Prof. Paolo Missier
Newcastle University, UK
Visiting Professor AY 2022-2023
Teaching offer – The teaching offer is designed to fit within the context of Unimore’s Master’s Degrees in Computer Science / Informatics and in Maths (Data Science) and PhD programs in Computer Science, in Computer Engineering, and in Math. It will consist of a series of topical seminars and practical lab sessions, for a total of about 15 hours, and organised into two complementary parts:
Part I: Scalable data processing for data science: architectures and programming models
Part II: Data Engineering and Data Science for Healthcare and medicine applications: challenges and case studies
Teaching program – Part II
Data Engineering and Data Science for Healthcare and medicine applications: challenges and case studies
Part II presents elements of the important, complex, and rapidly evolving landscape of Health Data Science and Engineering. Examples of cutting-edge results where AI has been successfully applied to a variety of problems in healthcare will be presented, to illustrate how machine learning and AI are enabling a new generation of data-driven, personalised medicine. Selected papers will be offered to the students for independent reading and then discussion in class. Open challenges will be presented, including those of data curation, learning with “small data”, and explainability of AI models.
Class schedule
Thursday March 9 Room M2.3 9:00 a.m. – 12:00 a.m.
Thursday March 16 Room M2.3 9:00 a.m. – 12:00 a.m. / 14:00-17:00 p.m.
Prof. Paolo Missier is Professor of Scalable Data Analytics with the School of Computing at Newcastle University, where he leads the School of Computing’s post-graduate academic teaching on Data Engineering for AI (aka Big Data Analytics), and a Fellow (2018-2023) of the Alan Turing Institute, UK’s National Institute for Data Science and Artificial Intelligence. His current research interests focus on the challenges of Health Data Engineering and Data Science, as well as on the efficient generation of data provenance to make Data Science more explainable and trustworthy.
Host
Prof. Federica Mandreoli
Info: federica.mandreoli@unimore.it