Our Lab News
(Jul ‘24) Our paper evaluating bias in neural compression models was accepted at ICML 2024 Workshop on Next Gen AI Safety. Led by Tian, Arjun, and Rasta 🙌🏻.
(Jul ‘24) Jin presented his first paper on “Coded Computing Meets Quantum Circuit Simulation: Coded Parallel Tensor Network Contraction Algorithm” got accepted to ISIT 2024 🥳 Full paper here.
(Jul ‘24) PI Jeong gave an invited talk at ISIT 2024 workshop on Information-Theoretic Methods for Trustworthy Machine Learning (IT-TML).
(Jun ‘24) PI Jeong is named as a 2024-2025 Hellman Fellow.
(May ‘24) Our amazing undergrad researcher Iain presented a poster on “Coded Computing for Fault-Tolerant Parallel QR Decomposition” at SIAM Linear Algebra 2024 🇫🇷
(May ‘24) PI Jeong received the prestigious NSF CAREER Award! See this article to learn more about the project.
Research
Our lab’s mission is to do science for machine learning and machine learning for science. We advance our fundamental understanding of machine learning algorithms and we apply data-driven approaches to solve challenging scientific problems. Specifically, our lab’s research centers around three research themes: reliable computing for ML, responsible AI, and ML for science. See below for more information about each theme.
Keywords:Machine Learning, Responsible AI, Algorithmic Fairness, Generative Models, Information Theory, Differential Privacy, Distributed Computing, Fault-tolerant Computing, Quantum Computing, AI for Science, AstroAI
