Past events
-
AI-Fun with ELLIS Seminar | Jack Liell-Cock: Compositional imprecise probability
11:00 - 12:00 13 November 2024
The Manchester Centre for AI Fundamentals and Manchester's ELLIS Unit are co-hosting a series of seminars featuring expert researchers working in the fundamentals of AI. Jack Liell-Cock is a PhD student at Keble College, Oxford, under the supervision of Jeremy Gibbons and Sam Staton. Title: Compositional...
Read more
-
Digital Health Equity seminar: An introduction to algorithmic fairness
13:00 - 14:00 13 November 2024
The next seminar in the Digital Health Inequities Seminar Series is on 13th of November 1-2pm via Zoom: https://zoom.us/j/96704384543. Matt Sperrin and Jose Benitez-Aurioles will provide “An introduction to algorithmic fairness”. Abstract Fairness can be defined as ' 'the absence of any prejudice...
Read more
-
AI-Fun with ELLIS Seminar | Chrysoula Zerva: Uncertainty in NLP & beyond: quantification, interpretation, evaluation
11:00 - 12:00 30 October 2024
The Manchester Centre for AI Fundamentals and Manchester's ELLIS Unit are co-hosting a series of seminars featuring expert researchers working in the fundamentals of AI. Title: Uncertainty in NLP & beyond: quantification, interpretation, evaluation Abstract: As language models grow in popularity,...
Read more
-
Digital Futures & Sustainable Futures PhD Workshop
13:00 - 15:30 30 October 2024
University of Manchester Staff and Student Only | In-person | Room tbc Join us to explore interdisciplinary research and synergies in the fields of digital technology and sustainability. We welcome PhD students from The University of Manchester who are conducting research in these areas and are interested...
Read more
-
AI-Fun with ELLIS Seminar | Luca Magri: Scientific machine learning for chaotic forecasting and real-time digital twinning
10:00 - 11:00 23 October 2024
The Manchester Centre for AI Fundamentals and Manchester's ELLIS Unit are co-hosting a series of seminars featuring expert researchers working in the fundamentals of AI. Scientific machine learning for chaotic forecasting and real-time digital twinning The ability of fluid mechanics modelling to...
Read more