My research centers on understanding and predicting nonlinear plasma phenomena in high-power RF and pulsed systems. I work at the intersection of plasma physics, electromagnetics, and machine learning, addressing problems that are fundamental to the operation and reliability of advanced microwave, accelerator, and space systems.
🧠 Current Research Focus
• Ultrafast Photoemission-Induced Plasma Discharges
At the University of Michigan, I investigate the formation and evolution of low-temperature plasmas initiated by ultrafast laser photoemission under high electric fields. This work explores surface-plasma coupling, ionization mechanisms, and breakdown thresholds, with relevance to RF injector design, vacuum electronics, and advanced cathode engineering.
• Machine Learning for Multipactor Prediction
I am actively developing supervised and physics-informed machine learning models to predict multipactor susceptibility across complex RF environments. This work involves feature engineering from simulation data, model selection, and interpretability tools to understand model behavior and uncertainty. It bridges first-principles physics with data-driven inference to improve predictive capability and design safety margins.
Image Courtesy: CST - Computer Simulation Technology - CST - Computer Simulation Technology, simulated with the particle-in-cell (PIC) solver in CST STUDIO SUITE., CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=36448503
• Dual- and Multi-Frequency RF Breakdown
I study how complex RF waveforms — particularly dual-frequency and non-sinusoidal excitations — influence multipactor onset and discharge dynamics. These waveform-driven scenarios introduce rich temporal structures in the RF electric field, which significantly affect electron trajectories, phase synchronization, and secondary emission rates. My work explores how tailoring waveform parameters can either suppress or enhance multipactor susceptibility, offering new strategies for designing more robust high-power microwave components and accelerator structures.
• Secondary Electron Emission and Surface Engineering
Secondary electron yield (SEY) plays a central role in multipactor and RF breakdown. I work on both empirical modeling and experimental data analysis to characterize SEY across novel materials, including microporous and engineered surfaces. This informs material design for multipactor mitigation and supports the development of simulation tools.
🧪 Tools and Techniques
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Modeling Approaches: Monte Carlo simulation, drift-diffusion models, 1D/2D/3D PIC (Particle-in-Cell)
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Software Platforms: CST Particle Studio, COMSOL Multiphysics, XOOPIC, Warp, XPDP1
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Machine Learning Frameworks: PyTorch, TensorFlow, Scikit-learn (ML for physical modeling)
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Programming Languages: Python, MATLAB, C/C++
🌍 Applications
My research contributes to the design and reliability of advanced accelerator, RF and plasma systems across several domains:
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Improving performance and resilience of superconducting cavities, spaceborne RF Systems, TWTs, klystrons, and RF sources.
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Ultrafast Pulsed Power: Understanding nanosecond plasma dynamics for high-gradient cathode and emission systems.
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Machine Learning for Plasma Modeling: Accelerating susceptibility predictions and enhancing design workflows.
Multipacting Electron Avalanche