Meetings/Workshops on Probability and Statistics, Game Theory in Canada

The Banff International Research Station will host the "Statistical Modeling for Large Complex Time Dependent Systems" workshop in the year 2020. This workshop will focus on recent developments and open problems involving the techniques of dimension reduction, dynamic networks, multi-scale modeling for nonlinear processes and count processes, and machine learning, with applications to fields such as climatology, disease mapping, social media behavior, ecology, measurement of small populations, and neuroscience.

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

What could be a bigger source of "big data" than the entire Universe? Researchers in statistics and computer science are developing new methods to analyse the "big data" that companies and governments want to understand. To test out these methods they need big datasets that everyone can share - and new astronomical telescopes just happen to be gearing up to generate observations of billions of stars and galaxies that fit the bill. Insights from statistics will help astrophysicists to get the most out of these observations. The Astrostatistics 2021 workshop will bring together statisticians and astrophysicists to learn the technical languages of the two fields and identify how to work together to combine the excitement of space with big data.

Courses and Events for Math Students and Early Career Researchers,    Astronomy, Astrophysics and Cosmology

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

The workshop will bring together some of the world's top experts and promising young researchers working at the interface of Stochastics and Geometry, broadly understood. Through expository talks and research presentations, the workshop will give a vibrant overview of the most recent developments in the interaction between analysis, geometry and stochastic analysis. The focus of the workshop will be different approaches to random processes in geometric settings, with emphasis on problems on heat kernel analysis on finite- and infinite-dimensional manifolds, sometimes equipped with degenerate geometries, as well as random matrices and non-commutative probability and geometric rough paths. Another objective of the workshop is to identify fundamental open problems in this area that are motivated by mathematical physics and other potential applications. We plan to maintain geographic diversity and also to invite some of the most promising junior mathematicians.

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

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

Particle Physicists study matter at is smallest scale and try to reveal how the physical world is constructed from elementary particles and the forces acting between them. The well-known proton, neutron and similar particles are composed of quarks. Atoms also contain electrons; together with the two heavier versions of the electron (the muon and tau), each with its associated neutrino, they make up the group of leptons. Another ingredient of the catalogue of particles is the Higgs boson, discovered at the CERN Lab's Large Hadron Collider (LHC) in 2012.

High Energy Physics, Particles and Fields,    Scientific Computing

Department of Mathematics & Statistics and the Department of Supply Chain & Business Technology Management of Concordia University in Montreal

In keeping with the long tradition of previous decennial conferences (starting in 1971), this convention is dedicated to all areas of statistical sciences. In addition to the traditional theoretical/applied areas, interdisciplinary research would be encouraged and promoted including major theme areas Applied Statistics, Big Data, Bioinformatics, Biostatistics, Computational Statistics, Data Mining, Data Science, Demography, Econometrics, Health Statistics, Model Selection and Validation, Social Statistics, Supply Chain, Survival Analysis, Stochastic Methods in Ecology, Finance and Engineering.

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

The systematic study of random graphs commenced with the seminal work of Erd" os and Renyi in the 1960s. Beyond the classical connections and synergies between random graphs and computer science, over the last few years a new exciting connection between random graphs and statistical inference has been discovered. These connections have led to important new insights on computational problems and statistical inference problems and to new methods for the analysis of random graphs or random discrete structures in general. In addition, since the late 1990s an insightful approach called the cavity method by physicists has been applied to put forward predictions on structural properties of random graphs and their phase transitions. More recently, it has inspired new algorithms for statistical inference problems such as message passing algorithms and novel spectral algorithms. With this workshop we hope to stimulate research and foster new exciting interdisciplinary collaborations between researchers with different backgrounds and expertise in random graphs, computer science, probability, statistics, statistical inference and information theory.

Courses and Events for Math Students and Early Career Researchers,    Graph Theory and Combinatorics

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

Advances in data collection and social media have led to network and graph data becoming available in many areas, including social sciences, biological sciences, and engineering. Understanding and modeling network structure, as well as conducting rigorous statistical inference to assess uncertainty, can provide crucial insights into the dynamics and interaction mechanisms of the system. Statistical network analysis to date has largely focused on the setting where a single network is observed as a noisy version of some underlying structure of interest. This setting in itself is challenging, requiring adaptation of existing statistical frameworks to networks and bridging the gap between theoretically optimal performance and computational feasibility. The workshop will start by covering recent advances in this setting.

Courses and Events for Math Students and Early Career Researchers,    Graph Theory and Combinatorics

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

Nonlinear inverse problems are ubiquitous in medical sciences, engineering, physics and other natural sciences. This workshop will advance a statistical paradigm that allows underpinning statistical decision making in such problems in a scientifically rigorous way. This will be achieved by providing `frequentist' large sample guarantees for inferences arising from commonly used (Bayesian or non-Bayesian) algorithms in such problems. Thereby, a large cluster of applied problems, where algorithms are used for day-to-day decision making with statistical data, will be put on a solid and robust foundation, facilitating its confident use in modern society.

Courses and Events for Math Students and Early Career Researchers,    Algebra

American Statistical Association