Daniel Rosendo
Research Scientist, Workflows & Ecosystem Services Group
Oak Ridge National Laboratory · National Center for Computational Sciences (NCCS), USA
Exploring Agentic workflows for accelerating scientific discovery.
Bio
Daniel Rosendo is a Research Scientist in the Workflows and Ecosystem Services group at the National Center for Computational Sciences (NCCS) . His work at Oak Ridge National Laboratory focuses on exploring workflow solutions for integrating advanced data science techniques across the Instrument-to-HPC continuum at leadership-class scale.
Research interests
Experience
- Research Scientist, Oak Ridge National Laboratory (USA), 2024 – Present
- Research Engineer, Inria (France), 2023
- Intern, Argonne National Laboratory (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, 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
Program Committees
- SC WORKS 2023 — 18th Workshop on Workflows in Support of Large-Scale Science
- ISPDC 2023 — 22nd IEEE International Symposium on Parallel and Distributed Computing
Research Projects
- Autonomous Science Network — Interconnected autonomous science laboratories for accelerated discovery (2025 – Present)
- Workflows Community — Network of international workflow users, developers, and researchers (2025 – Present)
- Research Data Alliance – FAIR4ML — FAIR for Machine Learning 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
- Academy — Build and deploy stateful agents across federated resources
- WfCommons — Framework for enabling scientific workflow research and development
- Flowcept — Runtime data integration system for capturing and querying workflow provenance
- EnOSlib — Library to build experimental frameworks on multiple platforms
- E2Clab — Framework for reproducible experimental research on large-scale testbeds
- ProvLight — Efficient provenance data capture on IoT/Edge
Awards
Selected Publications
- Daniel Rosendo, et al. AI Agents for Enabling Autonomous Experiments at ORNL's HPC and Manufacturing User Facilities . XLOOP, SC25.
- Woong Shin, Renan Souza, Daniel Rosendo, Frédéric Suter, Feiyi Wang, Prasanna Balaprakash, Rafael Ferreira da Silva. The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science . WORKS, SC25.
- Daniel Rosendo, Alexandru Costan, Patrick Valduriez, and Gabriel Antoniu. Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review . Journal of Parallel and Distributed Computing (JPDC), 2022.
- Daniel Rosendo, Pedro Silva, Matthieu Simonin, Alexandru Costan, and Gabriel Antoniu. E2Clab: Exploring the Computing Continuum through Repeatable, Replicable and Reproducible Edge-to-Cloud Experiments . IEEE Cluster, 2020.
- Daniel Rosendo, Kate Keahey, Alexandru Costan, Matthieu Simonin, Patrick Valduriez, and Gabriel Antoniu. KheOps: Cost-Effective Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments . ACM Conference on Reproducibility and Replicability (REP), 2023.