Jaime Cernuda
Open for Collaborations

Jaime Cernuda

Research Assistant Professor specializing in high-performance computing infrastructure with expertise in distributed storage systems, real-time data processing, and exascale computing environments.

Developed novel approaches to real-time data management and adaptive resource management for scientific computing applications. Currently advancing the intersection of HPC and artificial intelligence through agentic system architectures to address complex challenges in scientific computing at the Gnosis Research Center. Proven track record of building HPC-scale middleware systems.

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Illinois TechChicago, IL
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10+

Papers Published

150+

Citations

2

Active Grants

5+

Years Researching

Featured Projects

IOWarp
AI/MLData ManagementHPC

IOWarp

Advanced data management platform for AI-augmented scientific workflows with $5M NSF funding

ChronoLog
Distributed SystemsLog StorageHPC

ChronoLog

A high-performance distributed shared tiered log store with time-based data ordering

Featured Research

Figure from HStream: A hierarchical data streaming engine for high-throughput scientific applications
ICPP'242024

HStream: A hierarchical data streaming engine for high-throughput scientific applications

Jaime Cernuda, Jie Ye, Anthony Kougkas, Xian-He Sun
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Selected Publications

CCGRID'242024

Hades: A Context-Aware Active Storage Framework for Accelerating Large-Scale Data Analysis

Jaime Cernuda, Luke Logan, Ana Gainaru, Scott Klasky, Jay Lofstead, Anthony Kougkas, Xian-He Sun
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Cite this work

@inproceedings{cernuda2024hades,
  title={Hades: A Context-Aware Active Storage Framework for Accelerating Large-Scale Data Analysis},
  author={Cernuda, Jaime and Logan, Luke and Gainaru, Ana and Klasky, Scott and Lofstead, Jay and Kougkas, Anthony and Sun, Xian-He},
  booktitle={The 24th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing},
  pages = {577--586},
  year={2024},
  doi={DOI 10.1109/CCGrid59990.2024.00070}
}
CLUSTER'212021

HFlow: A Dynamic and Elastic Multi-Layered Data Forwarder

Jaime Cernuda, Hariharan Devarajan, Luke Logan, Keith Bateman, Neeraj Rajesh, Jie Ye, Anthony Kougkas, Xian-He Sun
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Cite this work

@inproceedings{cernuda2021HFlow,
  title={HFlow: A Dynamic and Elastic Multi-Layered Data Forwarder},
  author={Cernuda, Jaime and Devarajan, Hariharan and Logan, Luke and Bateman, Keith and Rajesh, Neeraj and Ye, Jie and Kougkas, Anthony and Sun, Xian-He},
  booktitle={Proceedings of the IEEE Cluster Conference 2021},
  pages={114--124},
  year={2021}
}
MSST'202020

ChronoLog: A Distributed Shared Tiered Log Store with Time-based Data Ordering

Anthony Kougkas, Hariharan Devarajan, Keith Bateman, Jaime Cernuda, Neeraj Rajesh, Xian-He Sun
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Cite this work

@inproceedings{kougkas2020chronolog,
  title={ChronoLog: A Distributed Shared Tiered Log Store with Time-based Data Ordering},
  author={Kougkas, Anthony and Devarajan, Hariharan and Bateman, Keith and Cernuda, Jaime and Rajesh, Neeraj and Sun, Xian-He},
  booktitle={Proceedings of the 36th International Conference on Massive Storage Systems and Technology (MSST 2020)}
}

Latest News

2026
Acropolis, Agent Error Simulator, and Rich I/O-centric Derived Quantity Operators were accepted at the 22nd IEEE International Conference on eScience.
Pre-RoPE versus Post-RoPE, Agentic Search Efficiency, Speculative Dispatching, and Correct Is Not I/O Efficient were presented at the 13th Greater Chicago Area Systems Research Workshop.
Illinois Institute of Technology conferred the PhD in Computer Science.
Joined the Gnosis Research Center faculty as Assistant Research Professor and Head of Student Development.
2025
Successfully defended the doctoral dissertation at Illinois Tech.
Towards an AI-driven scientific workflow was presented at the 21st IEEE International Conference on eScience.
A data stack that unifies HPC, big data, and machine learning I/O for AI-driven workflows.
Characterizing the behavior and impact of KV caching on transformer inference under concurrency.
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Let's Collaborate

I am always looking for motivated PhD students and collaboration opportunities. Reach out if you are interested in pushing the boundaries of AI.