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

38 entries « 8 of 8 »


Xue, S; Fern, A; Sheldon, D

Scheduling Conservation Designs via Network Cascade Optimization Conference

Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012, (<p>n/a</p>).

Abstract | BibTeX

Krafft, Peter; Moore, Juston; Wallach, Hanna; Desmarais, Bruce

Topic-Partitioned Multinetwork Embeddings Conference

Advances in Neural Information Processing Systems Twenty-Five, Lake Tahoe, NV, 2012.

Abstract | BibTeX

Marlin, Benjamin M; Kale, David C; Khemani, Robinder G; Wetzel, Randall C

Unsupervised pattern discovery in electronic health care data using probabilistic clustering models Conference

Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, IHI textquoteright12 ACM ACM, New York, NY, USA, 2012, ISBN: 978-1-4503-0781-9.

Abstract | Links | BibTeX

38 entries « 8 of 8 »