M.Sc. Computer Science — Applied ML & Physical Systems
Kassel-based researcher focused on machine learning for physical systems, time series, and real-time experimentation.
Implemented and tested AI models directly on the linear accelerator, built a live experimental data pipeline, and delivered real-time predictions in an international team.
Developed machine learning models to predict laser pulse characteristics and supported quality assurance across an international research team.
Co-developed a physics experiment control framework and trained AI models to detect magnetic particles and track their trajectories under a microscope.
Built a custom diffusion-based model for synthetic household load time series generation and evaluated it on German and UK energy datasets.
Nikita Popkov
Research on iterative, experiment-driven model improvement for physical systems, with emphasis on feedback stability and real-data constraints. Developed a TANGO based device network structure that allows real-time feature extraction and experimental analysis.
Nikita Popkov
Investigated denoising diffusion probabilistic models for time series, with application to energy load data cleaning and synthetic generation. Building own DDPM model architecture, training the model on a set of german household data and evaluating it against a set timeframe.
Coming soon.