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.


38 entries « 7 of 8 »


Sheldon, Daniel; Sun, Tao; Kumar, Akshat; Dietterich, Thomas G

Approximate Inference in Collective Graphical Models Conference

In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013., 2013.


Natarajan, Annamalai; Parate, Abhinav; Gaiser, Edward; Angarita, Gustavo; Malison, Robert; Marlin, Benjamin; Ganesan, Deepak

Detecting Cocaine Use with Wearable Electrocardiogram Sensors Proceeding

Zurich, Switzerland, 2013.


Marlin, Benjamin M; Adams, Roy J; Sadasivam, Rajani; Houston, Thomas K

Towards Collaborative Filtering Recommender Systems for Tailored Health Communications Proceeding

Washington D.C., 2013.



Sheldon, D; Dietterich, T G

Collective Graphical Models Conference

Advances in Neural Information Processing Systems (NIPS 2011), 2012, (<p>n/a</p>).

Abstract | BibTeX

Hochachka, Wesley M; Fink, Daniel; Hutchinson, Rebecca A; Sheldon, Daniel; Wong, Weng-Keen; Kelling, Steve

Data Intensive Science Applied to Broad-Scale Citizen Science Booklet

2012, (<p>n/a</p>).

Abstract | BibTeX

38 entries « 7 of 8 »