A

• Valerio Di Eugenio. AI-enabled AOI: a Deep Learning-based innovative approach to improve the manufacturing processes. Rel. Barbara Caputo. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Nicolo' Cordaro. Adaptive Onboarding: From a one-fits-all to a data-driven user-tailored onboarding experience. Rel. Silvia Anna Chiusano. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
• Francesco Bianco Morghet. Application of Transformers to edge-computing in ultra-low power devices. Rel. Daniele Jahier Pagliari, Alessio Burrello. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Daniele Genta. AutoML Solutions for Generative Models. Rel. Daniele Apiletti. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
• Emanuele Dri. Automatising defect management tasks for app developers: a Machine Learning approach. Rel. Andrea Calimera. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
• Marco Di Nepi. A Data Driven Approach to Remaining Time Prediction of Process Instances. Rel. Silvia Anna Chiusano. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Sofia Perosin. An Extraction-Abstraction Hybrid Approach for Financial Document Summarization. Rel. Luca Cagliero, Moreno La Quatra, Jacopo Fior. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Riccardo Mereu. A Study on Deep Learning Approaches for Visual Geo-localization. Rel. Barbara Caputo, Carlo Masone. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Ruggiero Francavilla. An autoencoder-based clustering strategy for usage pattern detection on heavy duty’s vehicles’ CAN bus data. Rel. Francesco Vaccarino, Luca Cagliero, Silvia Buccafusco. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Filippo Barba. A generative adversarial network approach to single image super-resolution of open-source satellite imagery. Rel. Roberto Fontana. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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B

• Christian Paesante. Bridge Aware Clustering with Noise Detection. Rel. Paolo Garza, Luca Cagliero. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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C

• Marina D'Amato. Computational neuroscience between machine learning and topology. Rel. Francesco Vaccarino, Robert Leech, Marco Guerra. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Vittorio Zampinetti. Copy-number aware clonal-tree reconstruction using single-cell RNA sequence. Rel. Mauro Gasparini, Jens Lagergren, Alessandra Guglielmi. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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D

• Alessio Serra. Data augmentation for medical image analysis: a Systematic Literature Review. Rel. Fabrizio Lamberti, Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Martina Toma. Data characterization by means of novel spatio-temporal patterns. Rel. Paolo Garza, Luca Colomba. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Matteo Villosio. Data-driven Analysis of Interactions and Popularity Increase in Online Social Networks. Rel. Luca Vassio, Martino Trevisan, Francesco Vaccarino. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Roberto Franceschi. Deep Learning-Based Radar Detector for Complex Automotive Scenarios. Rel. Barbara Caputo, Dmytro Rachkov. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
• Gabriele Trivigno. Deep learning for Sequence-based Visual Geo-localization. Rel. Barbara Caputo, Carlo Masone, Nicola Gatti. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Alessio Siciliano. Deep learning models for anomaly detection in time series. Rel. Andrea Bottino. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Francesco Foschi. Design of a data management system for value bet detection and soccer performance analysis. Rel. Daniele Apiletti. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Vito Cianchini. Development of Cloud-based Big Data solutions to support Business Intelligence for telco data analysis. Rel. Alessandro Fiori. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
• Nicolo' Bertozzi. Diagnosis methods for predictive maintenance of rolling bearings in an Industry 4.0 scenario. Rel. Tania Cerquitelli, Ilaria Bosi, Ariel Pablo Cedola, Rosaria Rossini. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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E

• Gabriele Goletto. EVEgo: Egocentric Event-data for cross-domain analysis in first-person action recognition. Rel. Barbara Caputo, Mirco Planamente, Chiara Plizzari, Matteo Matteucci. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Emanuele Gusso. EVEgo: Egocentric Event-data for cross-domain analysis in first-person action recognition. Rel. Barbara Caputo, Mirco Planamente, Chiara Plizzari, Marcello Restelli. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Manuele Macchia. Explaining black-box models in deep active learning in the context of image classification. Rel. Tania Cerquitelli, Salvatore Greco. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Silvia Giammarinaro. Exploiting background knowledge for scene graph generation with Logic Tensor Networks. Rel. Fabrizio Lamberti, Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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F

• Tommaso Calo'. Financial Time Series Summarization. Rel. Luca Cagliero, Jacopo Fior. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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G

• Sofia Borgato. Graph Neural Network for the prediction of Antibiotic Resistance. Rel. Giovanni Squillero, Alberto Paolo Tonda, Pietro Barbiero, Giulio Ferrero. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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I

• Piero Ciffolillo. IDBN-MINING: DATA MINING IN BASI DI CONOSCENZA MEDIANTE VALUTAZIONE DI MODELLI GRAFICI PROBABILISTICI. Rel. Mauro Gasparini. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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L

• Alberto Maria Falletta. Land Cover and Crop Type Classification using Machine Learning Techniques on Satellite Multispectral Data. Rel. Fabrizio Lamberti, Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Lorenzo De Nisi. Large-scale video scene retrieval through Transformer Encoder. Rel. Andrea Calimera. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Fabio Frattin. Learning to Grasp: an end-to-end sampling approach for robotic grasping in 6-DoF pose. Rel. Tatiana Tommasi, Antonio Alliegro, Matteo Matteucci, Martin Rudorfer. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Lorenzo Atzeni. Long-Term temporal attention in Efficient Human Action Recognition Architectures. Rel. Andrea Bottino. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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M

• Mattia Cappelli. Multi-domain data fusion for colorectal cancer prognosis. Rel. Maurizio Rebaudengo, Marta Lovino, Francesco Ponzio, Elisa Ficarra. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Fabrizio Lande. Multimodal-source image generation with deep learning. Rel. Paolo Garza, Erfan Ghaderey, Ruben Cartuyvels Cartuyvels. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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O

• Erich Malan. OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORKS TRAINING FOR FEDERATED LEARNING ON EMBEDDED SYSTEMS. Rel. Andrea Calimera, Valentino Peluso. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Eros Fani'. On the Challenges of Class Imbalance in Federated Learning for Semantic Segmentation. Rel. Barbara Caputo, Debora Caldarola, Fabio Cermelli, Antonio Tavera. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
• Gilberto Manunza. On the Impact of Adversarial Training on Uncertainty Estimation and Uncertainty Targeted Attacks. Rel. Barbara Caputo, Martin Jaggi, Matteo Matteucci. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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P

• Marco Gullotto. Portfolio management and Deep learning: Reinforcement learning and Transformer applied to stock market data. Rel. Enrico Bibbona, Patrizia Semeraro. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Paolo Alberto. Privacy Preserving Data Mining: a distributed approach to data anonymization. Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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Q

• Davide Vitaletti. Quality analysis of the Italian open government data through a generalized algorithm. Rel. Antonio Vetro', Marco Torchiano. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Francesco Montagna. Quantum circuit design with reinforcement learning. Rel. Davide Girolami. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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S

• Eduard Ciprian Ilas. Self-Supervised Deep Learning via Colorization on 3D Point Clouds for Object Part Segmentation. Rel. Enrico Magli, Tatiana Tommasi. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Matteo Latino. Self-supervised contrastive learning with hard positive mining for online action detection. Rel. Francesco Vaccarino. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Federico D'Asaro. Sensitive attributes disproportion as a risk indicator of algorithmic unfairness. Rel. Antonio Vetro', Juan Carlos De Martin. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Bianca Iacomussi. Spatio-temporal algorithms for predicting the usage of bike-sharing systems. Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Davide Massimino. Study and development of design techniques for 3D integrated circuits. Rel. Luca Sterpone, Sarah Azimi. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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T

• Alessio Cappellato. Towards Context based Monocular Depth Estimation. Rel. Barbara Caputo, Nicola Gatti, Sabine Süsstrunk. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Gabriele Tiboni. Towards safe and efficient transfer of robot policies from simulation to real world. Rel. Barbara Caputo, Ville Kyrki. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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V

• Irene Benedetto. Video lectures summarization. Rel. Laura Farinetti, Lorenzo Canale, Luca Cagliero. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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• Adele De Hoffer. Virus variants evolution via Machine Learning and epidemic Renormalization Group. Rel. Elisa Ficarra, Francesco Sannino, Francesco Conventi. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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X

• Niccolo' Cavagnero. Xpective Dataset: Towards Robust Pose Estimation with Radar Sensing. Rel. Barbara Caputo, Dario Fontanel. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
  

  

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