Diego Antognini is a research scientist at IBM Research AI working on efficient machine learning and low-resource settings for NLP. He received his Ph.D. from EPFL where he developed models to infer high-quality explanations from text documents in a scalable and data-driven manner via selective rationalization. He also designed new models to make explanations actionable (called critiquing) and examined two important applications in NLP and conversational recommendation systems. Current research interests include efficient ML, selfsupervision, knowledge distillation, text understanding, explainable AI, and conversational recommender systems.