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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Soha, Rostaminia; Addison, Mayberry; Deepak, Ganesan; Benjamin, Marlin; Jeremy, Gummeson

iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass Journal Article

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1 (2), pp. 23, 2017.

Abstract | BibTeX

Adams, Roy J; Marlin, Benjamin M

Learning Time Series Detection Models from Temporally Imprecise Labels Conference

The 20th International Conference on Artificial Intelligence and Statistics, 2017, (<p>n/a</p>).

Abstract | Links | BibTeX

Dadkhahi, Hamid; Marlin, Benjamin

Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices Proceeding

2017, (<p>To appear.</p>).

Abstract | BibTeX

Dadkhahi, Hamid; Duarte, Marco F; Marlin, Benjamin M

Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series Journal Article

IEEE Transactions on Image Processing, 26 (11), pp. 5435–5446, 2017.

Abstract | BibTeX


Bernstein, Garrett; Sheldon, Daniel R

Consistently Estimating Markov Chains with Noisy Aggregate Data. Conference

AISTATS, Cadiz, Spain, 2016.


38 entries « 1 of 8 »