Research Interest

My primary research field is unsupervised learning and data science, particularly in the field of complex systems dynamics. My goal is to build out a theory to provide new approaches to clustering and anomaly detection by applying topological methods (TDA) thus providing a new approach to Topology-based unsupervised learning.

My application domain mainly concerns the medical and biological field (in my PhD thesis I focused on the study of epilepsy and on the analysis of EEG traces). I especially analyze data represented as time series.

I am also interested in more purely educational aspects and I participated in the Erasmus+ Project, Da.Re, where a European curriculum was proposed for a master’s degree in Data Science.

Selected Publications

  1. Abdullahu, E., Wache, H., Piangerelli, M. (2025). Secure and Decentralized Hybrid Multi-Face Recognition for IoT Applications. Sensors, 25, 5880. https://doi.org/10.3390/s25185880
  2. Cruciata, L., Contino, S., Ciccarelli, M., Pirrone, R., Mostarda, L., Papetti, A., Piangerelli, M. (2025). Lightweight Vision Transformer for Frame-Level Ergonomic Posture Classification in Industrial Workflows. Sensors, 25, 4750. https://doi.org/10.3390/s25154750
  3. Corradini, F., Leonesi, M., Piangerelli, M. (2025). State of the Art and Future Directions of Small Language Models: A Systematic Review. Big Data and Cognitive Computing, 9, 189.https://doi.org/10.3390/bdcc9070189
  4. Pelosi, D., Cacciagrano, D., Piangerelli, M. (2025). Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review. Algorithms, 18, 443. https://doi.org/10.3390/a18070443
  5. Corradini, F., Mozzoni, L., Piangerelli, M., Re, B., Rossi, L. (2025). A Framework for Rapidly Prototyping Data Mining Pipelines. Big Data and Cognitive Computing, 9, 150. https://doi.org/10.3390/bdcc9060150
  6. Corradini, F., Nucci, V., Piangerelli, M., Re, B. (2025). Online Clustering with Interpretable Drift Adaptation to Mixed Features. Intelligent Systems with Applications,200510.https://doi.org/10.1016/j.iswa.2025.200510
  7. Assefa, R., Mamuye, A., Piangerelli, M. (2025). COVID-19 Severity Classification Using Hybrid Feature Extraction: Integrating Persistent Homology, Convolutional Neural Networks and Vision Transformers. Big Data and Cognitive Computing, 9, 83. https://doi.org/10.3390/bdcc9040083
  8. Ahmed, U., Alexopoulos, C., Piangerelli, M., Polini, A. (2024). BRYT: Automated Keyword Extraction for Open Datasets. Intelligent Systems with Applications, 23,200421. https://doi.org/10.1016/j.iswa.2024.200421
  9. Ciccarelli, M., Corradini, F., Germani, M., Menchi, G., Mostarda, L., Papetti, A., Piangerelli, M. (2022). SPECTRE: A Deep Learning Network for Posture Recognition in Manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01853-2
  10. De Simone, A., Piangerelli, M. (2020). A Bayesian Approach for Monitoring Epidemics in Presence of Undetected Cases. Chaos, Solitons and Fractals, 140, 110167.https://doi.org/10.1016/j.chaos.2020.110167

ResearchGate

A full list of publications is available at the above Google ReserchGate link.