Resume

Summary

Systems engineer and PhD candidate (Illinois Institute of Technology) building AI-driven infrastructure for high-performance storage and distributed systems. Rust-first, with hands-on work in reinforcement learning, eBPF tracing, ML-based telemetry, and multi-LLM platforms. Author of 10 papers and 2 posters; research collaborations with LLNL, LBNL, and Argonne.

Technical Skills

Languages: Rust (primary), Python, C, C++, Bash
Systems: Linux, eBPF/BPF, CephFS, HPC clusters, distributed storage, containers, systemd, GitLab CI/CD
ML/AI: PyTorch, reinforcement learning, neural networks, multi-LLM orchestration, CUDA (GPU training)

Experience

Graduate Research Assistant, Gnosis Research Center, Illinois Tech, Chicago

Jan 2021 - Present

Developing Jathakam, a unified AI platform for HPC storage optimization combining reinforcement learning, lightweight neural networks, and adaptive caching agents.
Built Eris, an RL gym for storage I/O optimization, plus burnme-rly, GPU-offloaded (CUDA) RL training infrastructure for Eris agents.
Developed Aneris, an adaptive data-importance characterizer for multi-tiered I/O systems using ML on live telemetry.
Built Apollo, an ML-assisted real-time storage resource observer deployed on HPC clusters for live resource management.
Contributed to ChronoLog (distributed tiered log store) and extensions to the Hermes multi-tier buffering system.

Graduate Research Intern, Lawrence Livermore National Laboratory

May - Aug 2025

Characterized I/O intents of traditional deep learning training applications using unsupervised learning.
Extended the DataCrumbs tracing infrastructure with PEARL-mode BPF probes to build per-request causal chains.

Graduate Research Intern, Argonne National Laboratory (ALCF)

May - Aug 2022

Worked on distributed storage systems and HPC I/O characterization of GPUs.

Graduate Research Intern, Lawrence Berkeley National Laboratory

May - Aug 2021

Characterized HDF5 application I/O behavior and developed feature-reduction methods using evolutionary algorithms on Darshan counters (SC '21 poster).

Graduate Research Intern, The HDF5 Group

May - Aug 2020

Worked on I/O characterization and optimization for scientific HDF5 workloads.

Projects

vloop-MOS: Multi-LLM research platform orchestrating ChatGPT, Claude, Gemini, and Perplexity, with observability and API design for production workflows.
vloop-coder: Autonomous development pipeline that turns ideas into GitLab issues, plans execution, and opens merge requests, covering prompt design through CI/CD.

Selected Publications

TunIO: An AI-powered Framework for Optimizing HPC I/O - IPDPS '24 (first author)
Aneris: An Adaptive Data Importance Characterizer for Multi-Tiered I/O Systems - HiPC '26 (first author)
DTIO: Data Stack for AI-driven Workflows - SSDBM '25
Apollo: An ML-assisted Real-Time Storage Resource Observer - HPDC '21 (first author)
Also: Viper (ICPP '24), LuxIO (HiPC '22), HFlow (CLUSTER '21), ChronoLog (MSST '20). Full list

Education

PhD, Computer Science, Illinois Institute of Technology (Advisors: Dr. X.-H. Sun, Dr. A. Kougkas), 2018 - Present
MS, Computer Science, Central Michigan University, Mount Pleasant, MI (GPA 3.78/4.0), 2015 - 2017
BTech, Computer Science and Engineering, Amrita Vishwa Vidyapeetham, India, 2010 - 2014