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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. Thanks to recent awards from NSF and NIH, my current applied work is focusing on machine learning-based analytics for clinical and mobile health (mHealth) data. 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 Funded Projects

[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)

Recent Publications

Natarajan, Annamalai, Edward Gaiser, Gustavo Angarita, Robert Malison, Deepak Ganesan, and Benjamin Marlin. "Conditional Random Fields for Morphological Analysis of Wireless ECG Signals." Proceedings of the 5th Annual conference on Bioinformatics, Computational Biology and Health Informatics. Newport Beach, CA: ACM, 2014. Abstractcrf_bcb2014.pdf

Thanks to advances in mobile sensing technologies, it has recently become practical to deploy wireless electrocardiograph sensors for continuous recording of ECG signals. This capability has diverse applications in the study of human health and behavior, but to realize its full potential, new computational tools are required to effectively deal with the uncertainty that results from the noisy and highly non-stationary signals collected using these devices. In this work, we present a novel approach to the problem of extracting the morphological structure of ECG signals based on the use of dynamically structured conditional random field (CRF) models. We apply this framework to the problem of extracting morphological structure from wireless ECG sensor data collected in a lab-based study of habituated cocaine users. Our results show that the proposed CRF-based approach significantly out-performs independent prediction models using the same features, as well as a widely cited open source toolkit.

Mayberry, Addison, Pan Hu, Benjamin Marlin, Christopher Salthouse, and Deepak Ganesan. "iShadow: Design of a Wearable, Real-Time Mobile Gaze Tracker." 12th International Conference on Mobile Systems, Applications, and Services. 2014. ishadow_mobisys14.pdf
Kae, Andrew, Erik Learned-Miller, and Benjamin M. Marlin The Shape-Time Random Field for Semantic Video Labeling. 2014 IEEE Conference on Computer Vision and Pattern Recognition., 2014. Abstractstrf_cvpr14.pdf

We propose a novel discriminative model for semantic labeling in videos by incorporating a prior to model both the shape and temporal dependencies of an object in video. A typical approach for this task is the conditional random field (CRF), which can model local interactions among adjacent regions in a video frame. Recent work [16, 14] has shown how to incorporate a shape prior into a CRF for improving labeling performance, but it may be difficult to model temporal dependencies present in video by using this prior. The conditional restricted Boltzmann machine (CRBM) can model both shape and temporal dependencies, and has been used to learn walking styles from motion- capture data. In this work, we incorporate a CRBM prior into a CRF framework and present a new state-of-the-art model for the task of semantic labeling in videos. In particular, we explore the task of labeling parts of complex face scenes from videos in the YouTube Faces Database (YFDB). Our combined model outperforms competitive baselines both qualitatively and quantitatively.

Adams, Roy J., Rajani S. Sadasivam, Kavitha Balakrishnan, Rebecca L. Kinney, Thomas K. Houston, and Benjamin M. Marlin. "PERSPeCT: Collaborative Filtering for Tailored Health Communications." Proceedings of the 8th ACM Conference on Recommender Systems. RecSys '14. New York, NY, USA: ACM, 2014. 329-332. Abstractperspect-recsys14.pdf

The goal of computer tailored health communications (CTHC) is to elicit healthy behavior changes by sending motivational messages personalized to individual patients. One prominent weakness of many existing CTHC systems is that they are based on expert-written rules and thus have no ability to learn from their users over time. One solution to this problem is to develop CTHC systems based on the principles of collaborative filtering, but this approach has not been widely studied. In this paper, we present a case study evaluating nine rating prediction methods for use in the Patient Experience Recommender System for Persuasive Communication Tailoring, a system developed for use in a clinical trial of CTHC-based smoking cessation support interventions.

Natarajan, Annamalai, Abhinav Parate, Edward Gaiser, Gustavo Angarita, Robert Malison, Benjamin M. Marlin, and Deepak Ganesan. "Detecting cocaine use with wearable electrocardiogram sensors." UbiComp. 2013. 123-132. Abstractcocaine_ubicomp13_paper.pdf

Ubiquitous physiological sensing has the potential to profoundly improve our understanding of human behavior, leading to more targeted treatments for a variety of disorders. The long term goal of this work is development of novel computational tools to support the study of addiction in the context of cocaine use. The current paper takes the first step in this important direction by posing a simple, but crucial question: Can cocaine use be reliably detected using wearable electrocardiogram (ECG) sensors? The main contributions in this paper include the presentation of a novel clinical study of cocaine use, the development of a computational pipeline for inferring morphological features from noisy ECG waveforms, and the evaluation of feature sets for cocaine use detection. Our results show that 32mg/70kg doses of cocaine can be detected with the area under the receiver operating characteristic curve levels above 0.9 both within and between-subjects.

Parate, Abhinav, Matthias Böhmer, David Chu, Deepak Ganesan, and Benjamin M. Marlin. "Practical prediction and prefetch for faster access to applications on mobile phones." UbiComp. 2013. 275-284. Abstractprefetch_ubicomp13_paper.pdf

Mobile phones have evolved from communication devices to indispensable accessories with access to real-time content. The increasing reliance on dynamic content comes at the cost of increased latency to pull the content from the Internet before the user can start using it. While prior work has explored parts of this problem, they ignore the bandwidth costs of prefetching, incur significant training overhead, need several sensors to be turned on, and do not consider practical systems issues that arise from the limited background processing capability supported by mobile operating systems. In this paper, we make app prefetch practical on mobile phones. Our contributions are two-fold. First, we design an app prediction algorithm, APPM, that requires no prior training, adapts to usage dynamics, predicts not only which app will be used next but also when it will be used, and provides high accuracy without requiring additional sensor context. Second, we perform parallel prefetch on screen unlock, a mechanism that leverages the benefits of prediction while operating within the constraints of mobile operating systems. Our experiments are conducted on long-term traces, live deployments on the Android Play Market, and user studies, and show that we outperform prior approaches to predicting app usage, while also providing practical ways to prefetch application content on mobile phones.