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 « 1 of 8 »


Soha, Rostaminia; Addison, Mayberry; Deepak, Ganesan; Benjamin, Marlin; Jeremy, Gummeson

iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass Journal Article

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1 (2), pp. 23, 2017.

Abstract | BibTeX

Adams, Roy J; Marlin, Benjamin M

Learning Time Series Detection Models from Temporally Imprecise Labels Conference

The 20th International Conference on Artificial Intelligence and Statistics, 2017, (<p>n/a</p>).

Abstract | Links | BibTeX

Dadkhahi, Hamid; Marlin, Benjamin

Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices Proceeding

2017, (<p>To appear.</p>).

Abstract | BibTeX

Dadkhahi, Hamid; Duarte, Marco F; Marlin, Benjamin M

Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series Journal Article

IEEE Transactions on Image Processing, 26 (11), pp. 5435–5446, 2017.

Abstract | BibTeX


Bernstein, Garrett; Sheldon, Daniel R

Consistently Estimating Markov Chains with Noisy Aggregate Data. Conference

AISTATS, Cadiz, Spain, 2016.


38 entries « 1 of 8 »