May 2021: Completed my Ph.D. at MIT: “Confident Learning for Machines and Humans”. The questions (and answers) in this thesis lay the foundation for a new field of data-centric machine learning with noisy labels + applications for enhancing human capabilities. [ thesis PDF | defense slides ]
Apr 2021: Released labelerrors.com. [ paper | code | blog ]
Mar 2021: Published Confident Learning in JAIR (Journal of Artificial Intelligence Research): Confident learning is a subfield for data-centric machine learning with noisy labels, with theory for exactly finding label errors in real-world datasets. [ paper | code | blog ]
June 2020: Founded ChipBrain.com, an empathy AI company building digital brains with IQ and state-of-the-art EQ.
Dec 16, 2019: Added many updates to my Research page.
Nov 17, 2019: Announcing cleanlab: The official Python framework for machine learning and deep learning with noisy labels in datasets. [ code | docs ]
Nov 4, 2019: Published Confident Learning: A family of theory and algorithms for characterizing, finding, and learning with label errors in datasets. Confident learning outperforms state-of-the-art (2019) approaches for learning with noisy labels by 30% increase an accuracy on CIFAR benchmarks with high label noise.: [ paper | code | blog ]
Jan 20, 2019: Announcing the L7 Machine Learning blog A place for machine learning and human learning: l7.curtisnorthcutt.com
Nov 30, 2017: Released a tutorial framing the field of Artificial Intelligence in Online Education. [ slides ]
Aug 15, 2017: Released Rank Pruning, a robust time-efficient solution for binary classification with noisy labels published at UAI ‘17. [ paper | code | arXiv ]
Apr 20, 2017: Forum Ranking Diversification published at L@S ‘17. [ paper | code ]
Sep 20, 2017: CAMEO Cheating Detection in MOOCs and online courses published in Computers & Education ‘16. [ paper | code | arXiv ]