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
2013 |
Approximate Inference in Collective Graphical Models Conference In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013., 2013. |
Detecting Cocaine Use with Wearable Electrocardiogram Sensors Proceedings Zurich, Switzerland, 2013. |
Towards Collaborative Filtering Recommender Systems for Tailored Health Communications Proceedings Washington D.C., 2013. |
2012 |
Collective Graphical Models Conference Advances in Neural Information Processing Systems (NIPS 2011), 2012, (<p>n/a</p>). |
Data Intensive Science Applied to Broad-Scale Citizen Science Booklet 2012, (<p>n/a</p>). |