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

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

Data for Policy CIC & Leiden University

Data for Policy envisions a future where data-driven technologies shape a more equitable and informed world. At the forefront of this vision is the concept of twin transitions—digital and green transformations—that form the heart of the 2025 conference theme, “Twin Transitions in Data and Policy for a Sustainable and Inclusive Future”. This theme emphasizes how data-driven strategies can address complex issues like environmental impact and social equity.

Community Manager;     Email: communications@dataforpolicy.org

Twin Transitions, Data Policy, Data Science, Artificial Intelligence, Digital Transition, Digital Inovation, AI Governance

Political science,    Neural Networks and Artificial Intelligence, Machine Learning

European Molecular Biology Organization (EMBO)