01-20-19: Announcing the L7 blog! A place for machine learning and human learning: l7.curtisnorthcutt.com
11-30-17: For a tutorial-style framing of the field of Artificial Intelligence in Online Education, including state-of-the-art solutions to important online education problems, as well as bits of my unpublished research, see these slides.
08-15-17: Rank Pruning is a state-of-the-art, robust, time-efficient, general algorithm for classification with noisy labels published at UAI ‘17.
04-20-17: Forum Ranking Diversification published at L@S ‘17.
09-20-16: CAMEO Cheating Detection in MOOCs and online courses published in Computers & Education ‘16.
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Curtis G. Northcutt is a grad student in Computer Science at MIT, supported by a NSF Fellowship and a MITx Digital Learning Research Fellowship working with Isaac Chuang. His work focuses on two goals: (1) characterizing and fixing (or learning in spite of) label errors in machine learning datasets, (2) using artificial intelligence to enable human intelligence. To this end, Curtis invented confident learning, a family of theory and algorithms for learning with label errors, and created cleanlab, a Python package using confident learning to find label errors in datasets, characterize label noise, and learn with noisy labels. Other fields related to Curtis’s work are weak supervision, semi-supervised learning, and online education.
Curtis has been fortunate to receive the MIT Morris Joseph Levin Masters Thesis Award, an NSF Graduate Research Fellowship, the Barry M. Goldwater National Scholarship, and the Vanderbilt Founder’s Medal (Valedictorian). Curtis created and manages the cheating detection system used by MITx and HarvardX online course teams, particularly the MIT MicroMasters courses. While at MIT, he TA’d 6.867, a large graduate machine learning course.
I am fortunate to have had the opportunity to work or intern at Amazon Research, Facebook AI Research (FAIR), Microsoft Research (MSR) India, MIT Lincoln Laboratory, Microsoft, NASA, General Electric, and a National Science Foundation REU including collaborations with MIT, Harvard, Vanderbilt, Notre Dame, and the University of Kentucky. Details here.
When you educate a person, you empower them within their community, and when you empower people socially, you give them hope, purpose, opportunity, and most importantly, you give them freedom.
Growing up below the poverty line in rural Kentucky, I experienced a glass ceiling of limited human and monetary resources. The ladder of opportunity often rises from prosperity rather than ability. My ladder was my education. Education led to exposure, then summer programs, then small scholarships, then bigger scholarships, and eventually opportunity. Everyone deserves access to quality educational resources – this underlies my motivation to pursue research that democratizes education.
To this end, I develop robust machine learning algorithms to enable open learning, i.e. to make advanced education more accessible. I work with edX student data to (1) infer user-intent across terabytes of noisy, massive interaction datasets and (2) implement prediction, inference, and detection algorithms distributed across 400+ MITx and HarvardX open online courses. For example, I ensure the legitimacy of online course certificates via cheating detection algorithsm and with the help of exceptional colleagues, have demonstrated how machine learning can transform human learning with accurate proficiency estimation and diversification of comment rankings in discussion forums.