Author of the book: "Numerical Modeling of Charging-Up in Gas Electron Multipliers: A Genetic Programming Approach"

1. Please introduce yourself. What would you like your reader to know about you?

I am a senior academic and researcher specializing in computational physics, high-energy particle physics, and artificial intelligence applications in scientific modeling. I currently serve as a Doctor in the Department of Physics at Sultan Qaboos University (SQU) in Oman. Over a career spanning more than two decades, I have divided my time between university teaching, postgraduate supervision, and leading large-scale international research collaborations. Notably, since 2012, I have served as a Team Leader for the CERN Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC), which allows me to contribute to highly cited global discoveries while bridging the gap between advanced instrumentation and machine learning.

2. What is your inspiration/motivation?

My primary motivation lies at the intersection of physics and computer science. I have always been fascinated by how we can leverage intelligent computational methods—such as genetic programming, neural networks, and deep learning—to solve highly complex physical problems that traditional analytical methods struggle to untangle. Whether it is decoding subatomic particle collisions at CERN or designing next-generation radiation detectors, the ability to train algorithms to discover underlying physical laws or optimize complex instruments is what drives my research and teaching daily.

3. How long did it take to complete your book from the idea to publication?

The timeline from the initial conceptual stage to final publication spans several years of dedicated research, testing, and algorithmic refinement. While the formal writing and compilation of the manuscript with Eliva Press moved swiftly, the underlying foundation relies on an extensive trajectory of academic work. This includes years of modeling particle tracking systems, supervising advanced research on gas electron multipliers, and developing specialized genetic programming frameworks designed to handle complex simulation data.

4. What's the main message and idea of your book "Numerical Modeling of Charging-Up in Gas Electron Multipliers: A Genetic Programming Approach"?

The core idea of the book is to demonstrate how evolutionary computation—specifically genetic programming—can revolutionize the way we model intricate detector dynamics in high-energy physics. Gas Electron Multipliers (GEMs) are vital for modern particle tracking, but they suffer from "charging-up" effects that alter their gain and behavior over time. Standard numerical simulations can be incredibly computationally expensive. This book highlights a paradigm shift: using genetic programming to automatically discover efficient mathematical models and learning rules directly from experimental or simulation data, thereby accurately predicting detector behavior with far less computational overhead.

5. What was the most unexpected conclusion you came up with while preparing "Numerical Modeling of Charging-Up in Gas Electron Multipliers: A Genetic Programming Approach"?

The most striking conclusion was just how remarkably adaptable and robust genetic programming symbolic regression can be when applied to highly non-linear gaseous detector dynamics. We often expect AI models to act as "black boxes" (like deep neural networks), but genetic programming surprised us by yielding explicit, interpretable mathematical expressions that closely mirror the underlying physical behavior of gas mixtures and electron drift dynamics. It proved that evolutionary algorithms aren't just optimization shortcuts—they can actively assist physicists in uncovering underlying physical relationships.

6. How would you describe your publishing experience with Eliva Press in a few words?

An incredibly professional, efficient, and supportive publishing experience that smoothly bridges academic research with global reach.

7. How do you hope readers—especially researchers and students—will use your book in their work or studies?

For students, I hope this book serves as a clear, practical guide on how to merge physics with advanced computer science, showing them that programming languages and AI are fundamental tools for the modern scientist. For researchers and engineers working on the LHC upgrade or medical imaging instrumentation, I hope they use these evolutionary modeling techniques to optimize their own detector designs, predict gain stabilization times, and accelerate their simulation pipelines without sacrificing accuracy.

8. What future research topics or projects are you planning to explore next?

Moving forward, I am deeply focused on expanding our AI frameworks to address the upcoming high-luminosity phases of major collider experiments. My current and future projects involve using deep learning and machine learning to model the physical properties of complex gas mixtures, optimize ionization detector performance, and classify microseismic or cosmological gamma-ray bursts. Furthermore, I plan to continue developing smart microcontroller and FPGA-based instrumentation to deploy these AI models directly onto hardware for real-time particle detection.

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