Success of artificial intelligence (AI) methods typically require large computational times and energy for computation. Recently, computing on neuromorphic chips has shown gains up to 100000× in terms of energy-delay product for certain applications compared to the traditional chips. Despite these promising initial results, extending these successes to a wider range of applications is a major challenge due to limitations of learning algorithms applicable on neuromorphic computing platforms. This project focuses on this challenge. Our main tool is spiking neural networks (SNNs). SNNs process data using spikes similar to how our brains process information. Using SNNs, we will develop machine learning solutions compatible with neuromorphic hardware. The goal of the project is to contribute to data and energy efficient AI using these brain-inspired solutions.