Abstract: One of the hallmarks of advanced manufacturing is a shift from the homogenous designs and repetitive tasks of mass production to unique, customizable, and adaptable designs and manufacturing processes. Recent advances in machine learning hold great promise for various aspects of advancements to manufacturing. For example, generative models provide opportunities for design automation of customizable forms, while deep learning enables autonomous robots and decision making systems to react to novel sensory information in unstructured environments. This talk will provide examples of recent advances in both of these domains, while also discussing remaining open challenges -- such as co-optimizing the physical structure and control policies of 3D-printed robots or optimizing many-task machine learning models for online continual learning in ever-changing environments. My hope is that laying out the current progress and open challenges in these aspects of machine learning for advanced manufacturing may lead to collaborations in both fundamental and use-inspired research across (and beyond) the newly formed Northeast Integrated Intelligent Manufacturing Lab.