We are always interested in hearing from MS/PhD and PhD students who would like to join the lab. The desired educational background includes calculus, linear algebra, probability and statistics, artificial intelligence, algorithms, and programming. Prior experience with machine learning, numerical optimization or Bayesian statistics is a plus. Relevant prior research experience is highly valued. Candidates should apply directly to the College of Information and Computer Sciences and should clearly indicate their interest in the MLDS lab in their personal statement. Information about the UMass CS graduate program is available here.
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>).