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Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and…

Machine Learning (ML) has become ubiquitous, fueling data-driven applications across various organizations. Contrary to the traditional perception of ML in research, ML workflows can be complex, resource-intensive, and time-consuming.…

Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems…

Machine learning (ML) continues to permeate all layers of academia, industry and society. Despite its successes, mental frameworks to capture and represent machine learning workflows in a consistent and coherent manner are lacking. For…

Scientific workflows have become essential for orchestrating complex computational processes across distributed resources, managing large datasets, and ensuring reproducibility in modern research. The Workflows Community Summit 2025, held…

[Context] Machine learning (ML)-enabled systems are present in our society, driving significant digital transformations. The dynamic nature of ML development, characterized by experimental cycles and rapid changes in data, poses challenges…

The field of machine learning (ML) has gained widespread adoption, leading to significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial. The predominant approaches towards the automation of solving ML…

Many large language models (LLMs) for medicine have largely been evaluated on short texts, and their ability to handle longer sequences such as a complete electronic health record (EHR) has not been systematically explored. Assessing these…

A common workflow in science and engineering is to (i) setup and deploy large experiments with tasks comprising an application and multiple parameter values; (ii) generate intermediate results; (iii) analyze them; and (iv) reprioritize the…

The ever higher complexity of manufacturing systems, continually shortening life cycles of products and their increasing variety, as well as the unstable market situation of the recent years require introducing grater flexibility and…

The increasing integration of machine learning across various domains has underscored the necessity for accessible systems that non-experts can utilize effectively. To address this need, the field of automated machine learning (AutoML) has…

Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science…

High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective alternative, but their limited capacity requires biomedical…

Memory-to-memory data streaming is essential for modern scientific workflows that require near real-time data analysis, experimental steering, and informed decision-making during experiment execution. It eliminates the latency bottlenecks…

End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing…

Flowcharts are typically presented as images, driving the trend of using vision-language models (VLMs) for end-to-end flowchart understanding. However, two key challenges arise: (i) Limited controllability--users have minimal influence over…

Molecular dynamics (MD) simulations are widely used to study large-scale molecular systems. HPC systems are ideal platforms to run these studies, however, reaching the necessary simulation timescale to detect rare processes is challenging,…

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods…

Replicability in machine learning (ML) research is increasingly concerning due to the utilization of complex non-deterministic algorithms and the dependence on numerous hyper-parameter choices, such as model architecture and training…

Exascale computers offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. However, these software combinations and…