Applied Mathematics workshops

The training school develops three foundations for AI in scientific computing. Daniel Bartl introduces statistical learning through high-dimensional probability, explaining how the geometry and complexity of function classes govern generalization. Stanislav Budzinskiy covers floating-point errors, deterministic and stochastic rounding, matrix multiplication, mixed precision and conditioning in neural-network training and inference. Maximilian Herde introduces neural operators and foundation models for partial differential equations, studying pretraining, scaling and transfer to new physical problems, with practical Python exercises. The school meets in person at RICAM, Science Park 2, fourth floor, Altenberger Straße 69, Linz, on 5–9 October. Monday through Thursday lectures start at 09:00 and run to 12:30, with discussion and exercise sessions generally scheduled 13:30–17:00; Wednesday has an excursion. Friday’s lectures run 09:00–11:30. Times are Europe/Vienna, CEST (UTC+02:00). The registration form still lists the Training School and requires an institutional email address. Registration is binding; participants should review the organizer’s cancellation and non-attendance policy. The event begins shortly after this listing’s publication.

This RICAM workshop examines the numerical foundations of reliable deep-learning computation. Low-precision and mixed-precision arithmetic, quantization and parallel GPU computation enable larger models and deployment on constrained devices, while introducing questions about error, stability and performance. Researchers from theoretical and applied communities will compare methods for stable training and efficient inference and identify directions for further work. The programme brings together expertise in numerical analysis, machine learning and hardware-aware computation. The in-person workshop runs on 2–6 November at RICAM, Science Park 2, fourth floor, Altenberger Straße 69, Linz. Monday registration and welcome run 14:00–14:40, followed by talks until 17:00. Tuesday through Friday sessions begin at 09:00; Friday’s programme ends at 11:40. Times are Europe/Vienna, CET (UTC+01:00). Register through the special-semester form, select Workshop 2 and Regular Participant, and use an institutional email address to verify affiliation. Registration is binding; the organizer asks for cancellation at least one week before the workshop and states that unreported non-attendance can exclude participants from future events.

The second M4H workshop brings mathematics, data science and medical research together to improve care. Mathematical models, numerical methods, statistics and machine learning support precision medicine, healthcare-resource allocation and multimodal patient-data analysis. Keynotes examine evolutionary cancer treatment and model-driven digital twins, longitudinal health records, topology and geometry for cyclic gene expression, statistical approaches to individual mental-health variation and patient-specific blood-flow simulation. Panels connect these methods with cardiovascular medicine, health data science and sustainable brain health. The Department of Mathematics at Politecnico di Milano hosts the in-person workshop on 11–13 November 2026 in Aula Rogers, Building 11, Via Ampère 2, Milan. Posters use Aula Vetrata, Trifoglio Building 13, Via Bonardi 9. The programme runs 11 November 14:00–19:30, 12 November 09:00–18:10 and 13 November 09:00–16:00. Times are Europe/Rome, CET (UTC+01:00). Registration is free and open until 15 October through the form on the official workshop page. Speaking slots are invitation only; general attendance is open, and participants may submit poster abstracts.