Presentations

Academic seminars / tutorial / invited talks

• Artificial Intelligence and the Uncertainty challenge in Fundamental Physics, Advancing Explainable AI: Testing and Enhancing Techniques Across Multidisciplinary Use-Cases.
• 2023 Summer School of Information Engineering (SSIE), Designing modular and efficient neural networks with conditional computation. Repeated at the Master in AI from USI. An abridged version is also available from the MLDM Workshop of AI*IA 2023.
• eBISS 2023, Designing and explaining graph neural networks (preliminary version from an internal seminar at Baker Hughes).
• Universitat Autònoma de Barcelona (2023), Overview on our research, hosted at the LAMB group of the Computer Vision Center.
• LUISS (2023), Transformers as universal neural layers, guest lecture for the cours on special topics in machine learning.
• INFN Roma (2023), An Introduction to Modern Deep Learning, inside the seminar cycle on “ML in particle physics: the state of the art”.
• KU Leuven (2022), Exploring task and data embeddings for continual learning.
• 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science: Alice’s adventures in a differentiable wonderland.
• 2022 Summer School of Information Engineering (SSIE): Graph neural networks: foundations, Graph Neural networks: explainability.
• XXXVI Riunione Annuale dei Ricercatori di Elettrotecnica (ET2022): Reti neurali e grafi: sfide ed applicazioni.
• La ricerca umanistica e i big data - Esperienze a confronto (virtual meeting, Centro di ricerca DigiLab, Sapienza, 2021): In Codice Ratio: Beyond Supervised Data.
• INNS Big Data and Deep Learning Conference (Sestri Levante, 2019): Deep randomized neural networks (joint tutorial with Claudio Gallicchio).
• University of Siena (Siena, 2019): Designing non-parametric activation functions: recent advances.
• University of Exeter (Exeter, 2018): Compressing deep neural networks: Challenges and theoretical foundations.
• Roma Tre University (Rome, 2017): Learning over networks with non-convex cost functions.

Miscellaneous (mostly in Italian)

Most of these are from invited talks to Meetups or communities.

• Regolare la GenAI è possibile (Cloudera/S3K, 2024, Palazzo Patrizi), La formazione in ambito AI: una prospettiva dall’università.
• Machine learning nella pratica della ricerca scientifica (ciclo di seminari Nuovo SEFIR, 2023), Machine Learning per la Ricerca Scientifica.
• Cloud ed IA per la sicurezza e la sostenibilità (2022, Palazzo Merulana, Roma): Verso modelli sostenibili di Intelligenza Artificiale.
• Enel Third Global Data Meetup (2021, online): Graph and geometric deep learning.
• Democratize.AI Meetup (2021, online): A homeostat, a deep network, and a differentiable computer walk into a bar….
• GDG DevParty Together (2020, online): Fairness in machine learning and the What-If Tool.
• PValue Meetup Pavia (2020): An introduction to neural networks for graphs (includes a small codelab).
• AI Day Rome (2019): TensorFlow 2.0 and its ecosystem (includes a 1-hour codelab).
• Data Science Seed Meetup (Genoa, 2019): In Codice Ratio: Mining the Vatican Secret Archives.
• Luiss EnLabs AI Worklab 2.0 (2019): State-of-the-art in deep learning frameworks.
• Codemotion Milan (2018): Bring your neural networks to the browser with TF.js! An updated version to TF.js 1.0 is also available.
• Converge Conference (Milan, 2018): Il “lato oscuro” del deep learning. Rifatto alla DevFest 2018 del GDG Milano.
• Facebook Dev Circle Rome (Opening Meetup): Dalle reti neurali ai computer differenziabili.
• Codemotion Milan (2017): The dark side of deep learning.
• Rome Machine Learning & Data Science Meetup: Quando le reti neurali inventano [demo available on GitHub].
• Global AI Hackaton Rome: Technical workshop (in preparation to the challenges).
• Codemotion Rome (2017): From a Developer’s POV: is Machine Learning Reshaping the World? [slightly modified slides].
• Data Driven Innovation Open Summit (2017): Il deep learning ed una nuova generazione di AI.
• Google DevFest 2016 (& Linux Day): A practical introduction to machine learning with sklearn.
• Data Driven Innovation Open Summit (2016): Big Data e Deep Learning.
• L’Apprendimento Automatico per la Rappresentazione della Conoscenza (Il Deep Learning e la nuova generazione di reti neurali), presentata alla scuola di formazione e ricerca SEFIR 2015.
• Sinergie Uomo-Macchina nel Processo Creativo (presentata al Campus Bio-Medico, ed in precedenza alla scuola di formazione e ricerca SEFIR 2014).
• GDG Meets U (L’Aquila): Smart Applications for ever-smarter Phones.
• Google DevFest Central Italy 2013: Automobili Intelligenti, Toyota e Università presentano il Machine Learning in Automotive (disponibile solo il video della presentazione).
• Visioni a Confronto (Google c/o Mercedes): Machine Learning & Prediction.
• Google Cloud Dev Conference 2013: Google Prediction API (disponibili slide e video della conferenza).
• Codemotion 2013: How to Make Smarter Programs: a Gentle Introduction to Machine Learning (disponibile anche il video della presentazione).