Konferenzen  >  Mathematik  >  Statistik, Spieltheorie

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

This workshop will bring together researchers working on two fields seemingly at odds, and incompatible: combinatorial geometry and uncertainty. Combinatorial geometry is a field of counting discrete objects, it is about how things are connected, or in general how discrete objects like points, lines, and circles intersect with each other. On the other hand, modeling uncertainty is about not allowing discrete events to happen precisely, or at least not with confidence. It replaces these notions with probabilistic concepts. How can one count geometric objects and their connectivity properties if there is uncertainty in their very existence?

Geometrie und Topologie,    Graphentheorie und Kombinatorik

Jagiellonian University, Faculty of Mathematics and Computer Science

Probabilistic methods were pioneered in combinatorics, often providing the first constructions of interesting classes of objects. We are now at a point where these methods evolved to yield important new examples in Riemannian geometry and dynamics. On the other side, ergodic methods were for a long time used to prove rigidity theorems in Riemannian geometry, a trend that continues to this day. Sometimes these two approaches can be fruitfully combined. The goal of this meeting is to explore interactions of probabilistic and ergodic methods in broadly understood geometry.

Organiser email;     Email: pierogy@matinf.uj.edu.pl

probabilistic notions of convergence of actions, random subgroups, random manifolds, geometric group theory, ergodic theory, measurable actions, measurable equivalence relations, percolation on graphs, stochastic geometry

Geometrie und Topologie,    Gruppentheorie

CRM / Université de Montréal

CRM

Zahlentheorie, Arithmetik,    Kurse und Veranstaltungen für Studenten der Mathematik

This is a regional conference intended to foster interactions between probability researchers in Upstate New York and nearby areas. Students and postdocs are very welcome.

CRM / Université de Montréal

Summer school “Universal Statistics in Number Theory” (May 4 - 15) will introduce the key topics from both sides of the program, including lecture series by probabilists as well as number theorists.

part of Montreal program 2026: Universal Statistics in number theory

Kurse und Veranstaltungen für Studenten der Mathematik,    Zahlentheorie, Arithmetik

Hosted by American University in Washington, DC

The International Workshop in Sequential Methodologies (IWSM) meets every two years and brings together researchers and practitioners to explore the latest advancements in sequential statistics. The workshop provides a platform for presenting theoretical results, exploring practical applications, and addressing challenges in areas such as sequential testing, change-point detection, sequential estimation, selection and ranking, machine learning, artificial intelligence, clinical trials, adaptive design, stochastic quality and process control, optimal stopping, stochastic approximation, applied probability, mathematical finance, and related fields of probability, statistics, and applications. By covering a broad range of methodological and applied areas, IWSM aims to advance sequential statistical analysis and its diverse modern applications.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Particle physics, astrophysics, and cosmology cover the study of the universe from its smallest to largest scales. We study them in order to understand both the fundamental interactions that govern the universe and its large-scale structure and history. Advanced detectors at collider facilities that accelerate particles to near the speed of light and telescopes that monitor the night's sky looking back to the time just after the Big Bang are used to collect vast amounts of data in complicated datasets. Analyzing these data requires modern tools, making use of advanced machine learning and high performance computing infrastructure. This workshop brings together physicists, statisticians, and machine learning experts in order to make the most use of this data and learn as much from it as possible by discussing how to address the complexities of these large and detailed datasets, searching for 1 in a trillion events, and understanding correlations between thousands of quantities.

Mathematische Physik,    Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen

We are pleased to announce the International Conference on Frontiers in Probability and Statistics: Celebrating the Distinguished Contributions of N. Balakrishnan on His 70th Birthday, to be held from June 11 to 13, 2026, in Xuzhou, China, celebrating the remarkable career and 70th birthday of Professor Narayanaswamy Balakrishnan, one of the most prolific and influential researchers in statistics and mathematics.

The Chinese University of Hong Kong, Institute of Mathematical Statistics (IMS)

The seventh meeting of the Institute of Mathematical Statistics Asia Pacific Rim Meeting (IMS-APRM) will take place in Hong Kong from June 13 to June 16, 2026, and will be hosted by The Chinese University of Hong Kong. This event will serve as an exceptional global forum for scientific communication and collaboration among researchers from Asia and the Pacific Rim. It aims to foster connections and partnerships between researchers in this region and colleagues from around the world. Building upon the successes of previous meetings, the seventh meeting will enhance our ongoing efforts to fulfil our shared mission within the statistical profession.

The IPMU conference is organized every two years with the focus of bringing together scientists working on methods for the management of uncertainty and aggregation. It also provides a forum for the exchange of ideas between theoreticians and practitioners in these and related areas. The 2026 edition of IPMU will take place at the Faculty of Economics of Sapienza University of Rome, Italy, in the nearby of the University city of Rome and the main train stations (Termini and Tiburtina). The 2026 edition of IPMU celebrates the 40th anniversary from the first edition of the conference that took place in Paris, France. The choice of Rome for this special occasion is not a case since Rome and Paris are exclusively and reciprocally twinned, starting from April 1956.

The University of Chicago Booth School of Business

The 2026 conference will be sponsored by the Operations Management Group at the University of Chicago Booth School of Business, and will be held in the heart of downtown Chicago at Gleacher Center. During the 2026 conference, you can enjoy Chicago’s culinary and brewery scenes, street festivals, and museums as spring turns into summer in our amazing metropolis.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

In recent years some of the most exciting breakthroughs in Combinatorics on longstanding conjectures have resulted from innovative applications of established techniques to areas where they have not necessarily been used before. We would like to harness the power of collaboration and bring together open-minded participants with different areas of expertise to produce novel research in a number of globally studied areas including. We aspire to create new productive long-term bonds between members of the global community.

EasyConferences Ltd.

The symposium will showcase recent advances and emerging trends in all areas of nonparametric statistics and machine learning. Its scientific program will feature plenary lectures by leading experts, special invited sessions, contributed talks, and a dedicated student poster session. To encourage and recognize the contributions of young researchers, a student paper competition will be held within the poster session, with travel support awarded to the winners.

The 18th World Meeting of the International Society for Bayesian Analysis will take place June 28 - July 3, 2026 in Nagoya, Japan at WINC AICHI. The purpose of the meeting is to bring together the diverse international community of investigators in statistics who develop and use Bayesian methods to share recent findings and to present new and challenging problems.

Institute of Mathematical Statistics

American Statistical Association (ASA)

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Catastrophic events, even though they happen rarely, have a significant impact when they occur. Disastrous climate, financial, insurance or complex network failure events can have devastating social and environmental consequences. A complete risk analysis for modelling and prediction purposes requires understanding how these extreme, rare events occur, and what are the main drivers causing them. Machine learning methods open the road for methodological developments to forecast these extreme events and discover their complex, possibly high-dimensional nature. The aim of this workshop is to bring together researchers contributing to closely related, but culturally disconnected research communities: extreme value theory and machine learning. The goal is to discuss new directions and open mathematical problems, and foster further collaboration. The leading experts will introduce young researchers, postdocs and graduate students to the state-of-the-art in the field.

Angewandte Mathematik (allgem.),    Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

The proposed workshop focuses on recent breakthroughs in arithmetic functions, L-functions, and pseudorandomness in Number Theory, as well as new connections to other areas (via both employed methods and applications), such as additive combinatorics, arithmetic statistics, and dynamical systems. Such breakthroughs include the latest advances in the study of various character and exponential sums (e.g., Kloosterman sums), which are powerful and versatile tools for understanding the anatomy of integers; moments, and other averages of L -functions (over number fields and function fields); integers having a prescribed arithmetic structure (e.g., prime, smooth, square-free, sum of two squares), along with their distribution in arithmetic progressions and short intervals, as well as function field analogs of these problems; distribution of elements of multiplicative subgroups; pseudorandomness of arithmetical functions. An essential aspect of these developments is the vast arsenal of tools and techniques that have been conceived, developed, and implemented, ranging from analysis to additive combinatorics, algebraic geometry, and random matrix theory. The proposed workshop will concentrate on the emerging methods that underlie such advances and on possible future directions.

Zahlentheorie, Arithmetik,    Graphentheorie und Kombinatorik

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

The fields of kernel approximation and Gaussian processes have evolved, mostly independently, over the past few decades to become mature subjects in numerical analysis and statistics/applied probability, respectively. The two fields have natural affinities, both by relying on a common mathematical machinery of reproducing kernel Hilbert spaces and positive definite functions, and by treating a diverse assortment of scientific applications. A hallmark problem for kernels is the modeling of irregularly sampled, deterministic data, but kernels are also heavily employed in the simulation of a range of phenomena from atmospheric flows to financial forecasting.; a key feature of kernel approximation is the ability to deliver solutions to computational problems without requiring costly underlying geometric structures like meshing. Gaussian processes have traditionally focused on regression, classification, and learning of the underlying probability distribution from random samples, although recent activity has focused on successful undertakings in interpretable and explainable machine learning, inverse problems and partial differential equations with stochastic parameters. Despite their different languages and different objects of interest, the fields share a similar collection of important, challenging theoretical and computational problems.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Understanding the resilience of ecosystems will be critical for our future considering the multiple crises that we face. Current understanding of resilience relies largely on examining the consequences of disturbances in a deterministic framework. However, disturbances will be arguably stochastic in nature. Disturbances will be multifaceted, often repeated, and probably highly variable over time. Furthermore there is uncertainty and randomness in the processes that respond to the disturbances. Consequently, resilience is most naturally explored in the context of stochasticity: randomness in disturbances must be expected and prepared for. In this workshop, we aim to bring together stochastic and deterministic researchers studying the resilience of ecosystems and the resilience of human economic-ecosystem interactions.

Angewandte Mathematik (allgem.),    Ökosysteme, Umwelt und nachhaltige Entwicklung

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Evolving random structures are discrete mathematical models that evolve randomly `step-by-step’ over time. Such evolving structures are powerful tools, and the topic has been receiving considerable attention from combinatorics, probability, computer science and related communities. For example, in combinatorics some of the best-known extremal constructions arise from natural evolving random hypergraph models, and in probability many interesting dynamic network models arise from carefully crafted evolving random graph models. Interestingly, in combinatorics and probability slightly different sets of ideas have been proposed for analyzing such random evolving structures, which in turn have also found applications in the analysis of randomized algorithms.