Berivan Isik is a PhD student at Stanford University, co-advised by Sanmi Koyejo and Tsachy Weissman. Her research focuses on scalable and trustworthy machine learning, federated learning, model compression, differential privacy, and information theory. She was twice a research intern at Google, an applied scientist intern at Amazon, and a visiting researcher in Nicolas Papernot’s lab at Vector Institute. She has organized three ICML workshops: ICML-21 Workshop on Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning; ICML-21 Women in Machine Learning Workshop; and ICML-23 Workshop on Neural Compression: From Information Theory to Applications. She is the recipient of the 3-year Stanford Graduate Fellowship and 3-year Google PhD Fellowship.