Prof. Paolo Missier – University of Birmingham, UK Part II of the PhD teaching “Scalable Data Science – Explanable AI”, described here, includes 6 hours and follows on from part I, as follows: Part I introduced methods for providing explanations to ML models (XAI). We focused specifically on Influence Functions. In this part we consider the problem of extending those explanations to the data processing that occurs when raw data, possibly from multiple sources, is engineered into a training set. We will introduce key concepts of data and process provenance, and explain how they apply to the problem, leading to “data explanations”, using a specific implementation we have developed over the years. We then connect the tool to the Influence Function framework, resulting in “End-to-End explanations” (XEE). We introduce well-known concepts of “algorithmic fairness” and provide definitions that apply specifically to classification models. We then introduce methods to ensure a model is “fair”, or at least “fairness-aware”, and finally we present our recent work on ensuring continued fairness in the presence of data drifts The programme is structured as follows. About 3 hours of seminar-style lectures, using key papers including from our own group About 3 hours of in-depth study, guided by relevant literature. Students are required to select recent work from a portfolio of recommended papers, and to present their insights to the class Class schedule (all classes take place in the Math building M018 of the FIM Department, via Campi 213/b – Modena) Mon 11-11 h.15:00-17:00 – Meeting Room 2° floor – MO18 Building Thu 13-11 h. 9:00-11:00 – Class Room M1.6 Thu 13-11 h. 11:30-13:30 – Class Room M2.4 Prof. Paolo Missier. is Professor of Data Management in the School of Computer Science, and the Director of the Institute for Data and AI at University of Birmingham. Bio, research interests, and link to research portal can be found here: https://www.birmingham.ac.uk/staff/profiles/computer-science/academic-staff/missier-paolo Host Prof. Federica Mandreoli
Publication date: May 30th, 2025. For more information visit the official page of the Doctoral Research Programmes of UNIMORE.
Prof. Paolo Missier – University of Birmingham, UK Part I of this seminar series runs over 10 hours and is focused on Explainable AI (XAI), covering the following topics: – The need for explanations in Machine Learning and AI – Classical global and local methods : LIME and Shapley (Shap values) – Influence functions: a robust statistical approach that has recently (circqa 2017) been revisited to associate a relative importance to training examples for explaining a specific model inference. One key advantage of using influence analysis over other methods, is its ability to bypass the “black box” barrier that is typical of complex nonlinear models (eg deep neural networks). The programme includes: – About 4 hours of seminar-style lectures, using key papers and a recent survey to dive into specific approaches to using influence functions – Guided study to relevant literature, where students are required to select recent work from a portfolio of recommended papers, and to present their insights to the class – Practical work using python libraries that implement influence-based methods to provide explanations, in combination with simple ML models. Class schedule (All classes will take place in the Math building of the FIM Department, via Campi 213/B Modena) Mon 12/5 h.12:00-14:00 – classroom M2.3 Tue 13/5 h.11:00-13:00 – 14:00-16:00 – sala Riunioni piano uffici Thu 15/5 h. 11:00-13:00 – 14:00-16:00 – sala Riunioni piano uffici The flyer for the event is available at this link.
March 7 – May 12, 2025 The series consists of seven meetings. The speakers will present their ongoing research on topics related to the analysis of language variation and change, and on the contribution that automatic data analysis is able to provide to the study of the processes and structures which define human language and explain its diversity.A unifying theme will be the interaction, in terms of methods and results, between historical and formal linguistics and the automatic tools for data analyses made available by computer sciences, with the aim to define novel research lines and envisage effective interdisciplinary collaboration.Each meeting will explore these topics from a different angle: the investigation of ancient texts of a composite nature (Homeric texts) through the implementation of automatic analysis techniques (C. Bozzone); the neurobiological coding of the linguistic capacity and its structures (A. Moro); ‘microscopic’ variation and language contact (A. De Angelis); the reconstruction of the human past through the investigation of syntactic change and the automatic analysis of comparative materials (G. Longobardi); the universal coding of syntactic properties with their synchronic and diachronic variation (P. Crisma). For more information see the attached flyer, or contact the organizer, Prof. Cristina Guardiano.
International Summer School on ADVANCES IN ARTIFICIAL INTELLIGENCE September 23-27, 2024 Villa del Grumello (Como, ITALY)
Summer School on Artificial Intelligence for a Secure Society 05 September 2024 – 10 September 2024 Capo Vaticano Resort Thalasso Spa Capo Vaticano, Calabria (Italy)
Dr. Giacomo Valente, PhD – Università degli Studi dell’Aquila 4, 5, 6 June 2024. Math building, Dept. of Physics, Informatics and Mathematics, via Campi 213/b.
Semantic Approaches for Entity Resolution (9:00 am – 1:00 pm); Towards Adaptive Context-aware Intelligent Environments (2:00 pm – 6:00 pm)
Details for four new PhD teachings are available on the TEACHING page.
