Haewon Jeong received a Ph.D. in Electrical and Computer Engineering at Carnegie Mellon University. Her thesis established important foundations on how we can apply coding theory to building reliable large-scale computing systems. Before joining UCSB, she was a postdoctoral fellow at Harvard University, where she explored reliability in a different sense: how to build a machine learning system humans can trust and rely on. In particular, she investigated how machine learning systems can discriminate against students in education-related applications, and how we can build more fair machine learning algorithms. She is passionate about social justice in education and has actively participated in outreach programs teaching math and science to underprivileged K-12 students.
Her work has been published in a wide range of journals and conferences including Proceedings of the IEEE, IEEE Transactions on Information Theory, Conference and Workshop on Neural Information Processing Systems, Proceedings of the AAAI Conference on Artificial Intelligence, IEEE International Conference on Big Data, and European Conference on Parallel Processing. She has organized the ICML-21 Workshop on Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning and the ISIT 2022 Tutorial on Information-Theoretic Tools for Responsible Machine Learning. Her research team will marry different fields (e.g., Information Theory, Statistics, Machine Learning, Distributed Systems) to develop theoretically-grounded tools for trustworthy and reliable machine learning systems.