Dan Fu - An ideal learned representation should display transferability and robustness. Supervised contrastive learning is a promising method for training accurate models, but produces representations that do not capture these properties due to class collapse -- when all points in a class map to the same representation. In this talk, we discuss how to alleviate these problems to improve the geometry of supervised contrastive learning. We identify two key principles: balancing the right amount of geometric "spread" in the embedding space, and inducing an inductive bias towards subclass clustering. We introduce two mechanisms for achieving these aims in supervised contrastive learning, and show that doing so improves transfer learning and worst-group robustness. Next, we show how we can apply these insights to improve entity retrieval in open-domain NLP tasks (e.g., QA, search). We present a new method, TABi, that trains bi-encoders with a type-aware supervised contrastive loss and improves long-tailed entity retrieval.