Nataliiia Koneva

Engaged in advanced research focused on the application of AI and ML techniques for intelligent network optimization in IoT systems. Responsibilities include developing algorithms to enhance QoS performance, conducting thorough analyses of 5G network traffic, and addressing the challenges posed by the exponential growth of connected devices. Actively involved in the EU projects B5G-Open, ALLEGRO, and SEASON. Teaching part of the course Networks Theory (Academic years: 2022/23, 2023/24, and 2024/25).

• Quantified the per-prompt traffic generated by the most popular open-source LLMs and forecasted the “Internet of Agents” traffic as an imminent backbone load. • Analysed global ping RTTs, uncovering widespread triangle-inequality violations and demonstrating latency-reducing multi-hop routing opportunities.

Developed applications for assisted teaching equipment based on data acquisition and signal processing, and created a mathematical simulator of physical processes in LabVIEW.

Software development for automation control and measuring processes on LabVIEW and Python; testing radio frequency equipment and analog/digital circuits.

Developed software for testing special-purpose products on instrumentation equipment and created testing methods, along with drafting technical documentation and manuals.

Taught 3D modeling, logic, algorithms, and programming (C++, Python, Arduino) to children; organized and created educational methods.

Thesis title: Applications of AI/ML techniques for intelligent network optimization.

Thesis title: Analysis of network requirements for the transmission of Industrial IoT applications with QoS guarantees.

Master’s Thesis title: Spectral Analysis of Electroencephalogram Signals in Human Object Recognition Using MATLAB.

Bachelor’s Thesis title: Spectral analysis of electroencephalography signals in MATLAB.