H O M E

I am an assistant professor in the School of Computer Science at the University of Massachusetts Amherst where I co-direct the Machine Learning for Data Science Lab with Hanna Wallach and Dan Sheldon. I was previously a fellow of both the Pacific Institute for the Mathematical Sciences and the Killam Trusts at the University of British Columbia where I was based in the Laboratory for Computational Intelligence in the Department of Computer Science. I completed my PhD in machine learning in the Department of Computer Science at the University of Toronto.

Research Interests: My research interests lie at the intersection of artificial intelligence, machine learning and statistics. I am particularly interested in hierarchical graphical models and approximate inference/learning techniques including Markov Chain Monte Carlo and variational Bayesian methods. My current research has a particular emphasis on models and algorithms for multivariate time series data. I have worked on a broad range of applications for these modeling and learning techniques including collaborative filtering and ranking, unsupervised structure discovery and feature induction, object recognition and image labeling, and natural language processing. My current applied research has a particular emphasis on machine learning for clinical data analysis and mobile on-body sensing.

Recent News:

[Feb 18, 2014] Invited to talk at the New England Machine Learning Day meeting this May 13th.
[Feb 17, 2014] Will be serving as an area chair for NIPS.
[Jan 27, 2014] Serving as the general co-chair of the 4th annual Meaningful Use of Complex Medical Data Symposium.
[Oct 28, 2013] Ran a hands-on programming activity for the UMass Women in Engineering and Computing Career Day.
[Sep 1, 2013] Took over as UMass CS Honors Program Director. See our new CS Honors Website.

Upcoming Papers and Abstracts:

[March 3, 2014] Addison Mayberry, Pan Hu, Benjamin Marlin, Christopher Salthouse and Deepak Ganesan. iShadow: Design of a Wearable, Real-Time Mobile Gaze Tracker. MobiSys 2014.
[March 3, 2014] Andrew Kae, Erik Learned-Miller and Benjamin Marlin. The Shape-Time Random Field for Semantic Video Labeling. CVPR 2014.
[March 3, 2014] G.A. Angarita, A. Nararajan, E. Gaiser, A. Parate, B. Marlin, R. R. Gueorguieva, D. Ganesan, R. T. Malison A Remote Wireless Sensor Network (RWSN)/Electrocardiographic (ECG) Approach to Discriminating Cocaine Use. CPDD 2014.

Recent Papers and Abstracts:

[July 3, 2013] Benjamin M. Marlin, Roy Adams, Rajani S. Sadasivam and Thomas K Houston. Towards Collaborative Filtering Recommender Systems for Tailored Health Communications. AMIA 2013.
[May 8, 2013] Annamalai Natarajan, Abhinav Parate, Gustavo Angarita, Edward Gaiser, Robert Malison, Deepak Ganesan and Benjamin Marlin. Detecting Cocaine Use with Wearable Electrocardiogram Sensors. UbiComp 2013.
[May 8, 2013] Abhinav Parate, Deepak Ganesan and Benjamin Marlin. Practical prediction and prefetch for faster access to applications on mobile phones. UbiComp 2013.
[Apr 3, 2013] Abhinav Parate, Meng-Chieh Chiu, Deepak Ganesan and Benjamin Marlin. Leveraging graphical models to improve accuracy and reduce privacy risks of mobile sensing. Proceedings of the 11th International Conference on Mobile Systems, Applications and Services. MobiSys 2013.
[Feb 19, 2013] Sebastian Riedel, Limin Yao, Andrew McCallum and Benjamin Marlin. Relation Extraction with Matrix Factorization and Universal Schemas. In Proceedings of NAACL 2013.
[Apr 21, 2012] M. E. Khan, S. Mohamed, B. Marlin, and K. Murphy. A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models. AIStats 2012 .

[Jan 28, 2012] Benjamin M. Marlin, Dave Kale, Robinder Khemani and Randall Wetzel. Unsupervised Pattern Discovery in Electronic Health Care Data using Probabilistic Clustering Models. 2nd ACM SIGHIT International Health Informatics Symposium
[June 1, 2011] Benjamin M. Marlin and Nando de Freitas. Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood. Proceedings of the The 27th Conference on Uncertainty in Artificial Intelligence.
[May 3, 2011] Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis and Malcolm Slaney. Recommender Systems: Missing Data and Statistical Model Estimation. Proceedings of the 22nd International Joint Conference on Artificial Intelligence. (Best papers track).
[May 3, 2011] Benjamin M. Marlin, Mohammad Emtiyaz Khan, and Kevin Murphy. Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. Proceedings of the 28th International Conference on Machine Learning.
[May 3, 2011] Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin and Nando de Freitas. On Autoencoders and Score Matching for Energy Based Models. Proceedings of the 28th International Conference on Machine Learning.
[May 3, 2011] David Duvenaud, Benjamin M. Marlin and Kevin Murphy. Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification. Proceedings of the Eighth Canadian Conference on Computer and Robot Vision, 2011.

PhD Thesis: My PhD thesis is titled Missing Data Problems in Machine Learning. It deals with the problem of unsupervised learning in the presence of non-random missing data, as well as the problem of classification in the presence of missing features. The work on non-random missing data is motivated by the problem of rating prediction in collaborative filtering, and uses a new data set collected at Yahoo! Research with Malcolm Slaney. The work on classification with missing features focuses on medical decision making using standard data sets, as well as higher dimensional tasks like digit classification with missing pixels. [Thesis Abstract] [Thesis PDF] [Short Defense Slides PDF] [Long Defense Slides PDF]

Contact Information:
marlin@cs.umass.edu
140 Governors Drive, Office 234
Amherst, MA 01003