BEGIN:VCALENDAR PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3- M3//EN VERSION:2.0 CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VEVENT DTSTAMP:20240923T090025Z DTSTART:20240930T130000Z DTEND:20240930T140000Z SUMMARY:Mauricio Álvarez -- Bridging the gap between data-driven and mech anistic modelling: the latent force model approach. [IN PERSON] UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}in0-m0mhogu 0-e2b36i DESCRIPTION:Join us for this seminar by Mauricio Álvarez (Manchester) as part of the North West Seminar Series in Mathematical Biology and Data S ciences. More details about the joint series can be found here https://n orthwestseminars.great-site.net/ . \n\nThe talk will be hosted in person in room 3.40 of the Simon Building. For those who cannot attend in pers on the talk will also be streamed via zoom\, please contact carl.whitfie ld@manchester.ac.uk or igor.chernyavsky@manchester.ac.uk for the zoom li nk\, or sign up to the mailing list.\n\nTitle: Bridging the gap between data-driven and mechanistic modelling: the latent force model approach. \n\nAbstract: Current machine learning models tend to be purely data-dri ven models where computation and data size are key requirements. By enco ding physics into machine learning models\, either in the form of ordina ry or partial differential equations\, we can develop data-efficient app roaches to data modelling that in many cases can provide further insight s into the data-generation mechanism. In this talk\, I will introduce a family of such physics-inspired machine learning models\, which we have coined as Latent Force Models. A latent force model is a Gaussian proce ss with a covariance function inspired by a differential operator. Such a covariance function is obtained by performing convolution integrals be tween Green's functions associated with the differential operators and c ovariance functions associated with latent functions. Latent force model s have been used in several fields for grey box modelling and Bayesian i nversion. In this talk\, I will introduce latent force models and severa l recent works in my group where we have extended this framework to non- linear problems. \n\nBio: \nMauricio A. Álvarez is a Senior Lecturer in Machine Learning in the Department of Computer Science at the Universit y of Manchester. Before joining Manchester\, he was an Associate Profess or at Universidad Tecnológica de Pereira\, Colombia\, and a Senior Lectu rer at the University of Sheffield. He is interested in machine learning in general\, its interplay with mathematics and statistics and its appl ications. In particular\, his research interests include probabilistic m odels\, kernel methods\, and stochastic processes. He works on the devel opment of new probabilistic models and their application in different en gineering and scientific areas including Neuroscience\, Neural Engineeri ng\, Systems biology\, and Humanoid Robotics\, among others. He is inter nationally known for his work on multi-output Gaussian processes and phy sically inspired probabilistic modelling. \n \nDr Álvarez is the Direct or of a new AI Centre for Doctoral Training on Decision Making for Compl ex Systems in Manchester. He is Associate Editor for the Transactions on Machine Learning OpenReview journal. He has been area chair for several machine learning conferences\, including the Advances in Neural Informa tion Processing Systems (NeurIPS) conference\, the Uncertainty in Artifi cial Intelligence (UAI) conference\, the International Conference on Lea rning Representations (ICLR)\, the AAAI Association conference and the A rtificial Intelligence and Statistics (AISTATS) conference. He has been the main organiser of the Gaussian process summer school in the UK since 2017 and co-organiser of the ELLIS (European Laboratory for Learning an d Intelligent Systems) Summer School on Machine Learning for Healthcare and Biology. \n\nTo subscribe to the mailing list for this event series\ , please send an e-mail with the phrase “subscribe math-lifesci-seminar” in the message body to listserv@listserv.manchester.ac.uk STATUS:TENTATIVE TRANSP:TRANSPARENT CLASS:PUBLIC LOCATION:3.40\, Simon Building\, Manchester END:VEVENT END:VCALENDAR