Dr. Ehsan Adibnia, Sistan and Baluchestan University, Iran
Dr. Ehsan Adibnia is an innovative electrical engineering researcher with a deep specialization in artificial intelligence 🤖, nanophotonics 🔬, and optical communication systems 📡. He bridges machine learning with photonic design, using computational intelligence to advance optical switching, biosensing, and integrated photonic structures. Dr. Adibnia’s work stands out for applying hybrid deep learning frameworks to design high-performance optical devices. Proficient in Python, MATLAB, and specialized simulation platforms like COMSOL and Lumerical, he contributes to the development of next-generation photonic systems with a practical approach rooted in academic rigor. His scholarly output and active engagement in the scientific community reflect his dedication to research, innovation, and problem-solving.
Profile
Orcid
🎓 Education
Dr. Adibnia completed his Bachelor’s degree in Electrical Engineering at the University of Sistan and Baluchestan in 2014. He is currently pursuing his Ph.D. at the same institution, with expected graduation in 2025. His academic training has provided a strong foundation in optoelectronics, plasmonics, quantum and solid-state physics, and advanced signal processing, which underpin his research focus in AI-assisted photonic device engineering.
đź’Ľ Experience
Dr. Adibnia brings a blend of academic and industrial expertise to his work. In the academic realm, he has contributed as an assistant editor for the International Journal of Industrial Electronics Control and Optimization and as a reviewer for multiple high-impact journals. In industry, he worked as a PLC specialist at Kerman Motor Automotive Factory, where he modernized automation systems in the paint shop. Through programming and redesign of PLC control logic, he improved production efficiency, reduced chemical waste, and enhanced workplace safety—saving significant operational costs. These practical skills complement his theoretical pursuits, equipping him to tackle interdisciplinary engineering challenges.
🔬 Research Interest
Dr. Adibnia’s research interests span the convergence of deep learning with photonic system design. He focuses on fiber Bragg gratings (FBGs), plasmonic structures, photonic crystal filters, and erbium-doped fiber amplifiers. His work is notable for using neural networks—particularly hybrid models such as CNN-MLP architectures—for the inverse design of optical components. These efforts aim to improve design precision, reduce simulation time, and unlock new functional properties in photonic devices, with applications in high-speed optical networks, miniaturized logic circuits, and nonlinear optics.
đź“š Publications Top NoteÂ
“Chirped Apodized Fiber Bragg Gratings Inverse Design via Deep Learning”
Optics & Laser Technology, 2025
DOI: [10.1016/J.OPTLASTEC.2024.111766]
This study employs deep learning to design chirped apodized FBGs with tailored spectral properties, offering a faster and more accurate alternative to conventional design methods.
“Inverse Design of FBG-Based Optical Filters Using Deep Learning: A Hybrid CNN-MLP Approach”
Journal of Lightwave Technology, 2025
DOI: [10.1109/JLT.2025.3534275]
Dr. Adibnia introduces a hybrid convolutional neural network and multilayer perceptron (CNN-MLP) model that enables the inverse design of FBG filters. This approach enhances the flexibility and efficiency of optical filter development for communications.
“High-Performance and Compact Photonic Crystal Channel Drop Filter Using P-Shaped Ring Resonator”
Results in Optics, Dec 2025
DOI: [10.1016/j.rio.2025.100817]
In this collaborative work, a compact photonic crystal-based drop filter is designed using a novel P-shaped resonator. The proposed structure demonstrates high selectivity and performance, critical for optical signal routing.
“Optimizing Few-Mode Erbium-Doped Fiber Amplifiers for High-Capacity Optical Networks Using a Multi-Objective Optimization Algorithm”
Optical Fiber Technology, Sep 2025
DOI: [10.1016/j.yofte.2025.104186]
This publication presents a multi-objective optimization framework to enhance the performance of few-mode EDFAs, a key component in boosting the bandwidth of high-capacity optical networks.
“Inverse Design of Octagonal Plasmonic Structure for Switching Using Deep Learning”
Results in Physics, Apr 2025
DOI: [10.1016/j.rinp.2025.108197]
Here, Dr. Adibnia applies deep learning to optimize octagonal plasmonic structures for optical switching, achieving compact design with enhanced nonlinearity—a step forward for integrated photonic computing.
🏆 Conclusion
Dr. Ehsan Adibnia is a rising talent in the global research community, with a distinctive interdisciplinary approach that blends artificial intelligence and photonic engineering. His work addresses complex design problems in optics using cutting-edge AI, making meaningful contributions to the fields of fiber optics, nanophotonics, and integrated systems. As a published scholar, innovative thinker, and practical engineer, Dr. Adibnia embodies the ideal candidate for research awards that recognize academic excellence, engineering impact, and forward-thinking innovation.