Conferences  >  Informatics  >  Scientific Computing and Data Science

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Neural Networks and Artificial Intelligence, Machine Learning,    Information & Knowledge Management, Big Data Computing

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

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.

High Performance Computing, Supercomputing, Parallel Computing,    Distributed, Edge and Cloud Computing

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Software Engineering,    Application Software

South American Institute for Fundamental Research (ICTP-SAIFR)

Courses and Events for Physics Students,    Neural Networks and Artificial Intelligence, Machine Learning

Institute of Mathematical Statistics (IMS)

The objective of ICSDS is to bring together researchers in statistics and data science from academia, industry, and government in a stimulating setting to exchange ideas on the developments of modern statistics, machine learning, and broadly defined theory, methods, and applications in data science. There will be a student paper award competition, in addition to plenary sessions, and invited, contributed and poster sessions. Young researchers are particularly encouraged to participate, as a portion of the invited sessions will be designated for young researchers.

Probability and Statistics, Game Theory,    Information & Knowledge Management, Big Data Computing

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

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

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

NEXT BUSINESS MEDIA

The Data Science Conference (DSC Next 2025) is a premier event set to take place from May 07 - 09, 2025, in Amsterdam. This conference brings together industry experts, data scientists, researchers, and professionals to explore the latest advancements in data science, machine learning, and artificial intelligence. It offers a platform for participants to network, share insights, and discuss the future of data-driven technologies.

EVENT MANAGER;     Phone: [1 702 551 9568];     Email: contact@nextbusinessmedia.com

European Molecular Biology Organization (EMBO)

Molecular Biology,    Systems Biology and Computational Biology

COLOURS is a 2-week event aimed at ensuing that early-career researchers (ECR) are prepared to make the most of Stage-IV cosmological survey data. The first week of the event (02/06/25-06/06/25) will be a 5-day summer school aimed at improving both technical (software development, machine learning, statistics) and soft skills (academic writing, transferable skills). Site visits and social activities will also be organised. The second week (10/06/25-13/06/25) will be a 4-day workshop with invited speakers. The aim will be to review key lessons learned in the analysis of Stage-III data and the scientific aspirations of on-going and upcoming Stage-IV surveys.

Organiser;     Email: samuel.farrens@cea.fr

Cosmology, machine learning, software development, academic writing, transferable skills

MIPRO Croatian Society

The DS-BE conference aims to showcase new research and advancements in computational methods, tools, and applications related to Data Science and Biomedical Engineering. We invite high-quality, original research papers highlighting theoretical and practical innovations in these areas, expecting interdisciplinary approaches promoting scalable solutions for real-world challenges. The conference focuses on hardware and software solutions designed to address specific domain-related challenges related to various domains in science and industry. Additionally, it encompasses broader research initiatives that provide generalizable approaches across data-driven disciplines.

Karolj Skala - conference chair;     Phone: [+38551423984];     Email: skala@irb.hr

Machine Learning and Deep Learning, Complex Networks: Structure, Behavior, and Dynamics, Knowledge Discovery and Interpretability, Data Science Methods, Tools and Platforms, Big Data Analysis and Visualization, Service Applications Based on Cloud, Fog, and/or Dew Computing, Interpretable and Explainable Machine Learning, Expert Systems and Knowledge-Based Systems, Knowledge Representation and Management, Natural Language Processing and Text Mining, Ethical AI, Data Science in Computational Biology and Medicine, Biological Network Analysis, Drug Target Identification and Validation, Medical and Biological Image Analysis, Analysis and Visualization of Biological and Clinical Data, Bioinformatics and Medical Applications, Advanced Bio-Signal Processing and Interpretation, Monitoring of Vital Functions with Sensor and ICT Systems, Wearable Sensors and Networks in Distributed Systems, Telehealth, Telecare, Telemonitoring, and Telediagnostics, Social Networking, Computing, and Education for Health, Internet of Things (IoT) for mHealth, Data Models and Representations for mHealth, Information Fusion of Biomedical Data, Biomedical Scientific Databases

Genomics and Bioinformatics,    Neural Networks and Artificial Intelligence, Machine Learning

European Molecular Biology Organization (EMBO)