Jessica Sorrell is an assistant professor in the Department of Computer Science at the Johns Hopkins University.
Sorrell works in the theoretical foundations of machine learning, with a focus on improving the reliability and trustworthiness of machine learning methods. She helped pioneer the study of formal replicability, designing algorithms for—and establishing computational barriers to—replicable learning. Her work spans privacy, fairness, and robustness in learning. She also has an interest in lattice-based cryptography with a focus on secure computation.
She was previously a postdoctoral researcher at the University of Pennsylvania, working with Aaron Roth and Michael Kearns. Sorrell completed her PhD at the University of California, San Diego, where she was advised by Daniele Micciancio and Russell Impagliazzo.