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
2016 |
Assessing the Limits of Program-Specific Garbage Collection Performance Conference Programming Language Design and Implementation, 2016, (<p>Distinguished Paper Award</p>). |
Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century Journal Article In: Journal of Medical Internet Research, vol. 18, 2016, (<p>n/a</p>). |
Detecting Divisions of the Autonomic Nervous System Using Wearables Proceedings Florida, USA, 2016. |
Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG Proceedings Heidelberg, Germany, 2016. |
Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams Conference International Conference on Machine Learning, 2016, (<p>n/a</p>). |