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Institute for Mathematical and Statistical Innovation

Materials informatics is an emerging field defined by the use of simulation tools combined with methods from data sciences and machine learning to better understand materials properties and design innovative materials. The models which are considered cover an extremely wide range, from Schrödinger’s equation, which describes matter at the (sub)atomistic scale, to the equations of continuum mechanics. Mathematical sciences play a key role in materials informatics, both to construct the databases used to train machine learning algorithms (since these databases are made of reference simulation results), and to harness them in order to extract the most relevant information. The aim of this program is bring together a diverse scientific audience, both between scientific fields (physical sciences, materials sciences, biophysics, etc) and within mathematics (mathematical modeling, numerical analysis, statistics and data analysis, etc), to make progress on key questions of materials informatics.

Condensed Matter Physics and Materials,    Metallurgy and Materials Science

Don't miss the opportunity to join this exciting event and learn about the latest methodologies and trends in the field of data visualization! We look forward to seeing you at the 2nd Eurasian Data Visualization Conference!

Event manager;     Email: award@data2speak.com

Data, business analytics, AI, data science, business intelligence

Institute for Computational and Experimental Research in Mathematics (ICERM)

This workshop focuses on the role of random matrices in data science, machine learning, and theoretical computer science. The goal is to discuss a number of hot topics in these areas, focusing on recent theoretical progress and the potential pool of applications, in a manner that is accessible to a wide audience, including researchers in data science, electrical engineering, statistics, and numerical analysis.

Email: info@icerm.brown.edu

data science, machine learning, theoretical computer science, electrical engineering, statistics, and numerical analysis

Numerical Analysis and Computational Mathematics,    Applied Mathematics (in general)

Seqera

This premier event brings together leading experts, innovators, and researchers to showcase the latest breakthroughs in workflow management. There will be a hackathon and training sessions on May 21st and May 22nd and the Summit on May 23rd and May 24th.

Event organizers;     Email: summit@nextflow.io

nextflow, nf-core, pipeline, workflow, bioinformatics, data science

Genomics and Bioinformatics,    Clinical Data Management, e-Health

Society for Industrial and Applied Mathematics (SIAM)

This conference is co-located with the SIAM Conference on Mathematics of Planet Earth (MPE24). This is the conference of the SIAM Activity Group on Life Sciences. The life sciences have become increasingly quantitative as new technologies facilitate collection and analysis of vast amounts of data ranging from complete genomic sequences of organisms to satellite imagery of forest landscapes on continental scales. As a consequence, mathematics and computational science have become crucial technologies for the study of complex models of biological processes.

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)

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