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 « 7 of 8 »


Sheldon, Daniel; Sun, Tao; Kumar, Akshat; Dietterich, Thomas G

Approximate Inference in Collective Graphical Models Conference

In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013., 2013.


Natarajan, Annamalai; Parate, Abhinav; Gaiser, Edward; Angarita, Gustavo; Malison, Robert; Marlin, Benjamin; Ganesan, Deepak

Detecting Cocaine Use with Wearable Electrocardiogram Sensors Proceeding

Zurich, Switzerland, 2013.


Marlin, Benjamin M; Adams, Roy J; Sadasivam, Rajani; Houston, Thomas K

Towards Collaborative Filtering Recommender Systems for Tailored Health Communications Proceeding

Washington D.C., 2013.



Sheldon, D; Dietterich, T G

Collective Graphical Models Conference

Advances in Neural Information Processing Systems (NIPS 2011), 2012, (<p>n/a</p>).

Abstract | BibTeX

Hochachka, Wesley M; Fink, Daniel; Hutchinson, Rebecca A; Sheldon, Daniel; Wong, Weng-Keen; Kelling, Steve

Data Intensive Science Applied to Broad-Scale Citizen Science Booklet

2012, (<p>n/a</p>).

Abstract | BibTeX

38 entries « 7 of 8 »