Foto del docente

Elisabetta Ronchieri

Professoressa a contratto

Dipartimento di Scienze Statistiche "Paolo Fortunati"

Didattica

Argomenti di tesi proposti dal docente.

Main Topics:

  • Anomaly detection
  •  ML accountability, explainability
  • Social media (on pandemic events, and natural disaster)
  •  Software Quality

Please contact me.

Thesis in progress:

  • Mirko Di Stefano, about COVID 19

Thesis completed

  • Nicola Ronzoni, a.a. 2021/2022, Outlier detection in water pump sensors
  • Laura Viola, a.a. 2021/2022, Detecting anomalies in a data center using heterogeneous data: a domain-specific dictionary approach
  • Lorenzo Cesari, a.a. 2021/2022, Statistical Monitoring for Packaging Machines
  • Filippo Pacinelli, a.a. 2021/2022, Unsupervised Machine Learning Techniques for Anomaly Detection with Multi-Source Data
  • Leonardo Scarso, a.a. 2021/2022, Tweet Analysis for Detecting Attitudes Towards COVID-19 Vaccines: A Case Study on Pfizer/Biontech, Moderna, Astrazeneca/Vaxzevria, Johnson & Johnson
  • Clara Biagi, a.a. 2021/2022, Accountability in Machine Learning: Comparing Methods for Mitigating Gender Bias in Word Embedding
  • Zhikang Qin, a.a. 2021/2022, Tracking pandemic events through social media
  • Shunfang Wang, a.a. 2021/2022, Assessing environmental conditions in China during the COVID-19 epidemic period
  • Rossana Di Staso, a.a. 2020/2021, Assessing the relation between the spreading of COVID-19, and air quality and meteorological data in Emilia-Romagna, Italy
  • Gianluca Bertaccini, a.a. 2020/2021, Spectral and neural gas clustering techniques for
    software defect prediction: performance evaluation with different feature selection methods
  • Gaetano Aloisio, a.a. 2020/2021, Missing values imputation and supervised classifiers: an extensive analysis
  • Andrea Marzocchi, a.a. 2020/2021, Anomaly Detection using NLP Techniques and ML Models
  • Yue Yang, a.a. 2020/2021, Application of Natural Language Processing and Machine learning techniques on unstructured log data of open source software on GitHub

 

 

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