Making computers human-like is one of the ultimate goals of electrical and computer engineers. So far, the AI development has been fueled by the advancement of hardware such as GPUs. The performance boost, on one hand due to specialized architecture, on the other hand is driven by the transistor scaling that is difficult to sustain its pace for sub-5nm nodes. Meanwhile, the increase in big data workloads, in part due to the IoTs, are driving computing increasingly data-centric, posing another challenge to von Neumann machines, since frequent data shuttling between the physically separated processing and memory units limits the energy efficiency and data bandwidth. The focus of Dr. Wang’s research is to address this challenge by computing with the emerging electronic memory (e.g. RRAM or memristors), where both information processing and storage are performed on the same device. This “compute-by-physics” not only accelerates machine learning, now being actively pursued by both academia and industry giants but also emulates the rich dynamics of neuromorphic computing.