Timon Citak

This study shows how mechanical crowding in 2D melanoma models influences resistance expansion and therapy failure, guiding adaptive therapy improvement through physical and computational models.

This paper introduces Reinforcement Failing, an AI framework that identifies emergent spatial dynamics in adaptive tumor therapy by refining training environments.

This study shows how intermittent treatment pulses reshape resource landscapes and spatial growth zones, enabling resistant mutants to escape confinement and drive therapy failure.

This paper develops computational frameworks to design personalized multi-strike therapies for ovarian cancer that reduce toxicity while enhancing tumor extinction.