Marco Piangerelli

I am an Adjunct Professor (INF/01, Computer Science) at the University of Camerino and an AI Engineer in the R&D division of Vici & C. S.p.A., where I work on machine learning for optical metrology.

Born in Battipaglia (SA), Italy, in 1984, I hold a B.Sc. in Biomedical Engineering from the Politecnico di Milano and an M.Sc. in Bioengineering from the University of Bologna, where I worked on mathematical modelling of cardiac arrhythmias in collaboration with Cornell University under the supervision of Prof. Flavio Fenton. I earned a Ph.D. in Computer Science at the University of Camerino under the supervision of Prof. Emanuela Merelli, with a thesis on topological data analysis for the study of epileptic seizures, carried out in collaboration with ab medica s.r.l. In 2024 I was a visiting researcher at the Addis Ababa Institute of Technology, Addis Ababa University.

My research lies at the intersection of machine learning, topological data analysis and complex systems modelling, with particular attention to trustworthy AI, concept and data drift, and interpretable learning methods. I have contributed to European, regional and industrial R&D projects including TOPDRIM, Da.Re., MIRACLE, SIMPLE, AI.EM, UNICAM 4 INGKA and Unicam 4 VICI (see the projects page).

I am a member of the Processes and Services (PROS) Lab and of the Bioshape and Data Science Lab at the University of Camerino, and an external member of the Center for Philosophy Science and Policy at Università Politecnica delle Marche. I serve on the editorial boards of Intelligenza Artificiale (Sage), PeerJ Computer Science, Applied Computational Intelligence and Soft Computing (Wiley) and Frontiers in Smart Manufacturing, and I review for journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE TPAMI, Neurocomputing and Entropy. I co-organise the SynDAiTE and DELTA workshop series on synthetic data and drift (see events).

I teach Machine Learning at the Master’s and Ph.D. programmes in Computer Science at the University of Camerino (see teaching). My institutional page is on the Unicam faculty directory.

Keywords: machine learning · topological data analysis · complex systems · trustworthy AI · concept drift · interpretability · healthcare · manufacturing

news

Apr 11, 2024	L'intelligenza artificiale è veramente intelligente?
Feb 23, 2024	Intelligenza artificiale, di cosa stiamo parlando?
Jul 03, 2021	Second edition of the first-level Master Digital Solution Manager at the University of Camerino.
Jul 01, 2021	Enrolment at the University of Camerino for the academic year 2021/22 opens today.
Dec 01, 2020	Interview on Il Piccolo (Trieste): “Un Rt preciso? Il modello è targato SISSA” (PDF).
Sep 22, 2020	Interview on Il Sole 24 Ore: “Perché l’Italia è in vantaggio su Spagna e Francia. E come capire i contagi in autunno” (PDF).

latest posts

Mar 16, 2020	Covid-19: qualche dato
Oct 09, 2019	A European approach to Master's degrees in Data Science
Sep 20, 2019	Future Tech Week 2019

selected publications

TMLR

COunterfactual Reasoning for Temporal EXplanations: Plausible and Robust Explanations for EEG-Based Seizure Detection

M. Zannotti, B. Prenkaj, M. Piangerelli, F. Corradini, and G. Kasneci

Transactions on Machine Learning Research, 2026

Bib HTML

@article{zannotti2026cortex,
  title = {{COunterfactual Reasoning for Temporal EXplanations}: Plausible and Robust Explanations for {EEG}-Based Seizure Detection},
  author = {Zannotti, M. and Prenkaj, B. and Piangerelli, M. and Corradini, F. and Kasneci, G.},
  journal = {Transactions on Machine Learning Research},
  year = {2026},
}

MLWA

Condition monitoring for pattern recognition in manufacturing

Marco Piangerelli, Vincenzo Nucci, Flavio Corradini, Luca Giulioni, and Barbara Re

Machine Learning with Applications, 2025

DOI Bib

@article{piangerelli2025condition,
  title = {Condition monitoring for pattern recognition in manufacturing},
  author = {Piangerelli, Marco and Nucci, Vincenzo and Corradini, Flavio and Giulioni, Luca and Re, Barbara},
  journal = {Machine Learning with Applications},
  pages = {100787},
  year = {2025},
  doi = {10.1016/j.mlwa.2025.100787},
}

Neural Netw.

A systematic literature review of spatio-temporal graph neural network models for time series forecasting and classification

Flavio Corradini, Flavio Gerosa, Marco Gori, Carlo Lucheroni, Marco Piangerelli, and Martina Zannotti

Neural Networks, 2025

DOI Bib

@article{corradini2025stgnn,
  title = {A systematic literature review of spatio-temporal graph neural network models for time series forecasting and classification},
  author = {Corradini, Flavio and Gerosa, Flavio and Gori, Marco and Lucheroni, Carlo and Piangerelli, Marco and Zannotti, Martina},
  journal = {Neural Networks},
  pages = {108269},
  year = {2025},
  doi = {10.1016/j.neunet.2025.108269},
}

ISWA

Online clustering with interpretable drift adaptation to mixed features

Flavio Corradini, Vincenzo Nucci, Marco Piangerelli, and Barbara Re

Intelligent Systems with Applications, 2025

DOI Bib

@article{corradini2025online,
  title = {Online clustering with interpretable drift adaptation to mixed features},
  author = {Corradini, Flavio and Nucci, Vincenzo and Piangerelli, Marco and Re, Barbara},
  journal = {Intelligent Systems with Applications},
  pages = {200510},
  year = {2025},
  doi = {10.1016/j.iswa.2025.200510},
}

JIM

SPECTRE: a deep learning network for posture recognition in manufacturing

Marianna Ciccarelli, Flavio Corradini, Michele Germani, Giacomo Menchi, Leonardo Mostarda, Alessandra Papetti, and Marco Piangerelli

Journal of Intelligent Manufacturing, 2022

DOI Bib

@article{ciccarelli2022spectre,
  title = {{SPECTRE}: a deep learning network for posture recognition in manufacturing},
  author = {Ciccarelli, Marianna and Corradini, Flavio and Germani, Michele and Menchi, Giacomo and Mostarda, Leonardo and Papetti, Alessandra and Piangerelli, Marco},
  journal = {Journal of Intelligent Manufacturing},
  year = {2022},
  doi = {10.1007/s10845-022-02014-y},
}

CSF

A Bayesian approach for monitoring epidemics in presence of undetected cases

Andrea De Simone and Marco Piangerelli

Chaos, Solitons & Fractals, 2020

DOI Bib

@article{desimone2020bayesian,
  title = {A Bayesian approach for monitoring epidemics in presence of undetected cases},
  author = {De Simone, Andrea and Piangerelli, Marco},
  journal = {Chaos, Solitons \& Fractals},
  volume = {140},
  pages = {110167},
  year = {2020},
  doi = {10.1016/j.chaos.2020.110167},
}