As part of the Qualcomm Innovation Fellowship 2019, I have a talk on the research produced throughout the academic year 2019/2020. This talk covers two exciting works on robustness: robustness against various types of adversarial examples using confidence-calibrated adversarial training (CCAT) and robustness against bit errors in the model’s quantized weights. The latter can be shown to be important to reduce the energy-consumption of accelerators for neural networks. In this article, I want to share the slides corresponding to the talk.