Daniel Rosendo
IEEE and ACM Member
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
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
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
Communities
- Autonomous Science Network — Interconnected autonomous science laboratories for accelerated discovery
- Workflows Community — Network of international workflow users, developers, and researchers
- Research Data Alliance – FAIR4ML — FAIR for Machine Learning Interest Group
Research Projects
- OPAL - Orchestrated Platform for Autonomous Laboratories — Agentic AI for the Advanced Plant Phenotyping Laboratory (2025 – Present)
- Genesis Mission and The American Science Cloud (AmSC) — Intelligent Interfaces and Core Agentic Framework (2025 – Present)
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
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
Awards
- Supplemental Performance Award (SPA) for "Extraordinary Accomplishment" (division-wide impact) , Agentic AI for Additive Manufacturing, Oak Ridge National Laboratory, 2025. Daniel got the award within the first year of service at ORNL. The purpose of the SPA is to encourage staff members to exceed expectations, rise above unforeseen challenges, and go the extra mile to advance important discoveries.
- Distinguished Paper The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science, SC25 WORKS, 2025
- Best Short Paper Award in "Systems and System Software" Track: Secure API-Driven Research Automation to Accelerate Scientific Discovery. PEARC Conference, 2025
- Honorable Mention PhD Thesis Award , BDA 2023
- Honorable Mention Master Thesis Award , SBRC 2018
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.