Spinal Cord Injury (SCI) disrupts the communication between the brain and spinal circuits below the lesion, leading to a plethora of neurological impairments, including the loss of motor function. At present, the only medical practices to enhance recovery after paralysis are activity-based therapies. In severe cases, SCI-patients fail to produce active movements voluntarily and are therefore unable to fully benefit from these therapies. Epidural Electrical Stimulation (EES) applied over the lumbosacral spinal cord has demonstrated to enable locomotion in preclinical animal models and humans with SCI. EES may therefore play a pivotal role in the recovery of locomotion after SCI. However, neither has EES reached the same level of efficacy in humans as in preclinical animal models, nor is there consensus on the definition of clinically meaningful stimulation protocols. In the absence of clear understanding on how EES promotes the formation of motor patterns, stimulation protocols have been largely based on empirical observations. Instead, hypothesis-driven definition of stimulation parameters has demonstrated superior efficacy in other neuromodulation therapies, such as deep brain stimulation. In this thesis, I outline a computational approach to inform EES strategies for the recovery of neurological function after SCI. For this purpose, I leveraged a combination of finite element methods, neurophysical and neuromusculoskeletal models to refine our understanding on how EES promotes locomotion after SCI, inform the design of neuromodulation technologies and develop personalized EES treatments in humans with SCI. In the first part of this thesis, I provide evidence that EES recruits proprioceptive and cutaneous afferents in the dorsal roots. I suggest that the recruitment of proprioceptive feedback circuits promotes the formation of motor patterns. In contrary, the continuous recruitment of cutaneous afferents may disrupt the production of locomotion. In the next chapter, I study interspecies difference of EES. I argue that these differences necessitate the adaption of stimulation protocols to enable translation of EES as a therapy from preclinical models to humans. I propose the application of spatiotemporal protocols with low-amplitude, high-frequency bursts to improve the efficacy of EES in humans. In the following chapter, I describe a technological framework that enables the implementation of such stimulation paradigms in people with SCI. I outline how personalized computational modeling can be used for treatment planning. I then summarize how I advanced this rudimentary computational tool into a computational framework for the rapid elaboration of highly realistic personalizable computational models. I explain how I leveraged this framework to inform the arrangements of electrodes on a paddle lead to enable locomotion in large and diversified patient-cohorts, perform preoperative treatment planning and validate the efficacy of both the lead and treatment planning protocol in individuals with severe, chronic SCI. Finally, I summarize how I translated my computational framework for other use-cases including the control of hemodynamics and the evaluation of an optoelectronic system to enable locomotion in preclinical models.