01
AI for Systems
Transforming SMART logs, device telemetry, and runtime traces into actionable reasoning signals for storage reliability, failure analysis, and operational decision support.
Ph.D. Student · Research Assistant · NERSC Berkeley Lab Intern
AI, systems, HPC, and quantum AI for reliable scientific computing.
I am a Computer Science Ph.D. student at Florida International University and currently an intern at NERSC, Lawrence Berkeley National Laboratory. My research builds intelligent systems that learn from runtime signals, operational telemetry, and quantum workflow behavior to improve reliability, performance, energy efficiency, and robustness for scientific computing.
AI and ML
AI ⇄ Systems
HPC
Quantum AI
AI for Science
Research
Research themes
My research sits at the intersection of AI and systems. I focus on how intelligent models can reason over noisy operational data, how AI infrastructure can be made more reliable, and how quantum and classical workflows can be continued safely under real hardware constraints.
02
Agentic AI for HPC
Designing agent capabilities for HPC operations, scientific AI infrastructure, incident triage, workflow assistance, and infrastructure aware decision making at scale.
03
Quantum AI and QML
Studying quantum neural networks, variational algorithms, restart contracts, checkpointing, and noise aware training under realistic hardware behavior.
04
Systems for AI
Building reliable pipelines for training, inference, monitoring, profiling, and deployment across cloud, distributed, and data intensive AI environments.
Agentic AI for HPC operations at NERSC
I am currently working as an intern at NERSC, Lawrence Berkeley National Laboratory, on agentic AI for HPC operations and scientific AI infrastructure. The goal is to prototype intelligent agent capabilities that help operators and scientists reason over complex infrastructure signals, support AI for Science workloads, and improve operational efficiency.
From raw device signals to operational reasoning
My storage systems work explores how SMART attributes, knowledge graphs, and LLM based reasoning can explain SSD behavior, identify risk patterns, and convert low level device measurements into useful operational narratives.
Robust continuation for quantum workflows
My quantum systems work studies checkpointing, restart contracts, noise aware training, and optimization behavior for hybrid quantum classical workflows where hardware drift, queue delays, and measurement uncertainty affect reproducibility.
Publications
Selected publications and manuscripts
Filter the list by publication status. The selected list highlights accepted papers, manuscripts under review, and related preprints across systems, AI, storage, and quantum computing.
OSDI 2026Accepted
SMARTTalk: Teaching SMART Logs to Talk to LLMs
Mayur Akewar, Sandeep Madireddy, Dongsheng Luo, Janki Bhimani
SMART LogsLLMsStorage Reliability
IPDPS 2026Accepted
KORAL: Knowledge Graph Guided LLM Reasoning for SSD Operational Analysis
Mayur Akewar, Sandeep Madireddy, Dongsheng Luo, Janki Bhimani
Knowledge GraphsSSD AnalysisLLM Reasoning
arXiv
AISTATS 2026Accepted
WSBD: Freezing-Based Optimizer for Quantum Neural Networks
Christopher Kverne, Mayur Akewar, Yuqian Huo, Tirthak Patel, Janki Bhimani
Quantum MLOptimizationQNNs
arXiv
HotStorage 2025Published · Scholar citations 1
Can LLMs Model the Environmental Impact on SSD?
Mayur Akewar, Gang Quan, Sandeep Madireddy, Janki Bhimani
LLMsSSDEnvironmental Signals
DOI
HotStorage 2025Published · Scholar citations 2
Quantum Neural Networks Need Checkpointing
Christopher Kverne, Mayur Akewar, Yuqian Huo, Tirthak Patel, Janki Bhimani
CheckpointingQuantum SystemsQNNs
DOI
ICCAD 2025Published · Scholar citations 6
Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?
Yuqian Huo, Jinbiao Wei, Christopher Kverne, Mayur Akewar, Janki Bhimani, Tirthak Patel
Quantum ComputingTranspilationNoise
arXiv
IEEE GRSM 2024Published · Scholar citations 10
An Integration of Natural Language and Hyperspectral Imaging: A Review
Mayur Akewar, Manoj Chandak
Hyperspectral ImagingNLPSurvey
DOI
ATC 2026Under review
Catch-Q: Semantic Restart Contracts for Reliable Recovery of Hybrid Quantum-Classical Workflows
Mayur Akewar, Christopher Kverne, Yuqian Huo, Tirthak Patel, Janki Bhimani
CheckpointingQuantum-Classical WorkflowsSystems
SC 2026Under review
QuMIA: Identifying and Characterizing Membership Inference Attack Vulnerabilities in Quantum Machine Learning
Yuqian Huo, Jason Han, Mayur Akewar, Christopher Kverne, Janki Bhimani, Tirthak Patel
Quantum MLSecurityPrivacy
2026Manuscript from Scholar
VARIATIONAL QUANTUM ALGORITHMS ARE LIPSCHITZ SMOOTH
Christopher Kverne, Mayur Akewar, NS DiBrita, Yuqian Huo, Tirthak Patel, Janki Bhimani
VQAOptimizationQuantum Theory
arXiv 2026Scholar citations 3
CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMs
R Ranjan, U Grover, M Akewar, X Lin, A Polyzou
RAGFair LLMsDebiasing
TechRxiv 2023Scholar citations 34
Hyperspectral Imaging Algorithms and Applications: A Review
Mayur Akewar, Manoj Chandak
Hyperspectral ImagingSurveyRemote Sensing
DOI
TechRxiv 2023Scholar citations 7
Classification of EEG Signals Utilizing DWT for Feature Extraction and Evolutionary Algorithms for Feature Selection
Mayur Akewar
EEGFeature SelectionSignal Processing
DOI
2014Scholar citations 8
A study of effective load balancing approaches in cloud computing
Roshan Kotkondawar, Pushpjit Khaire, Mayur Akewar, Y. Patil
Cloud ComputingLoad BalancingSurvey
2012Scholar citations 9
Grid based wireless mobile sensor network deployment with obstacle adaptability
Mayur Akewar, Nileshsingh Thakur
Sensor NetworksDeploymentObstacles
2012Scholar citations 40
A study of wireless mobile sensor network deployment
Mayur Akewar, Nileshsingh Thakur
Sensor NetworksMobile SensorsDeployment
Projects
Research projects
Representative projects connecting AI, systems, storage, HPC operations, scientific AI infrastructure, and quantum computing.
AI for StorageOSDI 2026
SMARTTalk
Teaching SMART logs to communicate with LLMs for storage health interpretation, operational reasoning, and failure risk analysis.
Knowledge GraphsIPDPS 2026
KORAL
Knowledge graph guided LLM reasoning for SSD operational analysis, connecting device signals, failure evidence, and explainable decisions.
HPC OperationsNERSC
Agentic AI for HPC Operations
Current NERSC internship project on intelligent agent capabilities for HPC operations, scientific AI infrastructure, operational efficiency, and DOE AI for Science workloads.
Quantum SystemsFAST Track
Quantum Restart Contracts
Restart contracts for hybrid quantum classical workflows, including checkpoint semantics, migration decisions, and restore auditing.
Quantum MLAISTATS 2026
QNN Optimization
Optimizer and checkpointing methods for quantum neural networks under realistic noise, training instability, and hardware constraints.
Multimodal AIIndustry
Content Understanding Systems
Scalable pipelines for text, image, audio, and video analytics using deep learning, object detection, classification, and cloud deployment.
Background
Experience, education, and skills
Research Intern · NERSC, Lawrence Berkeley National Laboratory
Working on agentic AI for HPC operations and scientific AI infrastructure, with a focus on intelligent assistance for operational workflows and AI for Science workloads.
Research Assistant · Florida International University
Research on intelligent adaptive systems for classical and quantum computing with a focus on reliability, performance, energy efficiency, and robust workflow continuation.
Principal Development Engineer · V2 Solutions
Built multimodal deep learning pipelines for text, image, audio, and video analytics, including scalable training and deployment.
Senior Software Engineer · V2 Solutions
Developed automation, scraping, image processing, perceptual hashing, classifier training, and cloud deployed services.
Assistant Professor · Pune University, Sinhgad Institutes
Taught machine learning, operating systems, compiler design, web technologies, cloud computing, data structures, and discrete mathematics.
Education
- Ph.D. Computer Science, Florida International University, 2024 to Present · GPA 4.0
- M.Tech. Computer Science and Engineering, RTMNU Nagpur University, 2012
- B.E. Computer Technology, RTMNU Nagpur University, 2010
Skills
PythonC/C++SQLPyTorchTensorFlowLLMsStorage SystemsDistributed SystemsDockerKubernetesAWSAzureQiskitPennyLaneVQEQAOA
Awards and service
- Best Innovative Employee Award, V2 Solutions, 2023
- Best Instructor and Result Award, Sinhgad Institutes, 2017
- Reviewer, Springer Nature Computer Science
- Reviewer, Springer Nature Multimedia Tools and Applications
Contact
Let us connect about AI, systems, HPC, or quantum AI.
I am interested in research collaborations across intelligent systems, storage reliability, scientific AI infrastructure, and quantum computing workflows.