Hybrid systems are versatile in modeling the interaction between the cyber and physical components of cyber-physical control systems (CPS) such as artificial pancreas (AP). They are typically used for analysis of safety of the human centric control systems which have serious consequences of failure. As such hybrid systems are parameterized and the variables often depend on the subject on which the control system is deployed. Traditionally, control systems are initially developed using average statistical estimates of the subject specific parameters. However, such excursions may lead to suboptimal designs. In this paper, we propose HyMn, a hybrid system parameter estimation tool, where the subject specific parameters in a hybrid system are automatically learned from experimental traces of the operation of a human centric CPS control system. We apply HyMn to the AP system and show that the blood glucose control is enhanced using the learned patient specific parameters.