Conferences  >  Informatics  >  Scientific Computing and Data Science  >  United States

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Geographic Information System (GIS), Geoinformatics, Earth Observation,    Application Software

Beginning with the PEARC20 conference, the PEARC conference series is sponsored by the Association for Computing Machinery (ACM) and its HPC and Applications special interest groups (SIGAPP and SIGHPC).

The ACM Practice and Experience in Advanced Research Computing (PEARC) Conference Series is a community-driven effort that unites cyberinfrastructure and research computing practitioners, developers, and users by providing a forum for discussing ideas, findings, techniques, tools, and experiences. The PEARC Conference welcomes a broad range of participants and offers a diverse, equitable, and inclusive conference experience.

Society for Industrial and Applied Mathematics (SIAM)

Institute for Computational and Experimental Research in Mathematics (ICERM)

In recent years, new opportunities have emerged for pattern detection and identification in applications using data-scientific approaches. These applications include spiral waves in cardiac dynamics, vegetation patterns in arid ecosystems, and cell organization in biological tissues. The aim of this workshop is to bring together researchers at the interface of these diverse aspects of pattern-formation theory, computation, and applications in order to share ideas and identify new challenges and open problems. The scientific program will focus on the use of dynamical systems methods in the study of pattern formation, as well as the integration of dynamics, data, and computation.

ICERM;     Email: info@icerm.brown.edu

dynamical systems, data science, pattern formation

Simons Laufer Mathematical Sciences Institute (SLMath)

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.

combinatorial statistics, random graphs, network inference, network reconstruction, detection, estimation

Complex Networks,    Graph Theory and Combinatorics