Aleksei Belonovskii

• As part of the emergency response team, diagnosed and resolved critical system malfunctions in C#, C++, and Java, preventing financial and operational disruptions. • Created urgent custom scripts in C#, C++, and Python to support debugging and enhance reliability of large-scale applications. • Deployed local Kubernetes and Docker environments with Kafka and databases to replicate cloud issues and improve troubleshooting.

• Conducted research on the application of transformer neural networks in photonics, specifically for determining the characteristics of nanostructures. • Authored the research paper “Predicting VCSEL Emission Properties Using Transformer Neural Networks”.

• Conducted research in the field of photonics and quantum optics, participating in multiple grant-funded projects. • Investigated spontaneous emission probabilities in nanostructures and introduced the concept of meso-cavities. • Developed a theoretical model for the interaction of a single exciton mode with multiple photonic modes, revealing nonlinear behavior and potential for strong/weak coupling in such systems.

• Conducted research on Tamm plasmon interactions with organic emitters and spontaneous radiative recombination in cylindrical structures. • Developed custom Python applications for scientific research, including the use of neural networks. • Authored and co-authored approximately 30 papers in scientific journals. Presented findings at conferences and engaged with industry professionals.

• Developed C#/WPF desktop software for X-ray analyzers and factory control stations, automating material analysis and system operations. • Built reusable C#/C++ modules that reduced deployment time and enabled easy reconfiguration across sites. • Improved spectrometer performance through applied research in X-ray fluorescence and algorithm tuning. • Created C++/Python tools for spectral analysis using physics-based and ML-assisted models.

• Conducted theoretical modeling of black hole systems, including estimation of spin and magnetic field parameters based on observational constraints. Developed a MATLAB application to automate these computations and streamline parameter extraction.