Svyatoslav Voloshynovskiy

Develop novel methods of inverse problem solutions based on denoising diffusion probabilistic models; investigate new approaches to uncertainty estimation based on stochasticity of denoising diffusion models; introduce new techniques of conditional generation based on concatenation, cross-attention and bias.

Include (in-situ) data information into the calibration of jet energy scales (JES) at an early ML-based stage, and improve the jet energy resolution due to considering the correlations among the variables relevant for the JES calibration and to integrating the in-situ constraints at the step of the training of the ML algorithm.

Develop XAI techniques for online and transfer learning applied to experimental data from physics and signal and image understanding; handle efficiently real-time massive sensor data in online and transfer learning; develop XAI techniques for high robustness and accuracy in multi-sensor environments.