Argomenti di tesi proposti dal docente.
Main Topics:
- Anomaly detection
- ML accountability, explainability
- Text analysis
- Software Quality
Please contact me.
Thesis in progress:
- Alberto Trashaj on LLM
- Manuel Rech on LLM
- Davide Gotti on AI ethical issues
- Roberto Cornali on Reinforcement Learning
- Riccardo Petrella on text classification
- Simone Mirabella on pollutants
- Jovan Kalyango, about text analysis for user support application
Thesis completed
- Sofia Camilla Todeschini on text analysis, a.a. 2023/2024,Transformer-based topic classification to assist biomedical literature review
- Maximilian Schlake, a.a. 2023/2024, Domain Adaptation in Machine Learning - application to IoT
- Magi Ceka, a.a. 2023/2024, From text to insights: Leveraging Topic Modeling to Explore Climate Change's Impact on Cultural Heritage Literature
- Aurela Sinanaj, a.a. 2022/2023, Multi-class text classification of tweets in the context of natural and human-made disasters using Natural Language Processing techniques
- Giovanni Zurlo, a.a. 2022/2023, AI-based Medical Imaging of COVID-19: A Visual and Textual Analysis of Scientific Literature
- Giovanni Battista Esposito, a.a. 2022/2023, Zero-shot learning for automated screening in systematic reviews
- Giorgia Castelli, a.a. 2022/2023, Forecasting Surgical Waiting Times: A Data Science and Machine Learning Approach for Enhanced Resource Management
- Sara Quinto, a.a. 2022/2023, Outlier Detection on Energy Consumption Data for Production Process Anomalies Classification
- Mirko Di Stefano, a.a. 2021/2022, Dynamic Cross-Correlation Analysis between COVID-19 and Air Pollutants Time Series: a Case Study on Lazio and Lombardy Regions during the First Pandemic Wave
- 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
Ultime tesi seguite dal docente
Tesi di Laurea Magistrale
- AI-Based Medical Imaging of COVID-19: A Visual and Textual Analysis of Scientific Literature
- Domain Adaptation in Machine Learning - application to IoT
- Forecasting Surgical Waiting Times: A Data
Science and Machine Learning Approach for
Enhanced Resource Management
- From Texts to Insights: Leveraging Topic Modeling to Explore Climate Change's Impact on Cultural Heritage Literature
- Multi-class text classification of tweets in the context of natural and human-made disasters using Natural Language Processing techniques
- Outlier Detection on Energy Consumption Data for Production Process Anomalies Classification
- Transformer-based Topic Classification to Assist Biomedical Literature Review