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1
Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling
11. Jun 2023 - 17. Jun 2026 • Oberwolfach, Deutschland
Veranstalter:
Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)
Eintrags-ID:
1494926
2
Fast and Efficient Python Computing School
19. Aug 2024 - 23. Aug 2024 • Aachen, Deutschland
Veranstalter:
This is a School organized by the ErUM-Data-Hub with support from DIG-UM.
Zusammenfassung:
In this school you will learn how Python code can be accelerated. A focus will be placed on numeric NumPy-like array computations. In addition, running these array computations on hardware accelerators, i.e., GPUs, will play a key role in this school. The school is intended for young researchers - especially for PhD students - who regularly work with the scientific Python ecosystem. Requirements are good knowledge of the scientific Python ecosystem, basics of the C++ programming language are beneficial.
Eintrags-ID:
1613617
3
Developers Workshop PyHEP.dev
26. Aug 2024 - 30. Aug 2024 • Aachen, Deutschland
Veranstalter:
HEP Software Foundation (HSF)
Zusammenfassung:
PyHEP.dev is an in-person, informal workshop for developers of Python software in HEP to plan a coherent roadmap and make priorities for the upcoming year. It complements the PyHEP Users online workshop, which is intended for both developers and physicists.
Einsendeschluss für Abstracts:
25. Jun 2024
Eintrags-ID:
1613585
4
Dagstuhl-Seminar — Statistical and Probabilistic Methods in Algorithmic Data Analysis
22. Sep 2024 - 27. Sep 2024 • Schloss Dagstuhl, Wadern, Deutschland
Veranstalter:
Schloss Dagstuhl - Leibniz-Zentrum für Informatik
Zusammenfassung:
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.
Eintrags-ID:
1589493
5
Massive Data Models and Computational Geometry
23. Sep 2024 - 27. Sep 2024 • Bonn, Deutschland
Veranstalter:
The Hausdorff Research Institute for Mathematics (HIM)
Zusammenfassung:
The last decade has seen an explosion of available data. To analyze the enormous amount of accrued data, classical algorithms are often not appropriate: the data may not be accessible in its entirety (but perhaps only in an aggregate form) and computations may need to be distributed or done in an online setting. The data often comes with an additional geometric flavor, either from applications or from mapping the data to feature spaces. The workshop will focus on three classes of algorithms for dealing with massive geometric data, namely, streaming, distributed, and sublinear algorithms. The aim of the workshop to identify and explore new research directions at the interface of massive data models and computational geometry, through fruitful discussions leading to potential collaborations.
Eintrags-ID:
1623253
Verwandte Fachgebiete:
6
Dagstuhl Research Meeting — A long-term strategy for NFDI for DataScience and Artificial Intelligence
28. Okt 2024 - 29. Okt 2024 • Schloss Dagstuhl, Wadern, Deutschland
Veranstalter:
Schloss Dagstuhl - Leibniz-Zentrum für Informatik
Zusammenfassung:
The vision of NFDI4DS is to support all steps of the complex and interdisciplinary research data lifecycle in Data Science and Artificial Intelligence. The overarching objective of NFDI4DS is the development, establishment, and sustainment of a national research data infrastructure for the Data Science and Artificial Intelligence community. The key idea is to work towards increasing the transparency, reproducibility and fairness of Data Science and Artificial Intelligence projects, by making all digital artifacts available, interlinking them, and offering innovative tools and services. Within this Dagstuhl Research Meeting, the NFDI4DS partners will reflect on their progress so far, and will work on their long-term strategy.
Eintrags-ID:
1566284
7
Dagstuhl-Seminar — Graph Algorithms: Distributed Meets Dynamic
17. Nov 2024 - 22. Nov 2024 • Schloss Dagstuhl, Wadern, Deutschland
Veranstalter:
Schloss Dagstuhl - Leibniz-Zentrum für Informatik
Zusammenfassung:
In modern computational systems, the need to handle large-scale inputs imposes interesting computational challenges. Two such challenges are (1) the need to distribute the computation over multiple units, and (2) the dynamic nature of the input, which may undergo changes over time. A particular class of problems studied in these settings is when the input to the computational task is a huge graph. The field of dynamic graph algorithms addresses efficiently processing edge/vertex insertions/deletions in the input graph. In distributed graph algorithms, the input resides across multiple machines, and the goal is to solve the problem while minimizing the number of rounds of communication. Both of these rich research areas have been extensively studied since at least the 1980’s. We know of efficient algorithms for a large variety of tasks, such as shortest paths problems, coloring, subgraph finding, symmetry breaking, approximations, and many more. However, there are still fundamental problems with no known efficient solutions in some of these models, and even more where the exact complexity of computation is yet to be determined. In the recent years, a number of influential works show how transferring ideas from one of these models to the other provides progress on some of the long-lasting open problems. The goal of this Dagstuhl Seminar is to build a bridge between the two research communities of dynamic graph algorithms and distributed computing, by working together on joint research frontiers.
Eintrags-ID:
1589390
8
Data Assimilation: From Mathematical and Statistical Foundations to Applications
23. Feb 2025 - 28. Feb 2025 • Oberwolfach, Deutschland
Themen:
Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)
Eintrags-ID:
1605198
Verwandte Fachgebiete:


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