Daniel Rosendo

Bio

Daniel Rosendo is a Research Scientist in the Workflows and Ecosystem Services group at the National Center for Computational Sciences (NCCS).

Daniel's work at Oak Ridge National Laboratory will focus on exploring workflow solutions for integrating advanced data science techniques across the Instrument-to-HPC Continuum at leadership-class scale.

His research interests include:

• Large-scale workflow management
• Workflow benchmarking
• AI-assisted workflows
• FAIR workflows

Experience

• Research Scientist, Oak Ridge National Laboratory - ORNL, USA (2024 - Present)
• Research Engineer, Inria, France (2023)
• Intern, Argonne National Laboratory - ANL, USA (2022)
• Teaching, INSA Rennes: Big Data Algorithms, France (2020 - 2021)
• Teaching, University Rennes 1: Cloud for Big Data, France (2019 - 2021)
• R&D Staff, Networking and Telecommunications Research Group, UFPE, Brazil (2014 - 2019)

Education

• Ph.D. in Computer Science, National Institute of Applied Sciences - INSA Rennes, France (2019-2023)
• Master’s in Computer Science, Federal University of Pernambuco, Brazil (2015-2017)
• Bachelor’s in Information Systems, University of Pernambuco, Brazil (2010-2014)

Research Projects

• Workflows Community - A thriving network of international workflow users, developers, and researchers (2025 - Present)
• Research Data Alliance - FAIR for Machine Learning (FAIR4ML) Interest Group (2025 - Present)
• Joint Laboratory for Extreme-Scale Computing (JLESC) - Advancing Chameleon and Grid'5000 testbeds II (2022 - 2023)
• HPC-BigData INRIA Project LAB - High Performance Computing and Big Data (2019 - 2023)

Software Contributions

• WfCommons - A framework for enabling Scientific Workflow Research and Development
• Flowcept - A runtime data integration system that captures and queries workflow provenance
• EnOSlib - A library to build experimental frameworks on multiple platforms
• E2Clab - A framework for enabling reproducible experimental research on large-scale testbeds
• ProvLight - A framework for enabling efficient provenance data capture on IoT/Edge

Program Committee Member

• SC WORKS 2023, 18th Workshop on Workflows in Support of Large-Scale Science
• ISPDC 2023, 22nd IEEE International Symposium on Parallel and Distributed Computing

Awards

• Accessit at the BDA PhD Thesis Award, BDA: 39th Data Management Conference – Principles, Technologies and Applications, 2023
• Honorable Mention Master Thesis Award, SBRC - Brazilian Symposium on Computer Networks and Distributed Systems, 2018

Selected Publications

1. Rosendo, Daniel, Alexandru Costan, Patrick Valduriez, and Gabriel Antoniu. Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review. In 2022 Journal of Parallel and Distributed Computing (JPDC), Elsevier.
2. Rosendo, Daniel, Pedro Silva, Matthieu Simonin, Alexandru Costan, and Gabriel Antoniu. E2Clab: Exploring the Computing Continuum through Repeatable, Replicable and Reproducible Edge-to-Cloud Experiments. In 2020 IEEE International Conference on Cluster Computing (CLUSTER).
3. Rosendo, Daniel, Kate Keahey, Alexandru Costan, Matthieu Simonin, Patrick Valduriez, and Gabriel Antoniu. KheOps: Cost-Effective Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments. In 2023 ACM Conference on Reproducibility and Replicability, ACM REP.
4. Rosendo, Daniel, Alexandru Costan, Gabriel Antoniu, Matthieu Simonin, Jean-Christophe Lombardo, Alexis Joly, and Patrick Valduriez. Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum. In 2021 IEEE International Conference on Cluster Computing (CLUSTER).
5. Rosendo, Daniel, Mattoso, Marta, Costan, Alexandru, Souza, Renan, Pina, Debora, Valduriez, Patrick and Antoniu, Gabriel. ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum. In 2023 IEEE International Conference on Cluster Computing (CLUSTER).

Complete list of publications.