Mariia Vidmuk

Designed and deployed production MCP-based agent system for automated extraction of structured data from clinical documents, using modular design, CI/CD, and version control, integrated with Oracle databases. Benchmarked LLM agent performance on information extraction tasks and developed AI solutions in a regulated environment.

Applied Bayesian optimization to EMG signal data to improve stimulation selectivity, achieving 97% accuracy in EMG-based optimal parameter identification. Performed experiments on human subjects.

Trained and fine-tuned deep learning models on multimodal MRI imaging data to predict patient benefit from surgery, achieving 94% agreement with expert annotations. Authored a scientific manuscript and a patent application, and communicated findings to non-technical stakeholders.

Reproduced and extended classification methods on EEG time-series data from epilepsy patients. Compared signal processing techniques, machine learning models, and validation strategies.

Grade: 5.06/6

Pursued a Master's degree in Data Science with a GPA of 5.06/6, graduating in 2025.

Grade: 89.2/100

Completed a Bachelor's degree in System Analysis with a GPA of 89.2/100, graduating in 2022.