popularity in solving difficult engineering problems ranging from design, manufacturing to system performance monitoring and control. Furthermore, with the advent of deep learning, the capability of handling high levels of system complexity and very large data sets has enhanced dramatically. This talk will discuss three recent success stories of Machine and Deep Learning for engineering analytics that are rather nontraditional in the context of computer science. First, I will share some recent supervised deep learning case studies in design optimization for microfluidic lab-on-chip devices and design for manufacturability for fast and democratized product design. I will then discuss how we used semi-supervised deep learning models for early detection of flame instability in combustion processes from hi-speed flame images in order to prevent catastrophic lean blow out in aircraft and other engines. Finally, I will conclude with a complex human-engineered system monitoring application that leverages unsupervised machine learning models.