The SmartData@PoliTO center focuses on Big Data technologies, Data Science and Machine Learning approaches. We blend interdisciplinary people and competences from different domains to provide cross-domain solutions to the widest spectrum of knowledge discovery challenges, by leveraging advanced expertise in data science, from data management, to data modeling, analytics, and engineering. We are a well-recognized center where experts in methodologies and domain experts from various disciplines work in a single space, facing both theoretical problems and helping companies toward applications.
Presenter: Andrea De MartinoWednesday, May 25th, 2022 17:00Location: SmartData@Covivio Intracellular metabolic activity (e.g. reaction fluxes and metabolite levels) is largely inaccessible to experiments. I will describe recent work aimed at constructing generative models of cellular metabolism through statistical inference from empirical data. After motivating why this problem is worth the effort and clarifying the technical […]
Presenter: Jost von HardenbergWednesday, May 11th, 2022 17:00Location: SmartData@Covivio Numerical global climate modelling with Earth-system models is currently one of the main tools available for studying processes and feedbacks at work in the climate system, attribution of climate change, projection of future climate change scenarios and the development and planning of mitigation and adaptation measures.These […]
Presenter: Luca ColombaMonday, April 27th, 2022 17:00Location: SmartData@Covivio Clustering is among the most popular unsupervised data mining tasks. Within the years, density-based clustering algorithms proved to be one of the most prominent strategies to identify clusters and noisy points. Such methodology encompasses a large variety of clustering algorithms that focus on the identification of core […]
Presenter: Marco CaniniTuesday, April 26th, 2022 14:00Location: Sala Ciminiera, 5th floor Corso Castelfidardo 34 ABSTRACT Scaling deep learning to a large cluster of workers is challenging due to high communication overheads that data-parallelism entails. This talk describes our efforts to rein in distributed deep learning’s communication bottlenecks. We describe SwitchML, the state-of-the-art in-network aggregation system […]
