I am an associate professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst where I co-direct the Machine Learning for Data Science Lab with Brendan O’Connor 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 dynamic programming, Markov Chain Monte Carlo and variational Bayesian methods. My current research has a particular emphasis on models and algorithms for multivariate time series data and explores both probabilistic and neural network-based models and their combination.

Thanks to awards from ARL, IARPA, NSF and NIH, my current application focus is on machine learning-based analytics for mobile and wearable sensor data, as well as electronic health records data. I am also interested in large-scale, real-time, heterogeneous distributed machine learning systems that bridge mobile and embedded computing with cloud-based systems including distributed prediction cascades and distributed real-time active learning. My research group collaborates widely with researchers in mobile and distributed computing, mobile health, behavioral science, and medicine.

In the past, I have worked on a broad range of applications including collaborative filtering and ranking, unsupervised structure discovery and feature induction, object recognition and image labeling, and natural language processing, and I continue to consult on projects in these areas.

Recent Publications

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Shukla, Satya Narayan; Marlin, Benjamin

Interpolation-Prediction Networks for Irregularly Sampled Time Series Conference Forthcoming

Seventh International Conference on Learning Representations, Forthcoming.

Abstract | BibTeX

Li, Steven Cheng-Xian; Jiang, Bo; Marlin, Benjamin

Learning from Incomplete Data with Generative Adversarial Networks Conference Forthcoming

Seventh International Conference on Learning Representations, Forthcoming.

Abstract | BibTeX

Jacek, Nicholas; Chiu, Meng-Chieh; Marlin, Benjamin M; Moss, Eliot J B

Optimal Choice of When to Garbage Collect Journal Article Forthcoming

ACM Transactions on Programming Languages and Systems, Forthcoming.

Abstract | BibTeX


Adams, Roy; Marlin, Benjamin M

Learning Time Series Segmentation Models from Temporally Imprecise Labels Conference


Abstract | Links | BibTeX


Kumar, Santosh; others,

Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) Journal Article

IEEE Pervasive Computing, 16 (2), pp. 18–22, 2017.

Abstract | Links | BibTeX

58 entries « 1 of 12 »

Funded Projects

[2017-2020] Enhancing Context-Awareness and Personalization for Intensively Adaptive Smoking Cessation Messaging Interventions. See NSF award listing.
[2017-2022] Alliance for IoBT Research on Evolving Intelligent Goal-driven Networks (IoBT-REIGN) (with Prashant Shenoy, UMass PI. UIUC prime to ARL.). See ARL and UMass Amherst press releases, and the IoBT website.
[2017-2020]  mPerf: A Theory-driven Approach to Model and Predict Everyday Job Performance Using Mobile Sensors (with Deepak Ganesan, UMass PI. U. Memphis prime to IARPA). See project website.
[2014-2018] Center of Excellence for Mobile Sensor Data to Knowledge (with Santosh Kumar, U. Memphis, PI). See center website.
[2014-2019]. NSF CAREER: Machine Learning for Complex Health Data Analytics.
[2013-2016] Accurate and Computationally Efficient Predictors of Java Memory Resource Consumption (with Eliot Moss, PI).
[2012-2015]  SensEye: An Architecture for Ubiquitous, Real-Time Visual Context Sensing and Inference (with Deepak Ganesan, PI).
[2012-2015]  Patient Experience Recommender System for Persuasive Communication Tailoring (with Tom Houston, UMMS, PI).
[2012-2014] Foresight and Understanding from Scientific Exposition (With Andrew McCallum, PI and Raytheon BBN Technologies)