Computational Mathematics

David Persson presents Polar Express, a method for polar decomposition and the matrix sign function motivated by the Muon neural-network optimizer. It uses matrix multiplications suited to GPUs and adapts each polynomial update through a minimax problem to reduce worst-case error. The talk covers convergence, finite-precision implementation in bfloat16 and validation-loss improvements when training GPT-2 on FineWeb data. This is an in-person PACM IDeAS seminar at Princeton.

SC26 connects researchers and practitioners developing high-performance computing, networking, storage and scientific data analysis. The programme includes research presentations, tutorials and workshops at McCormick Place in Chicago, 15–20 November 2026. Registration is open, with early rates through 14 October. A digital participation option is available through the official registration page.

ISEC brings together ecologists, statisticians and quantitative researchers working on ecological inference and modelling. The main conference runs 10–15 January 2027 in Mérida, Mexico; separate pre-conference workshops run 8–9 January. Registration is open, with early rates through 30 September 2026 and reduced rates for eligible Global South participants.

SIAM’s computational science and engineering meeting covers mathematical models, scalable algorithms, scientific software, high-performance computing and scientific machine learning. It takes place in Pittsburgh on 22–26 February 2027. Travel-support applications are open until 30 November 2026. Abstract deadlines have passed; registration is scheduled to open in December 2026. The meeting is co-located with ACDA27 and the International Meshing Roundtable.

Stockholm University's Department of Mathematics is recruiting a postdoctoral fellow to develop numerical time-stepping methods for glacial ice dynamics. The project connects computational mathematics, continuum modeling and climate-response questions through efficient and reliable simulation methods.

RIKEN invites junior researchers with advanced mathematical, computational or information-technology skills to support research across its host laboratories, including AI for Science. Around five appointments are planned. Fellows primarily join the Research Engineer Enablement Office in Wako and also work with a host laboratory; annual contracts may be renewed for a maximum of three years. Applicants must first secure a prospective host’s agreement. Mandatory registration closes on 23 October 2026 at 17:00 JST; documents and two reference letters are due on 30 October at 14:00 JST. The listed deadline is the earlier mandatory registration stage. Employment must begin between 1 April 2027 and 1 January 2028. The call and submission instructions are currently open.

Qihao Ye examines practical, affordable ways for individual mathematicians to use AI in literature searches, proof development and verification. The talk identifies seven recurring failure modes and introduces the open-source QED multi-agent system designed to address them. A case study on constructing Carleman weights for wave equations on the half-line shows how automated search can be combined with a researcher’s mathematical insight and rigorous checking. The organizer links both the implementation and the accompanying research paper. Online via Rutgers DIMACS Zoom. Wednesday 23 September 2026, 10:00–11:00 EDT (America/New_York; UTC−4). Use the Zoom link and passcode on the official event page. Organized by the DIMACS AI and Mathematics Seminar. This listing advertises the verified remote attendance option.

David Persson presents Polar Express, a method for polar decomposition and the matrix sign function motivated by the Muon neural-network optimizer. It uses matrix multiplications suited to GPUs and adapts each polynomial update through a minimax problem to reduce worst-case error. The talk covers convergence, finite-precision implementation in bfloat16 and validation-loss improvements when training GPT-2 on FineWeb data. This is an in-person PACM IDeAS seminar at Princeton.

AISTATS brings together machine learning, artificial intelligence and statistics, with an emphasis on methodological research. The 2027 conference is announced for 3–6 May in Montréal, Canada; the specific venue and registration details remain to be announced. The official schedule lists abstract submissions due 29 September 2026 and full papers due 6 October 2026, both at the end of the day Anywhere on Earth.

ISEC brings together ecologists, statisticians and quantitative researchers working on ecological inference and modelling. The main conference runs 10–15 January 2027 in Mérida, Mexico; separate pre-conference workshops run 8–9 January. Registration is open, with early rates through 30 September 2026 and reduced rates for eligible Global South participants.