it me

Roy Adams


Assistant Professor


Johns Hopkins School of Medicine













About Me

I am an Assistant Professor in the Johns Hopkins School of Medicine working on machine learning and statistical methods for electronic health record data as part of the Richman Family Precision Medicine Center of Excellence. Prior to this, I spent three years as a postdoctoral fellow in the Machine Learing and Healthcare lab and the Malone Center for Engineering in Healthcare. I received my PhD in 2018 from University of Massachusetts, Amherst working in the Machine Learning for Data Sciences lab with Prof. Benjamin Marlin.

Research Interests

I am interested in devloping machine learning methods and tools to improve the study of chronic health conditions and improve the safety, efficiency, and efficacy of patient care. Though I am primarily motivated by applications in healthcare, my work addresses core methodological questions in measurement error modeling, causal inference, and model evaluation with broad implications for machine learning and other fields using observational data, such as economics and ecology.

Current Projects

Past Projects

In submission

* indicates co-first authorship

Roy Adams, Suchi Saria, and Michael Rosenblum, “The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods,” Biostatistics (In submission). preprint

Noam Finkelstein*, Roy Adams*, Ilya Shpitser, and Suchi Saria, “Partial Identifiability in Discrete Data with Measurement Error,” Conference on Artificial Intelligence and Statistics (In submission). preprint

Adarsh Subbaswamy*, Roy Adams*, and Suchi Saria, “Evaluating Model Robustness to Dataset Shift,” Conference on Artificial Intelligence and Statistics (In submission). preprint

Publications

Roy Adams, Yuelong Ji, Xiaobin Wang, and Suchi Saria, “Learning Models from Data with Measurement Error: Tackling Underreporting,” in International Conference on Machine Learning, 2019. (23% acceptance rate) paper

Roy Adams and Benjamin Marlin, “Learning Time Series Segmentation Models from Temporally Imprecise Labels,” in Conference on Uncertainty in Artificial Intelligence, 2018. (31% acceptance rate) paper

Rummana Bari, Roy Adams, Md. Mahbubur Rahman, Megan Battles Parsons, Eugene Buder, and Santosh Kumar, "rConverse: Moment by moment conversation detection using a mobile respiration sensor," Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, num. 1, pp. 1-27, March 2018. paper

Roy Adams and Benjamin Marlin, “Learning Time Series Detection Models from Temporally Noisy Labels,” in Conference on Artificial Intelligence and Statistics, 2017. (32% acceptance rate) paper code

Thai Nguyen, Roy Adams, Annamalai Natarajan, and Benjamin Marlin, “Parsing Wireless Electrocardiogram Signals with Context Free Grammar Conditional Random Fields,” in IEEE Wireless Health, 2016. paper

Rajani Sadasivam, Erin Borglund, Roy Adams, Benjamin Marlin, and Thomas Houstson. Journal of Medical Internet Research, "Impact of a collective intelligence tailored messaging system on smoking cessation: The PERSPeCT randomized experiment," Journal of Medical Internet Research, vol. 18, num. 11, e285:pp. 1-13, November 2016. paper

Roy Adams, 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,” in International Conference on Machine Learning, 2016. paper code

Roy Adams, Rajani Sadasivam, Kavitha Balakrishnan, Rebecca Kinney, Thomas Houston, and Benjamin Marlin, “PERSPeCT: Collaborative Filtering for Tailored Health Communications,” in ACM Conference on Recommender Systems, 2014. (24% acceptance rate)

Benjamin Marlin, Roy Adams, Rajani, Sadasivam, and Thomas Houstson, “Towards Collaborative Filtering Recommender Systems for Tailored Health Communications,” in American Medical Informatics Association Annual Symposium, 2013. paper

Refereed Workshop Presentations

Thai Nguyen, Roy Adams, Annamalai Natarajan, and Benjamin Marlin, “Parsing Wireless Electrocardiogram Signals with the CRF-CFG Model,” UAI Workshop: Machine Learning for Health, 2016.

Roy Adams, Edison Thomaz, and Benjamin Marlin, “Hierarchical Nested CRFs for Segmentation and Labeling of Physiological Time Series,” NeurIPS Workshop: Machine Learning for Healthcare, 2015.

Teaching

Previous courses

COMPSCI 590N: Introduction to Numerical Computing with Python

Work Experience

Postdoctoral Researcher, Johns Hopkins University, 2018 to present.
Research Assistant, University of Massachusetts, 2012 to 2018.
Research Intern, Yahoo, Summer 2015
Software Engineering Intern, Hewlett-Packard, Summer 2012
Summer REU, Eco-Informatics Summer Institute, Oregon State University, Summer 2011
Student Programmer, Oregon State University, Summer 2010

Education

PhD in Computer Science, University of Massachusetts, 2018.
MS in Computer Science, University of Massachusetts, 2015.
BS with honors in Computer Science Engineering, University of California - Davis, 2011.