Conferences  >  Mathematics  >  Probability and Statistics, Game Theory

Institute of Mathematical Statistics (IMS)

The objective of ICSDS is to bring together researchers in statistics and data science from academia, industry, and government in a stimulating setting to exchange ideas on the developments of modern statistics, machine learning, and broadly defined theory, methods, and applications in data science.

CIRM – Centre International de Rencontres Mathématiques

Statistical thinking in the age of AI : robustness, fairness and privacy

Institute of Mathematical Statistics

IMS-APRM will provide an excellent forum for scientific communications and collaborations for the researchers in Asia and the Pacific Rim, and promote communications and collaborations between the researchers in this area and those from other parts of the world. The program covers a wide range of topics in statistics and probability, presenting recent developments and the state of the art in a variety of modern research topics and in applications.

Institute for Computational and Experimental Research in Mathematics (ICERM)

This workshop focuses on connections between higher-order statistics and symmetric tensors, and their applications to machine learning, network science, and other domains. Higher-order statistics refers to the study of correlations between three or more covariates. This is in contrast to the usual mean and covariance, which are based on one and two covariates. Higher-order statistics are needed to characterize complex data distributions, such as mixture models. Symmetric tensors, meanwhile, are multi-dimensional arrays. They generalize covariance matrices and affinity matrices and can be used to represent higher-order correlations. Tensor decompositions extend matrix factorizations from numerical linear algebra to multilinear algebra. Recently tensor-based approaches have become more practical, due to the availability of bigger datasets and new algorithms. The workshop brings together applied mathematicians, statisticians, probabilists, machine learning experts, and computational algebraic geometers. Presentations will expose how symmetric tensors, with nonlinear algebra and non-convex optimization, provide natural mathematical machinery for exploiting higher-order interactions. Topics include moment tensor decompositions; spectral methods for hypergraphs; and related random matrix theory.

Email: info@icerm.brown.edu

Applied Mathematics (in general),    Algebra

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

Mathematical Physics,    Courses and Events for Math Students and Early Career Researchers

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

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

Courses and Events for Math Students and Early Career Researchers,    Neural Networks and Artificial Intelligence, Machine Learning

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Stochastic partial differential equations arise naturally in several models of random phoenomena, in such fields as biology, physics and engineering. In particular, while deterministic models represent an efficient tool to describe time-evolution of real-world systems, they fail in rendering the presence of possible microscopic uncertainty of the model. Such randomness components may be related to several factors and are usually tracked through the introduction of a stochastic source of randomness in the equations involved. One of the most challenging goal of the mathematics of stochastic partial differential equations is the understanding of quantitative and qualitative properties of solutions and their dependence on the coefficients. The aim of the workshop is to bring together leading experts, as well as a number of talented young researchers and to share fresh information on new results and future perspectives. The stay at the Institute will promote informal interaction progress in collaborations.

United Arab Emirates University, Department of Mathematical Sciences

Stochastic calculus finds so extensive applications and enables us to analyze various phenomena affected by random factors, such as asset price movements, option pricing, risk assessment, and geopolitical or climate risks. Moreover, stochastic calculus's effectiveness extends beyond finance, with its use in physics, biology, engineering, and economics, where it helps describe and analyze systems influenced by random events. Its ability to handle stochastic processes makes it an invaluable resource for comprehending and navigating uncertain environments and investigating the panel of risks that affect and impact financial markets or supply chains in commodity one. Another powerful tool for modeling risks is machine learning. Machine learning has emerged as a powerful tool for modeling complex (financial and or economic) systems and making data-driven decisions. By exploiting advanced algorithms, machine learning enables the analysis of vast datasets to identify patterns, forecast market trends, and manage risks. The capabilities of these models range from forecasting stock prices and optimizing investment portfolios to detecting fraudulent activities and assessing credit risk. Machine learning's ability to adapt to changing market conditions and handle non-linear relationships makes it indispensable in the ever-evolving world of finance but also all possible relationships and contagion on commodity markets, offering the potential to enhance decision-making, reduce errors, and unlock valuable insights from data.

Organizing committee;     Email: youssef_elkhatib@uaeu.ac.ae

Financial mathematics, stochastic models, pricing financial derivatives, commodity markets, energy markets, global energy markets, geopolitical risk, climate change, natural resources, energy transition, volatility modeling, high-frequency data analysis, machine learning prediction models, asymmetric structures, software development with applications, complex networks, big data analytics, nonlinear time series analysis, recurrent neural networks, Stochastic calculus and machine Learning with applications to other sciences.

Mathematical Economics and Finance,    Neural Networks and Artificial Intelligence, Machine Learning

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

Society for Industrial and Applied Mathematics (SIAM)

Uncertainty quantification (UQ) is essential for establishing the reliability of predictions made by computational models. Such models are often deterministic, and the UQ discipline has traditionally focused on how to quantify uncertainty in that case. Statistical methods play a major role in that effort, and represent an alternative modeling strategy when less is known about the system of interest. The two approaches are naturally synergistic, and with the emergence of machine learning as a practical tool, it is becoming more important than ever to view the whole UQ ecosystem through a unifying lens. UQ is application-driven and inherently interdisciplinary, relying on a broad range of mathematical and statistical foundations, domain knowledge, and algorithmic and computational tools. UQ24 will bring together mathematicians, statisticians, scientists, engineers, and others interested in the theory, development, and application of UQ methods.

Major conference themes will include mathematical and statistical foundations, data-driven approaches and computational advances, applications of UQ in biology, medicine, environmental and climate sciences, decision making for societal benefit, and all areas of physical science and engineering.

In the last decade, several studies have raised concerns about a high prevalence of errors in published research. Theoretical arguments have suggested the possibility that the number of false claims in the scientific literature is much higher than desired and reports across fields have subsequently shown that many findings that researchers believed were firmly established failed to replicate. These erroneous claims in the scientific literature raise important issues. From an empirical practitioner’s perspective, errors mislead and slow down research projects. From a philosophical perspective, scientific error raises questions about the right forms of scientific inference, scientific progress, and the reliability of science as a source of knowledge. From the perspective of the general public, scientific error undermines the epistemic authority of science and the degree to which policy-makers trust scientific experts.

Research Institute for Mathematical Sciences (RIMS)

Eastern North American Region International Biometric Society (ENAR)

Fields Institute

The YEP Workshop is a yearly event organized by EURANDOM at Eindhoven University of Technology and this is the 19th workshop in this successful series. It is geared towards learning, development and networking opportunities for young researchers. The topic of the 2024 workshop is “Interplay between local and global graph structures”. An exciting area of research that covers a wide range of sub-topics within probability theory, random graph theory, and combinatorics, among others. A lot of cutting-edge research is currently undertaken in this area, so that perfectly lends itself as the subject of the 2024 workshop.

Seminar on Stochastic Processes has the status of Permanent Co-sponsored Meeting of the Institute of Mathematical Statistics.

Seminar on Stochastic Processes is a series of annual conferences devoted to stochastic analysis, Markov processes and other topics in probability theory of current interest. Every conference features five invited speakers and provides opportunity for short informal presentations of recent results and open problems.

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

The study of return-times and hitting-times of small sets ("rare events") in ergodic dynamical systems has undergone some intense research in the past 20 years. It is part of the wider field of probabilistic properties of (deterministic) dynamical systems. One general motivation from the sciences is that the times at which various extreme events (possibly modeling catastrophes in real-world systems) occur, often cannot be predicted over a reasonably long period. It is therefore important to at least understand the statistical laws governing these occurrences. Accordingly, there are well-developed stochastic models describing such rare events and enabling further theoretical analysis. These stochastic descriptions, however, necessarily start from simplifying assumptions and sometimes disregard additional detailed knowledge about the mechanisms driving the system. In contrast, there is a branch of ergodic theory which aims at analysing dynamical system models which incorporate this extra information, and rigorously derives statistical properties from the underlying deterministic dynamics. The central theme of this workshop is the aspiration for finding and proving probabilistic limit theorems which clarify patterns of occurrences of extreme events in nonlinear dynamics.

Systems theory, Control and Automation,    Complex Systems, Chaos and Self-Organisation

EURANDOM is turning 25 and we’re organizing a special anniversary event in the week of April 15-19, 2024 celebrating this wonderful milestone. The program will feature keynote talks by renowned speakers, reflect on some of the scientific highlights in the EURANDOM history as well as emerging research directions, cherish memories of the early days of the EURANDOM community, and discuss broader career perspectives for junior researchers as well as concepts for future workshops in a changing world.

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

ICMS - International Centre for Mathematical Sciences

The proposed summer school is devoted to the fundamental theory, state-of-the-art methodologies and real-world applications of Bayesian filtering, including sequential Monte Carlo (SMC) algorithms and other popular techniques, such as sigma-point methods for nonlinear Kalman filtering, Gaussian-mixture filters and others. We will introduce the basics of the field, so the summer school can be followed by an heterogenous audience. Basic notions on linear algebra, statistics, probability, and calculus are recommended. Then, the main targeted group for this summer school is PhD students with background in mathematics, statistics, physics, engineering, or computer science (but not only). Other students (e.g., last year MSc students) and early-career academics with similar background are also invited to apply. We intend to schedule 5 short courses (one-day tutorials) from renowned international researchers. These tutorials will cover the theoretical foundations of the field, current methodological trends, and relevant applications. Some courses may include hands-on sessions in (personal) computers. The target audience include researchers working on the field, as well as students aiming at being introduced to the topic.

Modeling and Simulation,    Courses and Events for Math Students and Early Career Researchers

ICMS - International Centre for Mathematical Sciences

Sequential Monte Carlo (SMC) methods, also known as particle filters or particle methods, have become popular and powerful tools for computational inference in complex probabilistic models used in many and varied fields and applications. The research community includes practitioners and theoreticians at the intersection of statistics, computer science, electrical engineering, and applied mathematics. The interest in SMC methods have rapidly grown in the last decade, jointly with the new challenges and the enormous potential of SMC to tackle high-impact problems in applied sciences (e.g., meteorology, biomedicine, robotics, etc.).

CIRM – Centre International de Rencontres Mathématiques

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Neural Networks and Artificial Intelligence, Machine Learning,    Courses and Events for Math Students and Early Career Researchers

Institute for Pure and Applied Mathematics (IPAM)

The interplay of integrable models of statistical mechanics with combinations of probability theory and algebraic methods such as transfer matrix formalism, diagram algebras, and quantum group techniques, has proved fruitful in the past decades in both mathematics and physics. It has been particularly beneficial to enhance interactions between researchers working at the interfaces of these areas. The aim of this workshop is to bring together experts in algebraic and probabilistic aspects of solvable lattice models as well as researchers working on related algebraic subjects who have a common interest in understanding universal phenomena such as KPZ behavior, limit shapes, and convergence of lattice models to CFT predictions. In particular, we aim to develop interactions between different approaches to the study of lattice models, such as Bethe ansatz, (inhomogeneous) CFT methods and the tangent method. Other topics of potential interest include multi-species, forest fires and sandpile models, for which such interactions are less developed as for now. We also intend to foster interactions between researchers studying quantum groups and CFT on the one hand and probabilists working on SLE/CLE topics on the other, hoping for a fruitful synthesis of ideas and techniques.

Department of Mathematics at Utah Valley University (UVU)

The International Workshop in Sequential Methodologies (IWSM) is a biennial conference that covers all aspects of sequential methodologies in statistics, applied mathematics, information theory, change point detection, and virtually any applications in mathematical finance, quality control, clinical trials, signal and image processing, and other related fields. The goal of the workshop is to bring together researchers and users from all areas of sequential methodologies. The presentations are not limited to the field of sequential analysis. Speakers from other areas of probability, statistics, and related areas are welcome.

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

Complex Systems, Chaos and Self-Organisation,    Modeling and Simulation

Casa Matemática Oaxaca (CMO)

Large-scale, high-dimensional data sets are becoming ubiquitous in modern society, particularly in the areas of physical, biomedical, and social applications. For example, in the problem of predicting thyroid malignancy from biopsy images, the images are typically about 150,000 by 100,000 dimensions, which limit the application of many existing methods. There is an urgent need for accurate and efficient mathematical and statistical tools for the analysis and engineering of high-dimensional data sets. The proposed 5-Day workshop will bring researchers from different disciplines to collaboratively address the foundational computational and theoretical challenges in high-dimensional data analysis. The workshop is designed around the simple question ``how to accurately and efficiently process large-scale data in 10+ dimensions’’. Invited participants will review existing mathematical and statistical tools for high dimensional data sets, including the Monte Carlo methods, randomized algorithms, dimension reduction, sparse grid, network analysis, and interpolation-based deep neural networks, and compare their performance and address their limitations. The invited participants will collaboratively address the current challenges in high-dimensional data analysis, and design new strategies by combining existing tools and by introducing new methodologies for problems in 10+ dimensions.

Complex Networks,    Applied Mathematics (in general)

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The fundamental and simple model of Stochastic Games was introduced in the fifties by prominent mathematicians (Shapley, Bellman) and form today a well adopted and thoroughly studied notion, with applications in computer science, mathematics, economics, and beyond. This wide adoption implies that many different research communities are involved in the study of stochastic games, oftentimes with different but related goals. Each community develops different tools towards understanding stochastic games, leading to a very broad and diverse literature with a variety of techniques and approaches. The goal of this Dagstuhl Seminar is to bring together researchers interested in the algorithmic aspects of Stochastic Games, from a theoretical as well as practical perspective.

ICMS - International Centre for Mathematical Sciences

In broad terms, this workshop will be centered on topics that lie in the intersection of mathematics and democracy. There are various strands of activity in this area, some more recent and some classical; for example, modern social choice theory has been around in some form since the 1780s, while rigorous mathematical approaches to districting are a recent development. Despite the differences in history and techniques, topics such as voting, representation, and districting have a common thread – they are motivated by the notion that mathematics can play an important role in making democracy work better for everyone. There is consequently a growing awareness that interweaving the research – and consequently the events featuring it – in these areas is desirable and necessary. This workshop will be guided by this principle.

This workshop aims to be interdisciplinary, bringing together researchers in the fields of computer science, economics, mathematics, and political science. The main themes of the workshop will be · Social Choice (theoretical and computational) · Proportional representation · Districting

Applied Mathematics (in general),    

Department of Mathematics, University of Coimbra, Portugal

The conference will bring forth recent advances and trends in the mathematical theory of ordered statistical data, in order to facilitate the exchange of research ideas, promote collaboration among researchers from all over the world, and contribute to the further development of the field.

Approximations, Bounds, Characterisations, Recurrence, Relations, Distribution Theory and Probability Models, Stochastic Orders, Reliability Theory and Survival Analysis, Censoring, Concomitants, Statistical Interference, Applications of Ordered Data, Information and Entropies, Nonparametric Methods, Shape Constrained Estimation, Ranked Set Sampling, Asymptotic Theory

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

This workshop will bring together applied mathematicians who work on different aspects of stochastic differential games to discuss the recent achievements and challenges in light of new trends.

game theory

The conference will bring forth recent advances and trends in several areas of nonparametric statistics, in order to facilitate the exchange of research ideas, promote collaboration among researchers from all over the world, and contribute to the further development of the field. The program will include plenary talks, special invited talks, invited talks, contributed talks and a poster session on all areas of nonparametric statistics.

Our workshop covers a broad range of topics, such as complex longitudinal and survival data analysis, high-dimensional data analysis, precision medicine, and artificial intelligence/machine learning methods, among others. Our workshop will have 2 plenary sessions, 24 invited sessions, and a poster session.

American Statistical Association

The Bernoulli–IMS World Congress in Probability and Statistics is held every four years.

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

Algebra,    Geometry and Topology

Casa Matemática Oaxaca (CMO)

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

Courses and Events for Math Students and Early Career Researchers,    Geometry and Topology

Eurandom workshop centre, Department of Mathematics and Computer Science, TU Eindhoven

Topics around Gibbs point processes, such as Bose-Einstein condensation, cristallization, phase-transitions, Gibbs-non-Gibbs, etc.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Modern algorithms for data analysis require the use of advanced probabilistic methods to achieve desirable scalability and accuracy guarantees. At the same time, modern data-analysis tasks require the use of advanced statistics to handle challenges such as testing for multiple hypotheses or identifying dependencies among data points, such as in time series or graphs. Probabilistic methods are also at the core theoretical computer-science areas, such as sublinear algorithms and average-case analysis. To obtain efficient data-analysis algorithms, probabilistic methods require careful balancing of theoretical and practical aspects. This Dagstuhl Seminar brings together researchers interested in statistical and probabilistic methods to design and analyze scalable algorithms for discovering knowledge in large and rich datasets. We plan to cover the following topics, among others.

Information & Knowledge Management, Big Data Computing,    Scientific Computing and Data Science

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Non-Bayesian approaches appear to solve many problems that Bayesians once dreamt of solving using Bayesian methods. We thus believe that it is timely and important to rethink and redefine the promises and challenges of Bayesian approaches; and also to elucidate which Bayesian methods might prevail against their non-Bayesian competitors; and finally identify key application areas where Bayes can shine.

Simons Laufer Mathematical Sciences Institute (SLMath)

Networks, graph driven algorithms, and dynamics on graphs such as epidemics, random walks and centrality measures all play a major role, both in our daily lives as well as many scientific and engineering disciplines. This introductory workshop will bring together experts and junior researchers in combinatorics, probability, and statistics to share a broad vision of major challenges and objectives, with a primary focus on models of random graphs and their limits, network inference, dynamic processes on networks and algorithms and optimization on random structures.

Network models and random graphs, statistcal learning and network inference, counting and sampling discrete structures, dynamics on networks, probabilistic analysis of network algorithms

American Statistical Association

Institute of Mathematical Statistics

American Statistical Association (ASA)