Featured in the News

• April 2021: Our work on finding pervasive label errors in the 10 most commonly used machine learning test sets was featured throughout the news, including Andrew Ng’s deeplearning.ai blog, and on WIRED magazine.
• Feb 11, 2021: My company [ChipBrain][https://chipbrain.com/] is featured on NASDAQ.com.
• Nov 15, 2019: Our confident learning paper is recognized. [ link ]
• Apr 21, 2019: Our Massive Open Online Courses paper is discussed. [ link ]
• Apr 17, 2019: My work helping researchers build deep learning machines at low-cost is discussed. [ link ]
• May 24, 2017: My work in human learning is discussed. [ link ]
• Aug 26, 2015: Our CAMEO cheating detection paper is discussed. [ link ]

Some Personal News

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 ]