theory-based machine learning strategies, graph neural networks, machine learning

• Invited Talk in the cycle Foundation AI, under the invitation of Prof. Pietro Liò, with the title "Enhancing Climate Prediction with Knowledge Infused Deep Learning Models" https://talks.cam.ac.uk/talk/index/222046 (2024)
• Information required for participating to the PhD Quality Awards (2024)
• Inizio di attività di ricerca su invito presso il Computer Lab dell'Università di Cambridge, sotto la guida del Prof. Pietro Liò. (2024)

• Collegio di Ingegneria Gestionale e della Produzione. Componente invitato
• Collegio di Ingegneria Informatica, del Cinema e Meccatronica. Componente invitato

Master of Science

• Challenge@PoliTo by Firms - Enertech. A.A. 2022/23, INGEGNERIA GESTIONALE (ENGINEERING AND MANAGEMENT). Collaboratore del corso
• Challenge@PoliTo by Students PoliTO - Living with natural risks. A.A. 2023/24, INGEGNERIA GESTIONALE (ENGINEERING AND MANAGEMENT). Collaboratore del corso
• Big data: architectures and data analytics. A.A. 2022/23, INGEGNERIA INFORMATICA (COMPUTER ENGINEERING). Collaboratore del corso
• Big data: architectures and data analytics. A.A. 2023/24, INGEGNERIA INFORMATICA (COMPUTER ENGINEERING). Collaboratore del corso
• Business intelligence per big data. A.A. 2024/25, INGEGNERIA GESTIONALE. Collaboratore del corso
• Distributed architectures for big data processing and analytics. A.A. 2024/25, DATA SCIENCE AND ENGINEERING. Collaboratore del corso
• Data management and visualization. A.A. 2021/22, DATA SCIENCE AND ENGINEERING. Collaboratore del corso
• Data management and visualization. A.A. 2022/23, DATA SCIENCE AND ENGINEERING. Collaboratore del corso
• Data management and visualization. A.A. 2023/24, DATA SCIENCE AND ENGINEERING. Collaboratore del corso
• Data management and visualization. A.A. 2024/25, DATA SCIENCE AND ENGINEERING. Collaboratore del corso

Bachelor of Science

• Introduction to databases. A.A. 2021/22, INGEGNERIA INFORMATICA (COMPUTER ENGINEERING). Collaboratore del corso

• DBDM - Database and Data Mining Group

Projects funded by competitive calls

• SentinelPV - Hybrid Forecasting of PV Generation through Sentinel Plants and Geospatial Modelling , (2025-2027) - Componente gruppo di Ricerca
  University research - Collaborative Projects
• SchedulAI - Supporto e ottimizzazione data driven della schedulazione AMIAT , (2025-2026) - Responsabile di progetto (VR, Delegato,...)
  Corporate-funded and donor-funded research

• Marco De Luca. Programme in Ingegneria Informatica E Dei Sistemi (cycle 41, 2025-in progress)
  Research subject: Graph neural networks, Reinforcement learning, Agentic AI, Knowledge-informed machine learning
  
  Data science, Computer vision and AI
• Aurora Gensale. Programme in Ingegneria Informatica E Dei Sistemi (cycle 40, 2024-in progress)
  Research subject: Knowledge-grounded data generation for reliable and scalable AI
  
  Data science, Computer vision and AI

• Monaco, Simone (2025)
  Theory-guided Data Science models. relatore: APILETTI, DANIELE; , 37. XXXVII Ciclo, P.: 125
  Doctoral Thesis
• Vazirov, Etibar; Monaco, Simone; Apiletti, Daniele (2025)
  Prediction of coffee consumption using Graph Neural Networks and Explainable AI. In: 19th IEEE International Conference on Application of Information and Communication Technologies (AICT), Al-Ain (UAE), 29-31 October 2025. ISBN: 979-8-3315-9342-1
  Contributo in Atti di Convegno (Proceeding)
• Monaco, Simone; Monaco, Luca; Apiletti, Daniele; Cremonini, Roberto; Barbero, Secondo (2025)
  Uncertainty-aware methods for enhancing rainfall prediction with deep-learning based post-processing segmentation. In: COMPUTERS & GEOSCIENCES, vol. 205. ISSN 0098-3004
  Contributo su Rivista
• Celia, Matteo; Monaco, Simone; Apiletti, Daniele (2025)
  A Comparative Study of Neural Ordinary Differential Equations and Neural Operators for Modeling Temporal Dynamics. In: NEURAL COMPUTING & APPLICATIONS, vol. 37, pp. 25319-25338. ISSN 1433-3058
  Contributo su Rivista
• Monaco, Simone; Apiletti, Daniele; Francica, Andrea; Cerquitelli, Tania (2025)
  Quantify production planning efficiency through predictive modeling in manufacturing systems. In: COMPUTERS & INDUSTRIAL ENGINEERING, vol. 201. ISSN 0360-8352
  Contributo su Rivista
• Fassio, Simone; Monaco, Simone; Apiletti, Daniele (2024)
  Deep Probability Segmentation: Are segmentation models probability estimators?. In: The 18th IEEE International Conference on Application of Information and Communication Technologies (AICT), Torino (ITA), 25-27 September 2024. ISBN: 979-8-3503-8753-7
  Contributo in Atti di Convegno (Proceeding)
• Monaco, Simone; Monaco, Luca; Apiletti, Daniele (2024)
  Uncertainty-aware segmentation for rainfall prediction post processing. In: 2024 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Workshops, Barcellona, August 25, 2024 - August 29, 2024
  Contributo in Atti di Convegno (Proceeding)
• Monaco, Simone; Bussola, Nicole; Buttò, Sara; Sona, Diego; Giobergia, Flavio; Jurman, ... (2024)
  AI models for automated segmentation of engineered polycystic kidney tubules. In: SCIENTIFIC REPORTS, vol. 14. ISSN 2045-2322
  Contributo su Rivista
• Koudounas, Alkis; Giobergia, Flavio; Benedetto, Irene; Monaco, Simone; Cagliero, Luca; ... (2023)
  baρtti at GeoLingIt: Beyond Boundaries, Enhancing Geolocation Prediction and Dialect Classification on Social Media in Italy. In: EVALITA 2023, Parma (ITA), September 7th - 8th, 2023. ISSN 1613-0073
  Contributo in Atti di Convegno (Proceeding)
• Monaco, Simone; Apiletti, Daniele (2023)
  Training physics-informed neural networks: One learning to rule them all?. In: RESULTS IN ENGINEERING, vol. 18. ISSN 2590-1230
  Contributo su Rivista
• Monaco, Simone; Barresi, Sebastiano; Apiletti, Daniele (2023)
  Lorentz-invariant augmentation for high-energy physics. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Turin (ITA), September 18-22 2023. ISSN 1865-0937
  Contributo in Atti di Convegno (Proceeding)
• Monaco, Simone; Apiletti, Daniele (2022)
  Experimental Comparison of Theory-Guided Deep Learning Algorithms. In: European Conference on Advances in Databases and Information Systems (ADBIS 2022), Torino, September 5-8 2022, pp. 256-265. ISSN 1865-0929. ISBN: 978-3-031-15742-4
  Contributo in Atti di Convegno (Proceeding)
• Monaco, Simone; Apiletti, Daniele; Malnati, Giovanni (2022)
  Theory-Guided Deep Learning Algorithms: An Experimental Evaluation. In: ELECTRONICS, vol. 11. ISSN 2079-9292
  Contributo su Rivista
• Colomba, Luca; Farasin, Alessandro; Monaco, Simone; Greco, Salvatore; Garza, Paolo; ... (2022)
  A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery. In: International Conference on Information and Knowledge Management (CIKM) 2022, Atlanta (Georgia, USA), 17/10/2022 - 21/10/2022, pp. 3893-3897. ISBN: 978-1-4503-9236-5
  Contributo in Atti di Convegno (Proceeding)
• Monaco, Simone; Bethaz, Paolo; Apiletti, Daniele; Baldini, Fabrizio Pio; Caso, Carlo; ... (2022)
  Exploring waste-collection fleet data: challenges in a real-world use case from multiple data providers. In: EDBT/ICDT Workshop, 6th International workshop on Data Analytics solutions for Real-LIfe APplications, Edinburgh, March 29 - April 1, 2022
  Contributo in Atti di Convegno (Proceeding)
• Monaco, Simone; Pasini, Andrea; Apiletti, Daniele; Colomba, Luca; Farasin, Alessandro; ... (2021)
  Double-Step deep learning framework to improve wildfire severity classification. In: Workshops of the 24th International Conference on Extending Database Technology/24th International Conference on Database Theory, EDBT-ICDT 2021, Nicosia (Cyprus), March 23-26, 2021. ISBN: 978-3-89318-084-4
  Contributo in Atti di Convegno (Proceeding)
• Monaco, Simone; Greco, Salvatore; Farasin, Alessandro; Colomba, Luca; Apiletti, Daniele; ... (2021)
  Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction. In: APPLIED SCIENCES, vol. 11. ISSN 2076-3417
  Contributo su Rivista
• Monaco, Simone; Bussola, Nicole; Butto, Sara; Sona, Diego; Apiletti, Daniele; Jurman, ... (2021)
  Cyst segmentation on kidney tubules by means of U-Net deep-learning models. In: IEEE International Conference on Big Data, 15-18 Dec. 2021, pp. 3923-3926. ISBN: 978-1-6654-3902-2
  Contributo in Atti di Convegno (Proceeding)
• Marguglio, Angelo; Veneziano, Giuseppe; Greco, Pietro; Jung, Sven; Siegburg, Robert; ... (2021)
  A hybrid cloud-to-edge predictive maintenance platform. In: Predictive Maintenance in Smart Factories / Marguglio A., Veneziano G., Greco P., Jung S., Siegburg R., Schmitt R. H., Monaco S., Apiletti D., Cerquitelli T., Nikolakis N., Macii E., S.L., Springer, pp. 19-37. ISBN: 978-981-16-2939-6
  Contributo in Volume