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.
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 Proceeding
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models Proceeding
Barcelona, Spain, 2016.
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>).