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ALLE LÄNDER (9)
1
BIRS Workshop — Algorithmic Structures for Uncoordinated Communications and Statistical Inference in Exceedingly Large Spaces
10. Mär 2024 - 15. Mär 2024 • Banff, Alberta, Kanada
Veranstalter:
Banff International Research Station (BIRS) for Mathematical Innovation and Discovery
Eintrags-ID:
1559375
2
Analysis on fractals and networks, and applications
18. Mär 2024 - 22. Mär 2024 • Marseille , Frankreich
Veranstalter:
CIRM – Centre International de Rencontres Mathématiques
Zusammenfassung:
A clear emphasis will be put on theoretical and numerical methods oriented towards applications in engineering and the sciences. Experts from applied mathematics will report on potential industrial applications, prototypes, and – where possible – existing use cases.
Eintrags-ID:
1568621
3
BIRS Workshop — Analysis of Complex Data: Tensors, Networks and Dynamic Systems
12. Mai 2024 - 17. Mai 2024 • Banff, Alberta, Kanada
Veranstalter:
Banff International Research Station (BIRS) for Mathematical Innovation and Discovery
Eintrags-ID:
1559410
4
Workshop on Dynamical Processes on Complex Networks
13. Mai 2024 - 17. Mai 2024 • São Paulo, Brasilien
Veranstalter:
South American Institute for Fundamental Research (ICTP-SAIFR)
Zusammenfassung:
Complex systems are characterized by a large number of units, such as particles, individuals or neurons, that interact typically with a few neighbors but lead to the emergence of large-scale collective behavior. Examples include swarms of birds, the spreading of infectious diseases, the transmission of electric impulses by neurons, and the synchronization of fireflies at nightfall. Networks provide a natural representation of these systems, where nodes play the role of the units, and links between nodes indicate pairwise interactions. The distribution of links among the nodes is a key property of networks, defining how the units of the system interact. Links may follow simple rules, such as regular lattices or random connections, or may be highly heterogeneous, displaying power law distributions. More recently, the concepts of multilayer and higher-order networks have emerged to describe interconnected sets of networks and many-body interactions, where single-layer networks are generalized to simplicial complexes or hypergraphs. Two of these processes have become particularly important and will be the focus of this workshop in terms of applications.
Eintrags-ID:
1600596
5
Mathematical and Statistical Tools for High Dimensional Data on Compressive Networks
26. Mai 2024 - 31. Mai 2024 • Oaxaca, Mexiko
Veranstalter:
Casa Matemática Oaxaca (CMO)
Zusammenfassung:
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.
Eintrags-ID:
1576641
6
WAW 2024 — 19th Workshop on Modelling and Mining Networks
03. Jun 2024 - 07. Jun 2024 • SGH Warsaw School of Economics, Warsaw, Polen
Zusammenfassung:
Virtually every human-technology interaction, or sensor network, generates observations that are in some relation with each other. As a result, many data science problems can be viewed as a study of some properties of complex networks in which nodes represent the entities that are being studied and edges represent relations between these entities. Such networks are often large-scale, decentralized, and evolve dynamically over time. Modeling and mining complex networks in order to understand the principles governing the organization and the behaviour of such networks is crucial for a broad range of fields of study, including information and social sciences, economics, biology, and neuroscience. The aim of the 19th Workshop on Modelling and Mining Networks (WAW 2024) is to further the understanding of networks that arise in theoretical as well as applied domains. The goal is also to stimulate the development of high-performance and scalable algorithms that exploit these networks. The workshop welcomes the researchers who are working on graph-theoretic and algorithmic aspects of networks represented as graphs or hypergraphs and other higher order structures.
Eintrags-ID:
1589113
7
BIRS Workshop — Formation of Looping Networks - from Nature to Models
07. Jul 2024 - 12. Jul 2024 • Banff, Alberta, Kanada
Veranstalter:
Banff International Research Station (BIRS) for Mathematical Innovation and Discovery
Eintrags-ID:
1559555
8
BIRS Workshop — Causal Inference and Prediction for Network Data
18. Aug 2024 - 23. Aug 2024 • Banff, Alberta, Kanada
Veranstalter:
Banff International Research Station (BIRS) for Mathematical Innovation and Discovery
Eintrags-ID:
1559571
9
Detection, Estimation, and Reconstruction in Networks
21. Apr 2025 - 25. Apr 2025 • Berkeley, Kalifornien, Vereinigte Staaten
Veranstalter:
Simons Laufer Mathematical Sciences Institute (SLMath)
Zusammenfassung:
In a growing number of applications, one needs to analyze and interpret data coming from massive networks. The statistical problems arising from such applications lead to important mathematical challenges: building novel probabilistic models, understanding the possibilities and limitations for statistical detection and inference, designing efficient algorithms, and understanding the inherent limitations of fast algorithms. The workshop will bring together leading researchers in combinatorial statistics, machine learning, and random graphs in the hope of cross-fertilization of ideas.
Themen:
combinatorial statistics, random graphs, network inference, network reconstruction, detection, estimation
Eintrags-ID:
1571397


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