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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 Publications

Adams, Roy J., and Benjamin M. Marlin. "Learning Time Series Detection Models from Temporally Imprecise Labels." The 20th International Conference on Artificial Intelligence and Statistics. 2017. Abstract

In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class events. Such temporally imprecise labels commonly occur in areas like mobile health research where human annotators are tasked with labeling the occurrence of very short duration events. We propose a general learning framework for this problem that can accommodate different base classifiers and noise models. We present results on real mobile health data showing that the proposed framework significantly outperforms a number of alternatives including assuming that the label time stamps are noise-free, transforming the problem into the multiple instance learning framework, and learning on labels that were manually re-aligned.

Sadasivam, Rajani Shankar, Sarah L. Cutrona, Rebecca L. Kinney, Benjamin M. Marlin, Kathleen M. Mazor, Stephenie C. Lemon, and Thomas K. Houston. "Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century." Journal of Medical Internet Research. 18.3 (2016). AbstractFull Text

What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC.

Adams, Roy, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, and Benjamin Marlin. "Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams." International Conference on Machine Learning. 2016. Abstract

The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP inference in quadratic time via dynamic programming, which we leverage to perform learning in the structured support vector machine framework. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance at the p=0.005 level relative to a hierarchical pairwise CRF.

Jacek, Nicholas, Meng-Chieh Chiu, Benjamin Marlin, and Eliot J. B. Moss. "Assessing the Limits of Program-Specific Garbage Collection Performance." Programming Language Design and Implementation. 2016. Abstract

Distinguished Paper Award

We consider the ultimate limits of program-specific garbage collector performance for real programs. We first characterize the GC schedule optimization problem using Markov Decision Processes (MDPs). Based on this characterization, we develop a method of determining, for a given program run and heap size, an optimal schedule of collections for a non-generational collector. We further explore the limits of performance of a generational collector, where it is not feasible to search the space of schedules to prove optimality. Still, we show significant improvements with Least Squares Policy Iteration, a reinforcement learning technique for solving MDPs. We demonstrate that there is considerable promise to reduce garbage collection costs by developing program-specific collection policies.

Dadkhahi, Hamid, Nazir Saleheen, Santosh Kumar, and Benjamin Marlin. "Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications." ICML On Device Intelligence Workshop. 2016. Abstract

The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-line setting. In this paper, we propose a novel approach to learning shallow detection cascades developed explicitly for use in a real-time wearable-phone or wearable-phone-cloud systems. We apply our approach to the problem of cigarette smoking detection from a combination of wrist-worn actigraphy data and respiration chest band data using two and three stage cascades.

Nguyen, Thai, Roy J. Adams, Annamalai Natarajan, and Benjamin M. Marlin Parsing Wireless Electrocardiogram Signals with the CRF-CFG Model. Conference on Uncertainty in Artificial Intelligence Machine Learning for Health Workshop., 2016. Abstract

Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals as they go about their daily activities in natural environments. However, extracting reliable higher-level inferences from these raw data streams remains a key data analysis challenge. In this paper, we focus on the specific case of the analysis of data from wireless electrocardiogram (ECG) sensors. We present a new robust probabilistic approach to ECG morphology extraction using conditional random field context free grammar models, which have traditionally been applied to parsing problems in natural language processing. We introduce a robust context free grammar for parsing noisy ECG data, and show significantly improved performance on the ECG morphological labeling task.

Chiu, Meng-Chieh, Benjamin Marlin, and Eliot Moss. "Real-Time Program-Specific Phase Change Detection for Java Programs." 13th International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machines, Languages, and Tools. 2016. Abstract

It is well-known that programs tend to have multiple phases in their execution. Because phases have impact on micro-architectural features such as caches and branch predictors, they are relevant to program performance and energy consumption. They are also relevant to detecting whether a program is executing as expected or is encountering unusual or exceptional conditions, a software engineering and program monitoring concern. We offer here a method for real-time phase change detection in Java programs. After applying a training protocol to a program of interest, our method can detect phase changes at run time for that program with good precision and recall (compared with a “ground truth” definition of phases) and with small performance impact (average less than 2%). We also offer improved methodology for evaluating phase change detection mechanisms. In sum, our approach offers the first known implementation of real-time phase detection for Java programs.

Nguyen, Thai, Roy J. Adams, Annamalai Natarajan, and Benjamin M. Marlin. "Parsing Wireless Electrocardiogram Signals with Context Free Grammar Conditional Random Fields." IEEE Wireless Health. 2016. Abstract

Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals as they go about their daily activities in natural environments. However, extracting reliable higher-level inferences from these raw data streams remains a key data analysis challenge. In this paper, we focus on the specific case of the analysis of data from wireless electrocardiogram (ECG) sensors. We present a new robust probabilistic approach to ECG morphology extraction using conditional random field context free grammar models, which have traditionally been applied to parsing problems in natural language processing. We introduce a robust context free grammar for parsing noisy ECG data, and show significantly improved performance on the ECG morphological labeling task.

Recent Funded Projects

[2016-2017] Improved Systems for Real-World Pervasive Human Sensing (with Deepak Ganesan, PI. DCS Cor. prime to ARL.).

[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)