12-01-17: This is a new site and currently under construction. It should be complete by early 2018. For now, here is my resume.
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in Online Education, including state-of-the-art solutions to important online education problems, as well as bits of my unpublished research, see these slides.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.
See news for more.
I am a fifth-year Ph.D. Candidate in Computer Science at MIT, an NSF Fellow, and an MITx Digital Learning Research Fellow working under the supervision of Isaac Chuang. My work spans robust algorithms for classification with mislabeled training data and noise estimation, noisy learning, weak supervision, semi-supervised learning, cheating detection, and online education. Some of my awards are 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). I created and manage the cheating detection system used by MITx and HarvardX online course teams, particularly the MIT MicroMasters courses. I am a teaching assistant for 6.867, a (300+ students) graduate machine learning course at MIT.
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.