My research is in machine learning and computer vision, including deep learning, and its applications to affective computing, automatic facial expression recognition, human behavior analysis, and educational data mining. I am also interested in how to make machine learning processes more efficient by devising innovative paradigms for collecting training datasets and annotations.
My work is highly interdisciplinary and frequently intersects cognitive science, psychology, and education.
- Aguerrebere, C., Cobo, C., and Whitehill, J. "Estimating the Treatment Effect of New Device Deployment on Uruguayan Students' Online Learning Activity". Educational Data Mining, 2018. PDF.
- Aung, A., Ramakrishnan, A., and Whitehill, J. "Who are they looking at? Automatic Eye Gaze Following for Classroom Observation Video Analysis". Educational Data Mining, 2018. PDF.
- Giancola, M., Paffenroth, R., and Whitehill, J. "Permutation-Invariant Consensus over Crowdsourced Labels". Human Computation (HCOMP), 2018. PDF.
- Aung, A., and Whitehill, J. "Harnessing Label Uncertainty to Improve Modeling: An Application to Student Engagement Recognition". IEEE Face & Gesture Recognition (FG), 2018. PDF. Supplementary Materials.
- Jiang, H., Dykstra, K., and Whitehill, J. "Predicting When Teachers Look at Their Students in 1-on-1 Tutoring Sessions". IEEE Face & Gesture Recognition (FG) Workshop on Human Behavior Understanding (HUB). PDF.
- Whitehill, J. "Climbing the Kaggle Leaderboard by Exploiting the Log-Loss Oracle". AAAI
Workshop on Engineering Dependable and Secure Machine Learning Systems. PDF.
- Whitehill, J., and Movellan, J. "Approximately Optimal Teaching of Approximately Optimal Learners". Transactions on Learning Technologies (accepted), 2017. PDF. Appendix: PDF.
- Whitehill, J., Mohan, K., Seaton,, D., Rosen, Y., and Tingley, D. "MOOC Dropout Prediction: How to Measure Accuracy?". Short paper at Learning at Scale 2017. PDF
- Whitehill, J., and Seltzer, M. "Toward Personalized Learning-at-Scale: A Crowdsourcing Approach". Short paper at Learning at Scale 2017. PDF
- Turkay, S., Eidelman, H., Rosen, Y., Seaton, D., Lopez, G., and Whitehill, J. "Getting to know English language learners in MOOCs: Their motivations, behaviors and outcomes." Short paper at Learning at Scale 2017. PDF: forthcoming
- Whitehilll, J., Ruvolo, P., Wu, T., Bergsma, J., and Movellan, J. "Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise. NIPS 2009. Supplementary materials. Software.
See my Google Scholar profile.
- Whitehill, J. and Ottmar, E. "Towards Computer-Assisted Coding of Classroom Observations: A Computer Vision Approach to Measuring Positive Climate". Spencer Foundation Small Research Grant, 2018. $49,850. Link.
- July 16, 2018: Educational Data Mining, slides.
- May 17, 2018: IEEE Automatic Face & Gesture Recognition: Harnessing Label Uncertainty to Improve Modeling: An Application to Student Engagement Recognition. slides.
- April 12, 2018: National Council on Educational Measurement Symposium: Measuring Collaboration and Engagement using Big Data slides.
- February 3, 2018: AAAI Workshop on Engineering Dependable and Secure Machine Learning Systems slides.
- December 14, 2017: IEEE Big Data PSBD Workshop slides.
- March 31, 2017: TU-Delft Data Science Seminar slides.
- October 5, 2016: Boston University IVC Group Talk slides.
- Spring 2018: CS453X: Machine Learning
- Spring 2018: CS525 191D: Deep Neural Networks
- Spring 2017: CS525 191N: Deep Neural Networks
- Fall 2016, B-term: CS210X: Accelerated Object-oriented Design