About the Lab
The MLDS lab focused on the development of machine learning models and algorithms for addressing a variety of challenging problems in the areas of computational social science, computational ecology, computational behavioral science and computational medicine.
The MLDS lab’s research continues in multiple labs within the the College of Information and Computer Sciences including the REML lab, the SLANG lab, and Prof. Sheldon’s research group.
Publications
2015 |
Message Passing for Collective Graphical Models Conference Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015, (<p>n/a</p>). |
puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation Conference Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM ACM, 2015, (<p>n/a</p>). |
Collaborative Multi-Output Gaussian Processes for Collections of Sparse Multivariate Time Series, Conference NIPS Time Series Workshop, 2015, (<p>n/a</p>). |
Hierarchical Nested CRFs for Segmentation and Labeling of Physiological Time Series Conference NIPS Workshop on Machine Learning in Healthcare, 2015, (<p>n/a</p>). |
iProgram: Inferring Smart Schedules for Dumb Thermostats Conference 10th Annual Women in Machine Learning Workshop, 2015, (<p>n/a</p>). |