Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Address: 32 Vassar St., Cambridge, MA 02142
Email: dshayden [at] csail [dot] mit [dot] edu
I build probabilistic models that enable large-scale hypothesis testing
with a minimum of human oversight. By working towards automated
interventions, my tools will make new experiment designs possible from
what once were observational studies.
Broadly, I work on interpretable machine learning and vision with focus on behavior analysis, multi-object tracking, Bayesian nonparametrics applied to time-series, distributions on manifolds, and using uncertainty to guide decision making, analysis, and experiment design.
Uncertainty Quantification and Structure Discovery for Scalable Behavior Science
David S. Hayden (Advisor: John W. Fisher III, Comittee: Robert Desimone, Justin Solomon)
PhD Thesis 2021
Sequential Bayesian Experimental Design with Variable Cost Structure
Sue Zheng, David S. Hayden, Jason Pacheco, John W. Fisher III
Efficient Data Association and Uncertainty Quantification for Multi-Object Tracking
David S. Hayden, Sue Zheng, John W. Fisher III
Nonparametric Object and Parts Modeling with Lie Group Dynamics
David S. Hayden, Jason Pacheco, John W. Fisher III
Atypical Behaviour and Connectivity in SHANK3-Mutant Macaques
Yang Zhou, Jitendra Sharma, Qiong Ke, Rogier Landman, Jingli Yuan, Hong Chen, David S. Hayden et al.
Nature, June 2019
Unobtrusive, Wearable Social Interaction Detection and Assistance
David S. Hayden, Robert C. Miller, Seth Teller
CHI 2014 Workshop on Assistive Augmentation
The Accuracy-Obtrusiveness Tradeoff for Wearable Vision Platforms
David S. Hayden, Carl Vondrick, Stella Jia, Yafim Landa, Robert C. Miller, Antonio Torralba, Seth Teller
CVPR 2012 Workshop on Egocentric Vision
Using Clustering and Metric Learning to Improve Science Return of Remote Sensed Imagery
David S. Hayden, Steve Chien, David R. Thompson, Rebecca Castaño
ACM Transactions on Intelligent Systems and Technology 2012
Note-Taker 3.0: an Assistive Technology Enabling Students who are Legally Blind to Take Notes in Class
David S. Hayden, Michael Astrauskas, Qian Yan, Liqing Zhou, John A. Black Jr.
The Note-Taker: a Tablet PC Based Device that Helps Students Take and Review Classroom Notes
David S. Hayden, Liqing Zhou, John A. Black Jr.
The Impact of Tablet PCs and Pen-based Technology on Education (2010, ISBN: 1557535744)
Note-Taker 2.0: the Next Step Toward Enabling Students who are Legally Blind to Take Notes in Class
David S. Hayden, Liqing Zhou, Michael Astrauskas, John A. Black Jr.
Note-Taker: Enabling Students who are Legally Blind to Take Notes in Class
David S. Hayden, Dirk Colbry, John A. Black Jr., Sethuraman Panchanathan