Curtis Northcutt is a fifth-year NSF fellow, MITx Research Fellow, and Ph.D. Candidate in Computer Science at MIT, jointly in the Electrical Enginnering and Computer Science department and the Office of Digital Learning, working under the supervision of Isaac Chuang. His work spans learning with mislabeled training data, noisy learning, semi-supervised and unsupervised learning, cheating detection, and online education. Curtis has won numerous awards, including 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. Curtis invented and built the CAMEO cheating detection system used by MITx and HarvardX online course teams, particularly the MIT MicroMasters courses. He has led or been a part of numerous research and industrial efforts and has worked or interned 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. At MIT, Curtis is a teaching assistant for 6.867, a (400+ students) graduate machine learning course.
Areas of Expertise
- Learning with noisy labels
- Robust classification algorithms
- Weakly supervised learning
- Cheating detection
- Online education
Click here for a full list of Curtis Northcutt's publications.