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 |
Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications Conference ICML On Device Intelligence Workshop, 2016, (<p>n/a</p>). |
Parsing Wireless Electrocardiogram Signals with the CRF-CFG Model Proceedings 2016, (<p>n/a</p>). |
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models Proceedings Barcelona, Spain, 2016. |
2015 |
Hierarchical Nested CRFs for Segmentation and Labeling of Physiological Time Series Conference NIPS Workshop: Machine Learning for Healthcare, 2015. |
Bayesian poisson tensor factorization for inferring multilateral relations from sparse dyadic event counts Conference Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM ACM, 2015, (<p>n/a</p>). |