- Electrical Engineering and Computer Science
Top 3 Areas of Expertise:
Two Sentence Summary: (1) Algorithm/Design and (2) High Impact Implementation
• Design data-mining and machine learning algorithms to infer user-intent across terabytes of noisy, large interaction datasets.
• Implement prediction, inference, and detection algorithms widely across 200+ MITx and HarvardX MOOC courses.
As a current PhD candidate in Computer Science at MIT, I work to make advanced education more accessible. Using machine learning and educational data mining technologies, I work with edX student data to (1) infer user-intent across terabytes of noisy, large interaction datasets and (2) implement prediction, inference, and detection algorithms distributed across 200+ MITx and HarvardX MOOC courses. Put simply, I research ways to improve online courses via the design and implementation of machine learning and data analytics algorithms.
Growing up in rural Kentucky, my peers and I encountered a glass ceiling of limited human and monetary resources that prevented students from the wealth of educational resources available in more prosperous areas. Fueled by this experience, I am motivated to pursue research that mitigates this glass ceiling for all students. Everyone deserves access to quality educational resources.
I see MOOCs as an answer to this problem and so my research focuses on ways we can improve the validity of MOOCs. My current work focuses on understanding how students use EdX in order to increase the validity of the learning process in MOOCs. More specifically, I employ a variety of machine learning techniques and algorithms to identify students who use technological exploits to attain certificates. By ensuring that earning a certificate in MOOCs is legitimately achieved, we can validate the worth of MOOCs and its global acceptance. In the most idealistic viewpoint, if my research can promote the validity of MOOC certifications, we step another step closer to the democratization of education.
Expected date of graduation:
Top 5 Awards and honors (name of award, date received):
5 Recent Papers:
Corrigan-Gibbs, Henry, Nakull Gupta, Curtis Northcutt, Edward Cutrell, and William Thies. "Measuring and Maximizing the Effectiveness of Honor Codes in Online Courses." In Proceedings of the Second (2015) ACM Conference on Learning@ Scale, pp. 223-228. ACM, 2015
Ho, Andrew Dean, Isaac Chuang, Justin Reich, Cody Austun Coleman, Jacob Whitehill, Curtis G. Northcutt, Joseph Jay Williams, John D. Hansen, Glenn Lopez, and Rebecca Petersen. "HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014." Available at SSRN 2586847 (2015).
Corrigan-Gibbs, Henry, Nakull Gupta, Curtis Northcutt, Edward Cutrell, and William Thies. "Deterring Cheating in Online Environments." ACM Transactions on Computer-Human Interaction (TOCHI) 22, no. 6 (2015): 28.
[PRE-PRINT, publication pending] Northcutt, Curtis G., Andrew D. Ho, and Isaac L. Chuang. "Detecting and Preventing" Multiple-Account" Cheating in Massive Open Online Courses." arXiv preprint arXiv:1508.05699 (2015).
Northcutt, Curtis G. "Security of Cyber-Physical Systems: A Generalized Algorithm for Intrusion Detection and Determining Security Robustness of Cyber Physical Systems using Logical Truth Tables." Vanderbilt Undergraduate Research Journal 9 (2013).