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 |
Center of excellence for mobile sensor Data-to-Knowledge (MD2K) Journal Article In: Journal of the American Medical Informatics Association, vol. 22, pp. 1137–1142, 2015, (<p>n/a</p>). |
CIDER: Enabling Robustness-Power Tradeoffs on a Computational Eyeglass Conference Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, ACM ACM, 2015, (<p>n/a</p>). |
Classification of Sparse and Irregularly Sampled Time Series with Mixtures of Expected Gaussian Kernels and Random Features Journal Article In: Proceedings of the 31st Conference on Uncertainty in Artficial Intelligence(UAI-15), 2015, (<p>n/a</p>). |
Inference in a Partially Observed Queuing Model with Applications in Ecology Conference Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015, (<p>n/a</p>). |
iProgram: Inferring Smart Schedules for Dumb Thermostats Conference Proceedings of the 2Nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, BuildSys textquoteright15 ACM ACM, New York, NY, USA, 2015, ISBN: 978-1-4503-3981-0, (<p>n/a</p>). |