Ksenia Zlobina

This paper proposes a deep learning-based control framework for wound healing that accelerates closure by 17.71% in simulations and 40% in vivo.

This paper develops a deep reinforcement learning framework for real-time wound healing treatment optimization, showing 17.71% acceleration in in silico experiments and 40% in vivo.

The paper presents an algorithm to identify proteomic biomarkers for health status, applying it to find 9-protein indicators for atopic dermatitis and 6-protein indicators for heart failure.

This paper develops a mathematical model to identify optimal spatio-temporal regimes for wound treatment by analyzing macrophage polarization dynamics.