MOX COLLOQUIA

MOX COLLOQUIA

MOX Laboratory - Department of Mathematics, Politecnico di Milano

Founded in 2002, the Laboratory for Modeling and Scientific Computing (MOX) is part of the Department of Mathematics at Politecnico di Milano. MOX Laboratory conducts cutting-edge interdisciplinary research in mathematical modelling, numerical analysis, applied statistics, and scientific computing, applying these fields to address society's major challenges.

MOX Laboratory engages in extensive outreach efforts, with its flagship initiative being the "MOX Colloquia" seminar series. The “MOX Colloquia” offers a pivotal platform where globally renowned scientific experts deliver presentations, fostering intellectual exchange and advancing knowledge in the broad fields of applied mathematics and statistics, computational learning, and computational science.

Since 2024, SpringerNature is glad to host the MOX COLLOQUIA seminars on its Springer Mathematics & Statistics webinars channel and make them accessible to Cassyni’s readers.

MOX Colloquia Scientific Committee: Paola F. Antonietti, Gabriele Ciaramella, Laura Sangalli.

MOX Colloquia Organizing Committee: Gabriele Ciaramella, Laura Sangalli.

For more information about MOX Laboratory and the “MOX Colloquia” seminar series, please visit: mox.polimi.it

To follow MOX Laboratory on LinkedIn, please visit: linkedin.com/company/labmox

Speakers
Community

October 2025

SuMoTuWeThFrSa
1234
567891011
12131415161718
19202122232425
262728293031
University of Cambridge
The Alan Turing Institute

Statistical Finite Element Methods

Prof Mark Girolami
Mark Girolami
University of Cambridge and The Alan Turing Institute
Thursday, October 9, 2025 2:00 PM (GMT+2)
RSVP to seminar
Imperial College London

Compatible finite elements for numerical weather prediction

Prof. Colin Cotter
Colin Cotter
Imperial College London
Thursday, October 16, 2025 2:00 PM (GMT+2)
RSVP to seminar

Published seminars

Oregon State University

Multi-* mathematics and simulations of coupled processes the Arctic

Malgorzata Peszynska, Oregon State University
University of Cambridge

Can we discover fundamental laws from data using AI?

Mihaela van der Schaar, University of Cambridge
Eindhoven University of Technology
Technical University of Munich

Advancing Scientific Machine Learning in Industry

Wil Schilders, Eindhoven University of Technology and Technical University of Munich
ETH Zurich

Lattice Boltzmann for solid mechanics: elastostatics and elastodynamics

Laura De Lorenzis, ETH Zurich
University of Amsterdam

A quasi-optimal space-time finite element method for parabolic equations

Rob Stevenson, University of Amsterdam
University of Pavia

Isogeometric Analysis: Some recent advances with applications to complex and coupled problems

Alessandro Reali, University of Pavia
Technische Universität Berlin

Machine Learning and AI for the Sciences: toward understanding

Klaus-Robert Müller, Technische Universität Berlin
King Abdullah University of Science and Technology

Exascale Geostatistics for Environmental Data Science

Marc G. Genton, King Abdullah University of Science and Technology
Portland State University

From scalar to tensor finite elements

Jay Gopalakrishnan, Portland State University