Machine Learning (ML) has become essential in several industries. In Computational
Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large …

Abstract Machine learning (ML) has already fundamentally changed several businesses.
More recently, it has also been profoundly impacting the computational science and …

We present an algorithm and a software architecture for a cloud-based system that executes
cyclic scientific workflows whose structure may change during run time. Existing approaches …

Provenance determines the origin of the data by tracing and recording the actions that are
performed on the data. Therefore, provenance is used in many fields to ensure the reliability …

In long-lasting scientific workflow executions in HPC machines, computational scientists (the
users in this work) often need to fine-tune several workflow parameters. These tunings are …

Due to the exploratory nature of scientific experiments, computational scientists need to
steer dataflows running on High-Performance Computing (HPC) machines by tuning …

Complex scientific experiments from various domains are typically modeled as workflows
and executed on large-scale machines using a Parallel Workflow Management System …

The development lifecycle of Deep Learning (DL) models requires humans (the model
trainers) to analyze and steer the training evolution. They analyze intermediate data, fine …

There are two main avenues to design space exploration. In the first approach, a simulation
is run, analyzed, the problem modified, and the simulation run again. In the second …

The proliferation of big data pipelines has spurred collaborative efforts across multiple
disciplines to explore the intricacies of those domains. One notable collaboration involves …