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.


Recent Publications

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Dadkhahi, Hamid; Saleheen, Nazir; Kumar, Santosh; Marlin, Benjamin

Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications Conference

ICML On Device Intelligence Workshop, 2016, (<p>n/a</p>).

Abstract | BibTeX

Nguyen, Thai; Adams, Roy J; Natarajan, Annamalai; Marlin, Benjamin M

Parsing Wireless Electrocardiogram Signals with the CRF-CFG Model Proceeding

2016, (<p>n/a</p>).

Abstract | BibTeX

Winner, Kevin; Sheldon, Daniel

Probabilistic Inference with Generating Functions for Poisson Latent Variable Models Proceeding

Barcelona, Spain, 2016.



Adams, Roy; Thomaz, Edison; Marlin, Benjamin M

Hierarchical Nested CRFs for Segmentation and Labeling of Physiological Time Series Conference

NIPS Workshop: Machine Learning for Healthcare, 2015.


Schein, Aaron; Paisley, John; Blei, David M; Wallach, Hanna

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>).

Abstract | BibTeX

38 entries « 3 of 8 »