*Big Data challenges for Mathematics: state of the art and future perspectives*
*Online webinar – February 25, 2022*
The workshop will be held on the platform Zoom and will be streamed on YouTube. Participants who would like to follow the workshop in real time and participate to the discussion with the speakers are requested to register.
See the web site of the workshop for registration, programme and additional information:
*http://itn-bigmath.unimi.it/* http://itn-bigmath.unimi.it/
*Aims of the workshop:*
Nowadays the data deluge is the hallmark of a new kind of ‘Law of Large Numbers’ that builds intelligence from large, heterogeneous, noisy and, in general, complex data sets collected from mobile devices, the Internet of Things, software logs, automated medical devices, social media, and so many other data sources. The classical mathematical and statistical paradigms are often not applicable to current real-world problems, so there is a growing demand of new mathematical and statistical techniques to face such problems, able to shed some light on the often black-box techniques which are usually applied in such context and which characterise Deep Learning and, more generally, Artificial Intelligence. On the other side, since computers can not do everything by themselves, there is a growing need for new professional and scientific figures, the data scientists, that master a whole range of skills, ranging from data processing to sophisticated math tools and computational skills that are needed to extract the knowledge.
The state of the art and future research perspectives in this framework will be highlighted and discussed in this webinar by the PhD students enrolled in the EU funded MSCA Project BIGMATH http://itn-bigmath.unimi.it/ (grant n. 812912), starting from a set of challenging industrial case studies.
Additionally some well known keynote speakers will introduce their point of view on possible future perspectives in different specific and quite hot subject areas related with the analysis of complex and big data.