School Seminar Series

School Seminar: Jon Rogers, “There’s plenty of room in our communities: Rethinking computational scale through open hardware”

You are warmly invited to the third School Seminar: Speaker: Jon Rogers Title: There’s plenty of room in our communities: Rethinking computational scale through open hardware Abstract: The dominant business model of Big Tech is built on scale. Scale to outpace competitors, capture global markets, and consolidate control. Today, just a handful of companies mediate School Seminar: Jon Rogers, “There’s plenty of room in our communities: Rethinking computational scale through open hardware”

School Seminar – Peter Macgregor “Fast Dynamic Algorithms for Modern Clustering”

You are warmly invited to the second School Seminar: Speaker: Peter Macgregor Title: Fast Dynamic Algorithms for Modern Clustering Abstract: Spectral clustering and DBSCAN both have long histories as theoretically grounded, general-purpose clustering algorithms. However, they face practical challenges when scaling to large datasets which have limited their adoption in practice. In recent work, we School Seminar – Peter Macgregor “Fast Dynamic Algorithms for Modern Clustering”

PGR Seminar with Gen Li + Jess McGowan

The next PGR seminar is taking place this Friday 2nd May at 2PM in JC 1.33a Below are the Titles and Abstracts for Gen and Jess’ talks – Please do come along if you are able. Gen Li Title: Visualization of clinical pathways based on sepsis comorbidities Abstract: Sepsis is a severe infectious syndrome that can PGR Seminar with Gen Li + Jess McGowan

SICSA DVF Seminar – Dr André G. Pereira

We had our first School seminar of the semester today. The speaker was André G. Pereira visiting Scotland on a SICSA DVF Fellowship. André is working on AI Planning problems, an area that is closely related to the work of our own Constraint Programming research group. Title: Understanding Neuro-Symbolic Planning Abstract: In this seminar, we SICSA DVF Seminar – Dr André G. Pereira

Seminar – Richard Connor – 5th November

The second school seminar on 5th November at 2pm, on Teams.  If you do not have the Teams link available please contact the organiser, Ian Gent. Dimensionality Reduction in non-Euclidean Spaces Richard Connor Deep Learning (ie Convolutional Neural Networks) gives astoundingly good classification over many domains, notably images. Less well known, but perhaps more exciting, Seminar – Richard Connor – 5th November

Georgios Gerasimou (University of St Andrews): Frontiers in computational revealed preference analysis

RESCHEDULED: please note the changed date and a non-standard time! Abstract: Prest is a recently published piece of open-source software for computational revealed preference analysis that provides novel ways to estimate decision makers’ preferences over choice alternatives by analysing their observable choice behaviour. This software is informed by classic as well as recent developments in Georgios Gerasimou (University of St Andrews): Frontiers in computational revealed preference analysis

Philippe Palanque (University of Toulouse): Harnessing Usability, UX and Dependability for Interactions in Safety Critical Contexts

Abstract: Innovation and creativity are the research drivers of the Human-Computer Interaction (HCI) community which is currently investing a vast amount of resources in the design and evaluation of “new” user interfaces and interaction techniques, leaving the correct functioning of these interfaces at the discretion of the helpless developers. In the area of formal methods Philippe Palanque (University of Toulouse): Harnessing Usability, UX and Dependability for Interactions in Safety Critical Contexts

Nguyen Dang (University of St Andrews): Hyper-Parameter Tuning for an evolutionary algorithm

Abstract: In this talk, I will present a case study to illustrate how automated algorithm configuration can be used to gain insights into theoretical results on an evolutionary algorithm, namely the (1+(λ,λ)) Genetic Algorithm. This work is a collaboration with Carola Doerr. The (1+(λ,λ)) Genetic Algorithm is an evolutionary algorithm that has interesting theoretical properties. Nguyen Dang (University of St Andrews): Hyper-Parameter Tuning for an evolutionary algorithm