Bryan Conroy

1.9k total citations
26 papers, 1.2k citations indexed

About

Bryan Conroy is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Epidemiology. According to data from OpenAlex, Bryan Conroy has authored 26 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cognitive Neuroscience, 8 papers in Artificial Intelligence and 6 papers in Epidemiology. Recurrent topics in Bryan Conroy's work include Neural dynamics and brain function (5 papers), Sepsis Diagnosis and Treatment (5 papers) and Machine Learning in Healthcare (4 papers). Bryan Conroy is often cited by papers focused on Neural dynamics and brain function (5 papers), Sepsis Diagnosis and Treatment (5 papers) and Machine Learning in Healthcare (4 papers). Bryan Conroy collaborates with scholars based in United States, Italy and Finland. Bryan Conroy's co-authors include Peter J. Ramadge, James V. Haxby, Saman Parvaneh, Asif Rahman, J. Swaroop Guntupalli, Cristhian Potes, Andrew C. Connolly, Yaroslav O. Halchenko, Michael Hanke and M. Ida Gobbini and has published in prestigious journals such as Neuron, PLoS ONE and NeuroImage.

In The Last Decade

Bryan Conroy

23 papers receiving 1.2k citations

Peers

Bryan Conroy

CCM

RNMI

PRM

Ali Khazaee Iran

Mahmoud Hassan France

Miika Koskinen Finland

Hui Wen Loh Singapore

Ahmad Shalbaf Iran

Marjane Khodatars Iran

Palani Thanaraj Krishnan India

Do‐Un Jeong South Korea

Yisheng Zhu China

Mahboobeh Jafari Australia

Ali Khazaee Iran

Bryan Conroy

855 ×1.1

383 ×1.5

CCM

241 ×1.1

RNMI

79 ×0.4

PRM

155 ×1.4

Citations per year, relative to Bryan Conroy Bryan Conroy (= 1×) peers Ali Khazaee

Countries citing papers authored by Bryan Conroy

Since Specialization

Citations

This map shows the geographic impact of Bryan Conroy's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bryan Conroy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bryan Conroy more than expected).

Fields of papers citing papers by Bryan Conroy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bryan Conroy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bryan Conroy. The network helps show where Bryan Conroy may publish in the future.

Co-authorship network of co-authors of Bryan Conroy

This figure shows the co-authorship network connecting the top 25 collaborators of Bryan Conroy. A scholar is included among the top collaborators of Bryan Conroy based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bryan Conroy. Bryan Conroy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Feng, Ting, et al.. (2025). An infection prediction model developed from inpatient data can predict out-of-hospital COVID-19 infections from wearable data when controlled for dataset shift. Scientific Reports. 15(1). 33367–33367.

Wang, Chunxue, et al.. (2025). Wearable-derived short sleep duration is associated with higher C-reactive protein in a placebo-controlled vaccine trial among young adults. Scientific Reports. 15(1). 10501–10501.

Wang, Chunxue, Sara Mariani, Soheil Borhani, et al.. (2024). A randomized, triple-blinded, placebo-controlled clinical trial evaluating immune responses of Typhim Vi and PPSV23 vaccines in healthy adults: The PREP study. Vaccine. 42(26). 126292–126292. 1 indexed citations

Feng, Ting, David P. Noren, Sara Mariani, et al.. (2023). Machine learning-based clinical decision support for infection risk prediction. Frontiers in Medicine. 10. 1213411–1213411. 4 indexed citations

Conroy, Bryan, Golbarg Mehraei, Robert Damiano, et al.. (2022). Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19. Scientific Reports. 12(1). 3797–3797. 33 indexed citations

Dong, Junzi, Minnan Xu-Wilson, Bryan Conroy, Robinder G. Khemani, & Christopher J. L. Newth. (2022). Continuous noninvasive blood gas estimation in critically ill pediatric patients with respiratory failure. Scientific Reports. 12(1). 9853–9853.

Rahman, Asif, Yale Chang, Bryan Conroy, & Minnan Xu-Wilson. (2020). Phenotyping with Prior Knowledge using Patient Similarity. 331–351. 1 indexed citations

Rubin, Jonathan, Saman Parvaneh, Asif Rahman, Bryan Conroy, & Saeed Babaeizadeh. (2018). Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings. Journal of Electrocardiology. 51(6). S18–S21. 74 indexed citations

Parvaneh, Saman, Jonathan Rubin, Asif Rahman, Bryan Conroy, & Saeed Babaeizadeh. (2018). Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation. Physiological Measurement. 39(8). 84003–84003. 50 indexed citations

10.

Conroy, Bryan, Minnan Xu-Wilson, & Asif Rahman. (2017). Patient Similarity Using Population Statistics and Multiple Kernel Learning. 191–203. 1 indexed citations

11.

Muraskin, Jordan, Truman R. Brown, Jennifer M. Walz, et al.. (2017). A multimodal encoding model applied to imaging decision-related neural cascades in the human brain. NeuroImage. 180(Pt A). 211–222. 21 indexed citations

12.

Potes, Cristhian, Bryan Conroy, Minnan Xu-Wilson, et al.. (2017). A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit. Critical Care. 21(1). 282–282. 15 indexed citations

13.

Potes, Cristhian, Saman Parvaneh, Asif Rahman, & Bryan Conroy. (2016). Ensemble of Feature:based and Deep learning:based Classifiers for Detection of Abnormal Heart Sounds. Computing in cardiology. 43. 216 indexed citations

14.

Conroy, Bryan, Benjamin D. Singer, J. Swaroop Guntupalli, Peter J. Ramadge, & James V. Haxby. (2013). Inter-subject alignment of human cortical anatomy using functional connectivity. NeuroImage. 81. 400–411. 74 indexed citations

15.

Conroy, Bryan, Jennifer M. Walz, & Paul Sajda. (2013). Fast Bootstrapping and Permutation Testing for Assessing Reproducibility and Interpretability of Multivariate fMRI Decoding Models. PLoS ONE. 8(11). e79271–e79271. 8 indexed citations

16.

Conroy, Bryan & Paul Sajda. (2012). Fast, Exact Model Selection and Permutation Testing for l2-Regularized Logistic Regression. International Conference on Artificial Intelligence and Statistics. 246–254. 13 indexed citations

17.

Haxby, James V., J. Swaroop Guntupalli, Andrew C. Connolly, et al.. (2011). A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex. Neuron. 72(2). 404–416. 420 indexed citations

18.

Conroy, Bryan & Peter J. Ramadge. (2011). The grouped two-sided orthogonal Procrustes problem. 3688–3691. 1 indexed citations

19.

Sabuncu, Mert R., et al.. (2009). Function-based Intersubject Alignment of Human Cortical Anatomy. Cerebral Cortex. 20(1). 130–140. 121 indexed citations

20.

Conroy, Bryan, Benjamin D. Singer, James V. Haxby, & Peter J. Ramadge. (2009). fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity.. PubMed. 22. 378–386. 31 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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