Conferences  >  Informatics  >  Scientific Computing and Data Science

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

In many real-world situations, the input to an algorithm is revealed piece-meal over time, and algorithmic decisions must be taken without knowledge of future inputs. This has given rise to the field of online algorithms. Traditionally, the performance of an online algorithm is measured by comparing the quality of the solution produced by the algorithm to that of an offline optimal solution. This framework, called competitive analysis, has led to a beautiful theory of worst-case analysis for online algorithms over the last few decades. At the same time, it has also been painfully obvious that this model is overly pessimistic in many situations and leads to strong lower bounds that bucket together algorithms with significant differences in observed empirical performance. In recent years, there has been an increasing push to go beyond worst-case competitive analysis of online algorithms, and several models and frameworks have been proposed that have attracted much research activity. This Dagstuhl Seminar seeks to bring together the leaders in online algorithms research to discuss and debate these approaches, helping chart the future of online algorithms for the foreseeable future.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Voronoi diagrams are fundamental geometric structures that divide space into regions based on proximity to points or sites. They play an important role in many fields of science and have become cornerstones of disciplines such as computational geometry, scientific computing, and optimization.

Optimization Theory, Operational Research,    Modeling and Simulation

OECD Nuclear Energy Agency

The NJOY Nuclear Data Processing System is a modular computer software package designed to read evaluated data in the Evaluated Nuclear Data File (ENDF) format, transform the data in various ways and output the results as libraries designed to be used in various applications.

Nuclear Energy,    Nuclear Physics

This workshop, sponsored by AIM and the NSF, will bring together researchers from across the mathematical sciences to apply a broad range of mathematical, statistical, and data science tools to study the mathematical sciences community. The workshop will include both researchers currently working in this area, as well as researchers new to the field interested in applying their expertise to this area.

Developing MetaMath as an example of using mathematical, statistical, and computational methods to investigate the mathematical sciences community itself. Catalyzing research collaborations aimed at using data to answer questions about the mathematical sciences community. Exploring the variety of mathematical, statistical, and data science techniques that can be applied to gain insight into the systems and structure of the mathematical sciences community.

General Mathematical Research and Multidisciplinary Events,    Applied Mathematics (in general)

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Key questions for the seminar include identifying which language constructs are best suited for expressing mixed precision algorithms in software and determining the specific floating-point formats required. This could provide valuable feedback to hardware developers. Additionally, the seminar should address which tools can aid in and simplify the analysis of reduced precision computing, and what tools might be necessary to evaluate the benefits of these techniques. For instance, how can algorithm developers predict and quantify the energy savings achieved through the use of reduced precision?

Algorithms and Data Structures,    Modeling and Simulation

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Faithful data representation is key. While this is universally true for visual analysis, the field of biological data communication has its unique challenges and open questions. Researchers need the right context to interpret data accurately, especially when uncertainties arise from factors like sampling choices or analysis parameters. Simplifying data for wider audiences must still convey its complexity and uncertainty to support informed decisions. This Dagstuhl Seminar will bring together experts in data visualisation and design, researchers, public educators, and journalists to explore methods for embracing and communicating biodata complexity and uncertainty.

Multimedia, Computer Graphics and Visualization, Games,    Human-Computer Interaction, User Experience, Virtual Reality

Next Business Media

After a successful inaugural edition in 2025, the Data Science Conference (DSC Next 2026) is back for its 2nd edition, scheduled to take place from March 24–26, 2026, at NH Amsterdam Zuid, Netherlands. This landmark event continues to establish itself as one of Europe’s key platforms for connecting data science, machine learning, and AI professionals from academia and industry. With a broader agenda, expanded workshops, and more international participation expected, DSC Next 2026 promises to be even more impactful, offering three full days of insight, innovation, and interaction.

Diwakar Chandra Gour, Event Manager;     Phone: [+91 84483 67524];     Email: diwakar@datasciencenext.com

Data Ethics and Privacy, Big Data Analytics, Machine Learning and AI Applications, Data Visualization, Natural Language Processing, Predictive Analytics, AI in Healthcare, Finance, and Industry, Data Governance and Security

Neural Networks and Artificial Intelligence, Machine Learning,    Pattern Recognition and Image Processing, Machine Vision

The International Centre for Mathematical Sciences (ICMS)

In the last few years, an important trend has emerged for using data-driven image models, in particular encoded by neural networks. Novel families of hybrid imaging methodologies, mixing data-driven and traditional mathematical approaches (such as optimisation or sampling methods) have flourished. For instance, generative or discriminative networks such as GANS, VAEs or normalising flows, can be either used in optimisation or sampling schemes as data-driven regularisers for solving inverse problems. Similarly, denoising networks or more generally regularising networks can be incorporated into optimisation or sampling schemes leading to Plug-and-Play methods. From another perspective, unrolled optimisation approaches have been investigated to provide robust network architectures as alternative to traditional black-box end-to-end networks. All these approaches have shown a remarkable versatility and efficiency to solve inverse imaging problems.

Numerical Analysis and Computational Mathematics,    Neural Networks and Artificial Intelligence, Machine Learning

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

This workshop aims to bring together researchers from diverse fields of applied mathematics to explore the critical role of variational methods in both physical sciences and data sciences. Variational problems are fundamental in modeling natural phenomena and optimizing complex systems. This interdisciplinary workshop will focus on the latest advances in the analysis of physical problems, mathematical aspects of data science, optimization techniques, and computational mathematics, and at the same time celebrate the important contributions of Rustum Choksi at the occasion of his 60th birthday. By bringing together a diverse group of scholars, the workshop will also create a venue for new research directions, innovative solutions, and enhanced computational methods.

Modeling and Simulation,    Computational Physics and Numerical Simulation