Neeraj Rajesh is a PhD Candidate in the Department of Computer Science at the Illinois Institute of Technology and a member of the Gnosis Research Center. His research focuses on I/O optimization and scientific data management, developing systems like TunIO that use reinforcement learning to tune HPC I/O stacks. Neeraj works under the guidance of Dr. Xian-He Sun and Dr. Anthony Kougkas.
Joined: Aug 2019Expected: May 2027
14Publications
8Projects
Research Interests
- Parallel and Distributed Systems
- HPC
- ML-Assisted Systems
- AI for I/O Optimization
Projects
- ChronoLog (2020–present)
- Coeus (2022–2026)
- DeepIO (2023–present)
- DTIO (2023–2026)
- Hermes (2020–2025)
- IOWarp (2024–present)
- IRIS (2019–2023)
- LABIOS (2025–present)
Publications
2026
Extending I/O Observability to Explainability for Targeted Optimization
2026
DataCrumbs: Efficient Cross-Layer Data Capture for Explainable I/O Performance in HPC Storage Stacks
2026
Applications do I/O. What if files actually went... somewhere else?
2025
DTIO: Data Stack for AI-driven Workflows
2025
A Unified Data Stack for AI Workflows
2025
Eris: A Trace-Driven Gym for AI-Powered Cache Optimization in HPC
2024
Viper: A High-Performance I/O Framework for Transparently Updating, Storing, and Transferring Deep Neural Network Models
2024
TunIO: An AI-powered Framework for Optimizing HPC I/O
2022
LuxIO: Intelligent Resource Provisioning and Auto-Configuration for Storage Services
2021
Feature Reduction of Darshan Counters Using Evolutionary Algorithms
2021
HFlow: A Dynamic and Elastic Multi-Layered Data Forwarder
2021
Apollo: An ML-assisted Real-Time Storage Resource Observer
2020
Characterizing and Approximating I/O Behavior of HDF5 Applications
2020
