PNNL

Oceane Bel, a computer scientist, has been at Pacific Northwest National Laboratory (PNNL) since October 2020. Previously, she collaborated with PNNL on performance enhancement projects focused on data movement and placement on scientific networks and systems while studying for her PhD. Her research focuses on applying machine learning to systems, with a particular interest in system security and performance.

At PNNL, she has contributed to projects such as Geomancy, an automated performance enhancement system that optimizes file systems through data layout optimization. Her research leverages neural networks for automated performance enhancement through data placement optimization in distributed storage systems. She also developed WinnowML, a model-based optimized feature selection method for system modeling and has worked on cybersecurity projects such as “Co-simulation framework for network attack generation and monitoring” and “Cyber-attack sequences generation for electric power grid.” Her work also includes quantum network simulation exploration through “Simulators for quantum network modeling: A comprehensive review.” Bel’s work “Increased Interpretability for Model-Driven Deception” on increased interpretability for model-driven deception is also a notable contribution to cybersecurity research. She has presented her work at prestigious conferences like SC23 and SC24, where she actively participates as a reproducibility member and SC Workshop Committee member.

Oceane Bel’s research interests, in general, include network simulation, performance enhancement of networks and systems, artificial intelligence, and cybersecurity. Oceane Bel studied at the University of California, Santa Cruz, where she received her PhD in computer science, with a focus on dynamic performance enhancement of scientific networks and systems. From the same university, she received her MS in computer science, with a focus on computer security. Finally, she received her BS from the University of Southern California in computer engineering and computer science, as a recipient of the Honors in Multimedia Scholarship.

• C
• C++
• Computer networking
• Computer systems analysis
• Machine learning
• Python

• PhD in computer science and engineering, University of California, Santa Cruz
• MS in computer science, University of California, Santa Cruz
• BS in computer science and engineering, University of Southern California

2025

Bel, O. and Kiran, M. 2025 "Simulators for quantum network modeling: A comprehensive review.” Computer Networks 263: 111204. ISSN 1389-1286. https://doi.org/10.1016/j.comnet.2025.111204.

Abebe, W., J. Strube, L. Guo, N. R. Tallent, O. Bel, S. Spurgeon, C. Doty, and A. Jannesari. 2025. “SAM-I-Am: Semantic boosting for zero-shot atomic-scale electron micrograph segmentation.” Computational Materials Science 246: 113400. ISSN 0927-0256. https://doi.org/10.1016/j.commatsci.2024.113400.

2024

Bel, O., B. O. Mutlu, J. Manzano, C. Wright-Hamor, O. Subasi, and K. J. Barker. 2024. “Cookie-Jar: An Adaptive Re-configurable Framework for Wireless Network Infrastructures.” In Proceedings of the 21st ACM International Conference on Computing Frontiers (CF ‘24). Association for Computing Machinery, New York, NY, USA, 189–198. https://doi.org/10.1145/3649153.3649190.

Bel, O., J. Kim, W. J. Hofer, M. Maharjan, B. Hyder, S. Purohit, and S. Niddodi. 2024. “Co-Simulation Framework for Network Attack Generation and Monitoring.” IEEE Access 12: 142227–142240. https://doi.org/10.1109/ACCESS.2024.3468272.

Purohit, S., R. Mayur, O. M. Bel, A. S. Mendoza, B. K. Webb, and S. Donald. 2024. Efficient Hybrid Attack Graph Generation for Cyber-Physical System Resilience Experimentation. Pacific Northwest National Laboratory PNNL-36847. Richland, WA. https://doi.org/10.2172/2478018.

2023

Hofer, W. J., O. Bel, B. Hyder, M. Bruggeman, and T. Edgar. 2023. Increased Interpretability for Model-Driven Deception: MARS LDRD Project. Pacific Northwest National Laboratory PNNL-34963. Richland, WA. https://doi.org/10.2172/2203118.

Tallent, N., R. Friese, J. Suetterlein, J. Strube, M. Schram, T. Elsethagen, L. DeLaTorre, M. Halappanavar, K. J. Barker, K. K. van Dam, L. Pouchard, L. Fang, Z. Dong, S. Yoo, S. Jha, D. Long, O. Bel, I. Altintas, K. Marcus, and V. Vural. 2023. Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows. https://doi.org/10.2172/1970012

Subasi, O., O. Bel, J. Manzano, and K. Barker. 2023. “The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning.” https://arxiv.org/abs/2312.03120.

2022

Fan, X., J. P. Ogle, J. V. Cree, D. Wang, Y. Chen, E. S. Peterson, T. Fu, H. Ren, O. Bel, K. Barker, V. Kumar, and L. Wang. 2022. Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications. Pacific Northwest National Laboratory PNNL-33221. Richland, WA. https://doi.org/10.2172/1983947.

Dutta, A., S. Purohit, A. Bhattacharya, and O. Bel. “Cyber Attack Sequences Generation for Electric Power Grid.” 2022 10th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES), Milan, Italy, 2022, pp. 1–6. https://doi.org/10.1109/MSCPES55116.2022.9770105.

2021

Suetterlein, J., O. Bel, R. Friese, and B. Mutlu. 2021. Data-centric Abstractions and Adaptation to Enable Distributed Scientific Exploration.

Bel, O., S. Mukhopadhyay, N. Tallent, F. Nawab, and D. Long. “WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling.” 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 3031–3041. https://doi.org/10.1109/BigData52589.2021.9671602.

Bel, O., J. Pata, J. R. Vlimant, N. Tallent, J. Balcas, and M. Spiropulu. 2021. “Diolkos: improving ethernet throughput through dynamic port selection.” In Proceedings of the 18th ACM International Conference on Computing Frontiers (CF ‘21). Association for Computing Machinery, New York, NY, USA, 83–92. https://doi.org/10.1145/3457388.3458659.

2020

Bel, O., K. Chang, N. R. Tallent, D. Duellmann, E. L. Miller, F. Nawab, and D. D. E. Long. “Geomancy: Automated Performance Enhancement through Data Layout Optimization.” 2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), Boston, MA, USA, 2020, pp. 119–120, https://doi.org/10.1109/ISPASS48437.2020.00025.

2018

Bel, O., K. Chang, D. Bittman, D. D. E. Long, H. Isozaki, and E. L. Miller. 2018. “Inkpack: A Secure, Data-Exposure Resistant Storage System.” In Proceedings of the 11th ACM International Systems and Storage Conference (SYSTOR ‘18). Association for Computing Machinery, New York, NY, USA, 89–100. https://doi.org/10.1145/3211890.3211899.

2017

Li, Y., K. Chang, O. Bel, E. L. Miller, and D. D. E. Long. 2017. “CAPES: unsupervised storage performance tuning using neural network-based deep reinforcement learning.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC ‘17). Association for Computing Machinery, New York, NY, USA, Article 42, 1–14. https://doi.org/10.1145/3126908.3126951.

2015

Davis, R., E. Bumbacher, O. Bel, A. Sipitakiat, and P. Blikstein. 2015. “Sketching intentions: comparing different metaphors for programming robots.” In Proceedings of the 14th International Conference on Interaction Design and Children (IDC ‘15). Association for Computing Machinery, New York, NY, USA, 391–394. https://doi.org/10.1145/2771839.2771924.