Suren Byna
Computer Science · The Ohio State University
Publications
228
Citations
3,328
Est. group size
~1
Recurring co-author estimate
Active years
24
Publishing since 2003
Suren Byna works on high-performance computing (HPC) systems, focusing on how large-scale scientific data is stored, moved, and managed efficiently on supercomputers. Recent work applies this expertise to AI-related challenges, including assessing whether datasets are 'ready' for machine learning, using large language models to automatically tune I/O (input/output) performance, and building trustworthy, privacy-preserving data pipelines. Much of the work centers on tools and libraries like HDF5, a widely used format for storing scientific data.
Publication output has fluctuated but remained fairly steady over the past decade, with a peak around 2021-2022, a dip in 2023, and a rebound in 2024, averaging about 14 papers per year over the last five years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- PMIO: Optimizing parallel collective I/O accesses through node-local persistent memory
Journal of Parallel and Distributed Computing · 2026
- Data Readiness for AI: A 360-Degree Survey
ACM Computing Surveys · 2025
- I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey
ACM Computing Surveys · 2025
- IOAgent: Democratizing Trustworthy HPC I/O Performance Diagnosis Capability via LLMs
2025
- CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
2025
- Securing HDF5 Plugins with Digital Signatures
2025
- Regen: An object layout regenerator on large-scale production HPC systems
Future Generation Computer Systems · 2025
- Streamlining HDF5’s AI Workloads Benchmarking
2025
- AIDRIN 2.0: A Framework to Assess Data Readiness for AI
arXiv (Cornell University) · 2025
- CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
arXiv (Cornell University) · 2025
- Data Management in the Continuum: Cross-facility Object-based Data Transfers
2025
- HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications
The International Journal of High Performance Computing Applications · 2024
- PROV-IO: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems
IEEE Transactions on Parallel and Distributed Systems · 2024
- h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre‐exascale platforms
Concurrency and Computation Practice and Experience · 2024
- AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
2024
- arXiv (Cornell University)×14
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)×12
- eScholarship (California Digital Library)×6
- SpringerBriefs in computer science×5
- Zenodo (CERN European Organization for Nuclear Research)×5
- Pouya Kousha
Computer Science · The Ohio State University
- Jiannan Tian
Computer Science · Indiana University
- Dingwen Tao
Computer Science · Indiana University
- Tom Peterka
Computer Science · The Ohio State University
- Sian Jin
Computer Science · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
Claim or correct this profile