retinal vascularization." Archives of Gynecology and Obstetrics 310.4: 2247-2252.
10) Ranalli, R., Rocci, R. (2023), “Model-based simultaneous classification and reduction for three-way ordinal data”. CLADAG 2023 BOOK OF ABSTRACTS AND SHORT PAPERS : 14th Scientific Meeting of the Classification and Data Analysis Group Salerno, pp. 264-267. Editors: Carla Rampichini, C., La Rocca, M., Coreto, P., Giordano, G., Parrella, ML. ISBN: 9788891935632
11) Notaristefano G., Merola A., Scarinci E., Ubaldi N., Ranalli M. et al. “Circulating irisin levels in functional hypothalamic amenorrhea: a new bone damage index? A pilot study”. Endocrine 77, 168–176 (2022). https://doi.org/10.1007/s12020-022-03050-7.
12) Ranalli M., Rocci, R. (2021) “Mixture of factor analyzers for mixed-type data via a composite likelihood approach” Models and Learning for Clustering and Classification pp. 51-56. ISBN: 9788855265393.
13) Polistena A, Ranalli M, Avenia S, Lucchini R, Sanguinetti A, Galasse S, Rondelli F, Vannucci J, Patrone R, Velotti N, Conzo G, Avenia N. (2021) “The Role of IONM in Reducing the Occurrence of Shoulder Syndrome Following Lateral Neck Dissection for Thyroid Cancer”. J Clin Med. 10(18):4246. doi: 10.3390/jcm10184246. PMID: 34575355; PMCID: PMC8469441.
14) Alberto Bucci, Lorenzo Carbonari, Monia Ranalli & Giovanni Trovato (2021) “Health and economic development: evidence from non-OECD countries”, Applied Economics, 53:55, 6348-6375, DOI: 10.1080/00036846.2021.1939856
15) Ranalli, R., Rocci, R. (2021), “Semi-constrained model-based clustering of mixed-type data using a composite likelihood approach”. CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS : 13th Scientific Meeting of the Classification and Data Analysis Group Firenze, pp. 408-411. Editors: Porzio, G. C.; Rampichini, C.; Bocci, C. ISBN: 978-88-5518-340-6.
16) Ranalli, R., Rocci, R. (2021), “A comparison between methods to cluster mixed-type data: Gaussian mixtures versus Gower distance”, Statistical Learning and Modeling in Data Analysis - Methods and Applications. Editors: Balzano, S., Porzio, G.C., Salvatore, R., Vistocco, D., Vichi, M. (in printing).
17) Ranalli, M., Martella, F. (2020). “Model-based approach to biclustering ordinal data”. Book of short papers SIS 2020, pp. 1177-1182. Pearson. ISBN 9788891910776
18) Farcomeni, A., Ranalli, M. and Viviani, S. (2020). “Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models”. TEST, 1-19.
19) Lagona, F., Ranalli, M. and Barbi, E. (2020). “A model with space-varying regression coefficients for clustering multivariate spatial count data”. Biometrical Journal.
20) Ranalli, M., Maruotti, A. (2020) “Model-based clustering for noisy longitudinal circular data, with application to animal movement.” Envirometrics 31:e2572. https://doi.org/10.1002/env.2572.
21) Ranalli, M., Lindsay, B.G., and Hunter, D. (2020). “A classical invariant solution to the normal mixture problem.” Statistica Sinica, 30(3), pp. 1235-1254. https://doi.org/10.5705/ss.202016.0483.
22) Maruotti, A., Ranalli, M., Rocci, R. (2019). “Composite likelihood inference for simultaneous clustering and dimensionality reduction of mixed-type longitudinal data” in Cladag 2019, 11th Meeting of the Classification and Data Analysis Group. Book of Short Papers. (ISBN:978-88-8317-108-6).
23) Rocci, R., Ranalli, M. (2019). “An INDSCAL based mixture model to cluster mixed-type of data” in Cladag 2019, 11th Meeting of the Classification and Data Analysis Group. Book of Short Papers. (ISBN:978-88-8317-108-6).
24) Ranalli, M. (2019). “New perspectives on likelihood-based inference for latent and observed Gaussian mixture models”. Best Ph.D. Theses in Statistics and Applications. SIS-CLEUP. ISBN: 9788854950634.
25) Ranalli, M., Rocci, R. (2019) “An overview on the URV model-based approach to cluster mixed-type data.” Statistical Learning of Complex Data, pp. 45-53. Editors: F. Greselin, L. Deldossi, L. Bagnato and M. Vichi. ISBN 978-3-030-21140-0.
26) Ranalli, M., Rocci, R. (2018) “Simultaneous clustering and dimensional reduction of mixed-type data.” ASMOD 2018: Proceedings of the International Conference on Advances in Statistical Modelling of Ordinal Data, ISBN 978-88-6887-042-3. doi/10.6093/978-88-6887-042-3. Editors Stefania Capecchi, Francesca Di Iorio, Rosaria Simone.
27) Lagona, F., Ranalli, M. (2018) “A multilevel hidden Markov model for space-time cylindrical data.” Book of short Papers SIS 2018, ISBN-9788891910233.
28) Ameijeiras-Alonso, J., Lagona, F., Ranalli, M., Crujeiras, R.M. (2018) “A circular non-homogeneous hidden Markov field for the spatial segmentation of wildfire occurrences” Environmetrics. 2018;e2501. https://doi.org/10.1002/env.2501.
29) Rocci, R., Vichi, M., Ranalli, M. (2017). “Mixture models for simultaneous classification and reduction of three-way data” in Cladag 2017, 10th Meeting of the Classification and Data Analysis Group. Book of Short Papers. ISBN 978-88-99459-71-0.
30) Ranalli, M., Lagona, F., Picone, M., and Zambianchi, E. (2017). “Segmentation of sea current fields by cylindrical hidden Markov models: a composite likelihood approach.” Journal of the Royal Statistical Society: Series C, vol. 67, p. 575-598, ISSN: 0035-9254, doi: 10.1111/rssc.12240
31) Ranalli, M., Rocci, R. (2017). “A model based approach to simultaneous clustering and dimensional reduction of ordinal data.” Psychometrika, vol. 82, p. 1007-1034, ISSN: 0033-3123, doi: 10.1007/s11336-017-9578-5
32) Ranalli, M., Rocci, R. (2017). “Mixture models for mixed-type data through a composite likelihood approach.” Computational Statistics & Data Analysis, 110, 87–102.
33) Ranalli, M. (2016). “Recent developments in approximated-likelihood inference methods for latent variable models”. Available at SSRN: https://ssrn.com/abstract=2980839. (ISSN: 1556-5068)
34) Ranalli, M., Rocci, R.(2016). “ Standard and novel model selection criteria in the pairwise likelihood estimation of a mixture model for ordinal data” in Studies in Classification, Data Analysis, and Knowledge Organization. Analysis of Large and Complex Data, 53–68. Editors: Wilhelm, A.F.X. and Kestler, H. A. ISBN 978-3-319-25224-7.
35) Ranalli, M., Rocci, R.(2016). “Mixture Models for Ordinal Data: A Pairwise Likelihood Approach”, Statistics and Computing, 26(1), 529–547.
36) Ranalli, M., Rocci, R. (2015). “A pairwise likelihood approach to simultaneous clustering and dimensional reduction of ordinal data”, arXiv preprint:1504.02913 (Ranked third in the paper competition, held during the IFCS 2015 Conference).
37) Ranalli, M., Rocci, R. (2015). “Clustering methods for ordinal data: a comparison between standard and new approaches”, p. 221-229 in Studies in Classification, Data Analysis, and Knowledge Organization . Advances in Statistical Models for Data Analysis. Editors: Morlini, I., Minerva, T. and Vichi, M. DOI 10.1007/978-3-319-17377-1
38) Farcomeni A., Marino M.F., Ranalli, M. (2014). Discussion on “Analysis of Forensic DNA Mixtures with Artefact” by Cowell, Graversen, Lauritzen and Mortera, Journal of the Royal Statistical Society (Series C), 64, 37.
39) Ranalli, M., Rocci, R. (2013). “Mixture models for ordinal data: a pairwise likelihood approach” in Cladag 2013, 9th Meeting of the Classification and Data Analysis Group. Book of Abstracts. ISBN: 9788867871179, p. 396-399.

Manuscripts under review
1) Ranalli, M., Lyu, Y., Li, Q. A statistical framework for measuring reproducibility of heterogenous ChIP-seq data from multiple labs. Manuscript under review.

Abstracts
1) Martella F., and Ranalli M. (2024). "Biclustering listeners and music genres using a composite likelihood-based approach." Book of abstracts of the 18th International Joint Conference CFE-CMStatistics. 2024. ISBN: 978-9925-7812-8-7
2) Ranalli M., and Martella F. (2024). "Biclustering of ordinal data through a composite likelihood approach." Book of abstracts of the 26th International Conference on Computational Statistics. 2024, ISBN: 9789073592521.
3) Ranalli, M., Rocci, R. (2023). ” Parsimonious and semi-constrained models for clustering mixed-type data through a composite likelihood approach”. 6th International Conference on Econometrics and Statistics (EcoSta 2023), ISBN: 978-9925-7812-2-5.
4) Ranalli, M., Martella, F. (2020). ”Biclustering ordinal data through a model-based approach” 13th International Conference of the ERCIM, ISBN: 978-9963-2227-9-7
5) Maruotti, A., Ranalli, M. (2019). ”Autoregressive random effects models for circular longitudinal data using the embedding approach”. Book of Abstracts of the GRASPA 2019 Conference
6) Ranalli, M., Rocci, R. (2018). ”Simultaneous clustering and dimensional reduction of mixed-type data”. 11th International Conference of the ERCIM, ISBN: 978-9963-2227-5-9.
7) Ameijeiras-Alonso, J., Lagona, F., Ranalli, M., Crujeiras, R.M. (2018) Hidden Markov random fields for the spatial segmentation of circular data”. 11th International Conference of the ERCIM, ISBN: 978-9963-2227-5-9.
8) Ranalli M (2017). ”A Hidden Markov Approach To the Analysis of Cylindrical Space-time Series”. Editors: M. Cameletti and F. Finazzi, Book of Abstracts of the TIES-GRASPA 2017 Conference, Bergamo, 24-26 July, 2017. Special issue of GRASPA Working Papers. Bergamo.ISSN 2037-7738
9) Ranalli, M., Rocci, R. (2014). ” Mixture models for mixed-type data through a pairwise likelihood approach”. 7th International Conference of the ERCIM, ISBN: 9788493782245.
10) Ranalli, M., Lindsay, B.G. (2014). ”Simulated likelihood for heteroscedastic Gaussian mixture models” (Abstract accepted at JSM conference in Boston, 2-7 August 2014, available at http://www.amstat.org/meetings/jsm/2014/onlineprogram/AbstractDetails.cf...).

To submit
1) A hidden Markov approach to the analysis of cylindrical space-time series (with Lagona, F.)

Research in the pipeline
1) Using Godambe Information in Monte Carlo, integrated, and marginal likelihoods (with Lindsay, B.G. and Hunter D.).
2) Composite likelihood inference for simultaneous clustering and dimensionality reduction of mixed-type longitudinal data (with Maruotti, A. and Rocci, R.)
3) Mixture models for simultaneous classification and reduction (with Rocci, R. and Vichi, M.)

Other publications
1) Translation of the book entitled ”Statistics for the Health Sciences: A Non-Mathematical Introduction” (by Christine P Dancey, John Reidy, and Richard Rowe. SAGE.) from English to Italian (with Marino, M.F. and Alunni Fegatelli, D.) - Publisher: Piccin Nuova Libraria S.p.A. (2016)

Part X– Other Details
Educational Activities
Bachelor degrees supervised
1. Akhmadjon Yuldoshev (Bachelor Degree in Applied computer science and artificial intelligence , Sapienza University). May 2025
2. Tibo Louis Nishonov (Bachelor Degree in Applied computer science and artificial intelligence , Sapienza University). Mar 2025
3. Bakhrom Nishonov (Bachelor Degree in Applied computer science and artificial intelligence , Sapienza University). Mar 2025
4. Alessio Ziantoni (Bachelor Degree in Statistics, Sapienza University). Dec 2024
5. Federico Fratoni (Bachelor Degree in Statistics, Sapienza University). Co-supervised with M. Vichi March 2021
MSc degrees supervised
6. Gianpiero D’Ippolito (Master Degree in Statistics, Sapienza University) Oct2024
7. Matteo Vona (Master Degree in Statistics, Sapienza University, Rome) Jan 2024
8. Dario Scalzo (MSc in Big Data, Tor Vergata University) Jan 2017
9. Matteo Tamburri (MSc in Big Data, Tor Vergata University) Jan 2017

Editorial and Refereeing activities
Jan 2023 External Referee of a PhD thesis (Padova University)
since Nov 2020 Associate Editor of Statistics and Probability Letters
since Dec 2014 Extensive reviewing activity for many international journals in Statistics, among which: Annals of Applied Statistics, Metron, ADAC, CSDA, Statistics and Computing, Journal of Statistical Computation and Simulation, Statistical Methods & Applications, Social Networks, Statistical Papers, Studies in Classification, Data Analysis,and Knowledge Organization (Springer Book), Statistics and Probability Letters

Other Activities

1 Open Badge QuID https://openbadges.bestr.it/public/assertions/tz9HJMpfTsi_eiHV7YnDYw
Jul 2022
2 How to choose and design an exam. Come scegliere e progettare una prova d’esame (6
hours) promoted by the Quality and Innovation Work Group (QuiD) Dec 2020
3 Some techniques for active learning. Pratiche didattiche per l’apprendimento attivo promoted by the Quality and Innovation Work Group (QuiD) (6 hours) Nov 2020
4 How to use IT platforms for effective teaching. Come utilizzare le Piattaforme Informatiche per una didattica efficace (4 hours) promoted by the Quality and Innovation Work Group (QuiD) Nov 2020
5 Programme for Sapienza Academic Staff promoted by the Quality and Innovation Work Group (QuiD) 18 Sept 2020, 21 Sept 2020

Invited Research Stays
Ghent University (Belgium). Host: Dr. Christopher Ley Sep 2017

Invited Research Stays as Host
Matthew Reimherr - Research Scientist at Amazon and an Affiliate Professor in the Department of Statistics at Penn State University (Invited seminar Nov 2024)
Adelaide Freitas - Department of Mathematics. Univerisity of Aveiro. (11-12 Nov 2024)

Scientific Committees

Member of Scientific committee of MBC2 2026 conference since Mar 2025
Member of Scientific committee of ERCIM 2024 conference since Jan 2024
Member of Scientific committee of ERCIM 2021 conference since Jan 2021
Member of Scientific committee of ClaDAG 2021 conference since Oct 2020

Chair Sessions & Organized Sessions

Session organized at ERCIM 2024 Dec 2024
Session organized at SIS 2024 Jun 2024
Session organized at ERCIM 2023 Recent developments in clustering for complex data structure Dec 2023
Session organized at ERCIM 2021 Dec 2021
Session Modern likelihood methods for model based-clustering organized at ClaDAG 2021 Sept 2021
Chair session Recent developments in model-based clustering. ERCIM 2020 (online) Dec 2020
Session Recent developments in model-based clustering organized at ERCIM 2020 (online) Dec 2020
Chair session Discussion Poster Session. MBC2 (online) Sep 2020
Chair session Mixture and Latent Class Models for Clustering. CLADAG 2017 (Milan) Sep 2017
Session Flexible modeling organized at COMPSTAT 2018 (Romania) Aug 2018

IT Skills
Microsoft Office, LaTex, R, R Markdown, R Sweave, Stata, SAS, Matlab, Minitab, Bedtools, MEME-chip, Galaxy, FIMO.

CONFERENCES & SEMINARS
• Invited Talks
Talk at IES 2025 University of Padova, Italy Jun 2025
Talk at COMPSTAT 2024 University of Giessen, Germany Aug 2024
Talk at ClaDAG 2023 University of Salerno, Italy Sept 2023
EcoSta 2023 Conference Waseda University, Tokyo, Japan Aug 2023
Invited Talk at ERCIM 2020 Online Conference Dec 2020
Invited Talk at ClaDAG 2019 University of Cassino, Italy Sept 2019
Invited Talk at ERCIM2018 University of Pisa, Italy Dec 2018
Invited Talk at ASMOD2018 University of Naples, Italy Oct 2018
Invited Talk at SIS University of Palermo, Italy Jun 2018
Invited Talk at ClaDAG University of Milano-Bicocca, Italy Sep 2017
Invited Talk at Classification Society Meeting. University of Missouri, USA Jun 2016
Invited Talk at ERCIM 2014. Session: Model-based clustering for categorical and mixed-type data University of Pisa, Italy Dec 2014
Invited talk at ECDA Conference Jacobs University (Bremen), Germany Jul 2014

• Contributed Presentations
Talk at Model-Based Clustering and Classification (MBC2) Online Conference Sept 2020
Talk at METMA IX, 9th Workshop on Spatio-temporal modeling, Montpellier, France Jun2018
Talk at TIES-GRASPA 2017 University of Bergamo, Italy Jul2017
Talk at Model-Based Clustering and Classification (MBC2) Catania, Italy Sept2016
Talk at COMPSTAT 2016 Oviedo, Spain Aug2016
Talk at International Federation of Classification Societies (IFCS) Bologna, Italy Jul 2015
Talk at Model-Based Clustering and Classification (MBC2) Catania, Italy Sept2014
Talk at JSM Boston, USA Aug2014
Talk at the 9th Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (ClaDAG), University of Modena and Reggio Emilia, Italy Sep 2013

• Seminars & Workshops
Talk at Workshop of Department of Statistics (III edition) - Methods for human health and life sciences Department of Statistics, University Sapienza (jointly work with R. Rocci and A. Maruotti) Feb 2025
Invited seminar at DSS Lunch Seminar Department of Statistics, University Sapienza (jointly work with L. Fattobene and U. Pomante) Oct 2024
Invited seminar at Department of Statistics University of Bologna (Italy) (jointly work with R. Rocci and A. Maruotti) Apr 2023
Invited seminar at Department of Statistics University of Dublin (Dublin) (jointly work with R. Rocci and A. Maruotti) Jan 2020
Invited seminar at Department of Statistics University of Florence (Italy) (jointly work with R. Rocci and A. Maruotti) Nov 2019
Invited seminar at PhD Lunch Seminar Department of Economics and Finance,
Tor Vergata University (Italy) (jointly work with R. Rocci) Nov 2018
Invited seminar at Center for Statistics - Adolphe Quetelet Seminar Series Ghent University (Belgium) Sep2017
Invited seminar at Young Researchers Seminars Memotef, Sapienza University Jan2017
Talk at Group Meeting (jointly work with Li Q.) Penn State University (USA) May2016
Talk at Genomics Seminars, (jointly work with Li Q.) Penn State University (USA) Oct2015
Poster presentation at 2015 Bioinformatics and Genomics Retreat Penn State University (USA) Aug2015
Poster presentation at 2015 Rao Conference Department of Statistics, Penn State University (USA) May2015
Talk at SMAC (Stochastic Modeling and Computing) seminar Penn State University (USA), Department of Statistics
Apr2014

• Attended Conferences
ADISTA 2017 Roma Tre University Jun 2017
SIS 2013-Advances in Latent Variables Brescia Jun 2013
SIS 2012 Rome Jun 2012
48th Gregynog Statistical Conference Wales, UK Apr 2012