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Ph.D. Candidate in Computer Engineering · University of Alabama in Huntsville
Quantum Computing · Quantum Machine Learning · Federated Learning · Trustworthy AI
I develop reliable learning methods for noisy and heterogeneous systems, with a focus on quantum machine learning, quantum error mitigation, quantum federated learning, distributed AI, and real quantum hardware.
I am seeking Research Scientist, Research Engineer, and academic opportunities beginning January 2027.
Research Areas
Noise-Resilient Quantum Machine Learning
Quantum neural networks, QCNNs, quantum kernels, barren plateaus, noise-aware learning, and robust NISQ training.
Quantum Error Mitigation & Hardware-Aware Learning
Adaptive zero-noise extrapolation, readout mitigation, dynamical decoupling, Bayesian methods, and real-QPU experimentation.
Quantum Federated & Distributed Learning
Personalization, heterogeneity, privacy, sporadic participation, multimodal QFL, distributed quantum sensing, and SimQFL.
Trustworthy AI & Federated Learning
Personalized and probabilistic FL, multimodal learning, structured reasoning, LLM-related learning, uncertainty, and distributed AI.
Featured Research & Software
CMAB-ZNE — Adaptive Zero-Noise Extrapolation on Real Quantum Hardware
Rigetti Cepheus-1-108Q · Amazon Braket · 240 real-QPU tasks
A contextual-bandit framework that adaptively selects ZNE noise-scaling strategies using accuracy, uncertainty, and execution-cost signals. The framework was evaluated through large-scale simulator studies and real-hardware quantum inference experiments.
SimQFL — Quantum Federated Learning Simulator
A client-server simulation platform for heterogeneous and personalized quantum federated learning with QPU-level noise modeling, configurable client participation, and real-time visualization.
QuNoise — Quantum Noise Simulator & Visual Analytics Platform
A research platform for studying depolarizing, amplitude-damping, phase-damping, and readout noise together with ZNE, probabilistic error cancellation, measurement-error mitigation, and Clifford data regression.
QuSenseSim — Variational Quantum Sensing Simulator
An interactive multi-sensor quantum sensing framework with heterogeneous noise, configurable mitigation, Fisher Information analysis, and Cramér–Rao Bound evaluation.
Scalable Quantum-Kernel Malware Classification @ KAUST
Developed a hybrid quantum-classical learning pipeline for malware-family classification on 18,836 PE samples across 23 labels, with scalable quantum-kernel approximation and real-device validation on a Rigetti superconducting QPU through Amazon Braket.
Selected Publications
- NeurIPS 2026 — Rethinking Structured Generation: Can Graph-Based Reasoning Resolve Ambiguity?
- IEEE JSAC 2026 — Quantum Noise Mitigation with Adaptive Zero-Noise Extrapolation: A Contextual Multi-Armed Bandits Approach.
- IEEE Transactions on Computers 2026 — Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach.
- IEEE TNNLS 2026 — Quantum Convolutional Neural Networks: A Survey on Architectures, Applications, and Future Directions.
- IEEE QCE 2026 — Toward High-Precision Variational Quantum Sensing via Asynchronous Federated Learning.
- IEEE QCE 2025 — SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization.
News
- Sep 2026 — Submitted Layer-Resolved Noise Degradation and Adaptive Dynamical Decoupling in Quantum Neural Networks to Physical Review A.
- 2026 — Rethinking Structured Generation: Can Graph-Based Reasoning Resolve Ambiguity? accepted at NeurIPS 2026.
- 2026 — Two papers accepted at IEEE QCE 2026 on variational quantum sensing and privacy-preserving distributed quantum sensing.
- 2026 — Adaptive ZNE work published in IEEE Journal on Selected Areas in Communications.
- Jan–Jun 2026 — Quantum Security Research Intern at KAUST.
- 2026 — Awarded the Alabama EPSCoR Graduate Research Scholars Program (GRSP) Fellowship.
Selected Awards & Recognition
- Alabama EPSCoR GRSP Round 21 Fellowship, 2026–2027
- Mary Makima and Lester Ross Scholarship, University of Alabama in Huntsville, 2026
- Oral Presentation, CVPR Workshop, 2025
- Top-30 Paper Award, Von Braun Space Exploration Symposium, 2023
View all awards & recognition →
About
I am a Ph.D. candidate in Computer Engineering at the University of Alabama in Huntsville and a Graduate Research Assistant in the Network Intelligence and Security Laboratory. My work spans quantum computing, quantum machine learning, federated learning, trustworthy AI, and distributed learning systems. I am particularly interested in making quantum learning more reliable under noise, device heterogeneity, finite-shot measurement, and real-hardware constraints.