The emerging in-memory computing technologies holds the great promise for achieving ultra-low power consumption in future efficient architectures. Among that various implemented hardware, the novel spintronic devices have emerged as an excellent candidate due to their rich nonlinear dynamics and non-volatile memory effect. The proposed project, ‘spintronic dynamics for reservoir computing’, aims to develop a spintronic-based computing diagram and enhance its computing capacity by controlling the spintronic dynamics. The nonlinearity and complexity dependency of spintronic for information processing capacity have been frequently observed, such as the edge of chaos, but still lacking the underlying principles behind those phenomena. Our research mainly focuses on computing optimization for spintronics-based systems. The selected UROP student will have the opportunity to deeply learn the nonlinear dynamics for spintronics and explore methods to enhance their computing capabilities, contributing to future spintronic-based in-memory computing technologies.