Michael C. Hughes

2.2k total citations
62 papers, 928 citations indexed

About

Michael C. Hughes is a scholar working on Artificial Intelligence, Oncology and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Michael C. Hughes has authored 62 papers receiving a total of 928 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 10 papers in Oncology and 7 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Michael C. Hughes's work include Metal complexes synthesis and properties (8 papers), Bayesian Methods and Mixture Models (7 papers) and Magnetism in coordination complexes (6 papers). Michael C. Hughes is often cited by papers focused on Metal complexes synthesis and properties (8 papers), Bayesian Methods and Mixture Models (7 papers) and Magnetism in coordination complexes (6 papers). Michael C. Hughes collaborates with scholars based in United States, United Kingdom and Canada. Michael C. Hughes's co-authors include Daniel J. Macero, Erik B. Sudderth, Lawrence D. Phillips, Emily B. Fox, Finale Doshi‐Velez, Michael I. Jordan, Charles D. Schaeffer, Mary Sohn, J. J. Zuckerman and Marzyeh Ghassemi and has published in prestigious journals such as Blood, Bioinformatics and Journal of Applied Physics.

In The Last Decade

Michael C. Hughes

59 papers receiving 844 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael C. Hughes United States	19	217	146	107	101	98	62	928
Xing Yang China	17	80 0.4×	27 0.2×	83 0.8×	121 1.2×	118 1.2×	109	945
Soumik Mandal India	15	414 1.9×	105 0.7×	135 1.3×	124 1.2×	36 0.4×	43	1.1k
Takashi Omori Japan	22	174 0.8×	47 0.3×	128 1.2×	352 3.5×	11 0.1×	257	2.4k
Yashu Liu China	15	60 0.3×	52 0.4×	20 0.2×	91 0.9×	84 0.9×	42	650
Giovanni V. Sebastiani Italy	21	89 0.4×	53 0.4×	612 5.7×	213 2.1×	15 0.2×	102	1.6k
Jordán United States	16	126 0.6×	102 0.7×	144 1.3×	69 0.7×	94 1.0×	81	962
Samuel Smith United States	24	272 1.3×	125 0.9×	59 0.6×	177 1.8×	24 0.2×	74	2.8k
Haiyan Yu China	17	144 0.7×	45 0.3×	47 0.4×	77 0.8×	19 0.2×	86	878
Atsuko Yamaguchi Japan	20	91 0.4×	33 0.2×	99 0.9×	71 0.7×	53 0.5×	115	1.6k
Yuting Hu China	20	66 0.3×	54 0.4×	58 0.5×	125 1.2×	15 0.2×	71	1.1k

Countries citing papers authored by Michael C. Hughes

Since Specialization

Citations

This map shows the geographic impact of Michael C. Hughes'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 Michael C. Hughes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael C. Hughes more than expected).

Fields of papers citing papers by Michael C. Hughes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael C. Hughes. 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 Michael C. Hughes. The network helps show where Michael C. Hughes may publish in the future.

Co-authorship network of co-authors of Michael C. Hughes

This figure shows the co-authorship network connecting the top 25 collaborators of Michael C. Hughes. A scholar is included among the top collaborators of Michael C. Hughes 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 Michael C. Hughes. Michael C. Hughes 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

Hughes, Michael C., et al.. (2024). Virtual sensing via Gaussian Process for bending moment response prediction of an offshore wind turbine using SCADA data. Renewable Energy. 227. 120466–120466. 9 indexed citations

Cheng, Kevin C., Eric L. Miller, Michael C. Hughes, & Shuchin Aeron. (2023). Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations. IEEE Transactions on Signal Processing. 71. 3164–3178. 1 indexed citations

Wessler, Benjamin S., et al.. (2023). Automated Detection of Aortic Stenosis Using Machine Learning. Journal of the American Society of Echocardiography. 36(4). 411–420. 16 indexed citations

Kent, David M., et al.. (2023). Performance metrics for models designed to predict treatment effect. BMC Medical Research Methodology. 23(1). 165–165. 9 indexed citations

Wang, Liang, et al.. (2021). Taming fNIRS-based BCI Input for Better Calibration and Broader Use. 179–197. 6 indexed citations

Hughes, Michael C., Melanie F. Pradier, Andrew Slavin Ross, et al.. (2020). Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models. JAMA Network Open. 3(5). e205308–e205308. 14 indexed citations

Hughes, Michael C., et al.. (2020). Hierarchical Classification of Enzyme Promiscuity Using Positive, Unlabeled, and Hard Negative Examples.. arXiv (Cornell University). 1 indexed citations

Pradier, Melanie F., Michael C. Hughes, Thomas H. McCoy, et al.. (2020). Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation. Neuropsychopharmacology. 46(2). 455–461. 18 indexed citations

Pradier, Melanie F., Thomas H. McCoy, Michael C. Hughes, Roy H. Perlis, & Finale Doshi‐Velez. (2020). Predicting treatment dropout after antidepressant initiation. Translational Psychiatry. 10(1). 60–60. 21 indexed citations

10.

Nestor, Bret, Matthew B. A. McDermott, Willie Boag, et al.. (2019). Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. 381–405. 5 indexed citations

11.

Hughes, Michael C., et al.. (2018). Semi-Supervised Prediction-Constrained Topic Models. International Conference on Artificial Intelligence and Statistics. 1067–1076. 6 indexed citations

12.

Kim, Daeil, et al.. (2017). Refinery: an open source topic modeling web platform. Journal of Machine Learning Research. 18(1). 382–386. 1 indexed citations

13.

Ji, Geng, Michael C. Hughes, & Erik B. Sudderth. (2017). From Patches to Images: A Nonparametric Generative Model.. International Conference on Machine Learning. 1675–1683. 2 indexed citations

14.

Hughes, Michael C., William Stephenson, & Erik B. Sudderth. (2015). Scalable adaptation of state complexity for nonparametric hidden Markov models. Neural Information Processing Systems. 28. 1198–1206. 11 indexed citations

15.

Hughes, Michael C., et al.. (2015). Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. International Conference on Artificial Intelligence and Statistics. 370–378. 12 indexed citations

16.

Hughes, Michael C. & Erik B. Sudderth. (2013). Memoized Online Variational Inference for Dirichlet Process Mixture Models. Neural Information Processing Systems. 26. 1133–1141. 44 indexed citations

17.

Hughes, Michael C., Emily B. Fox, & Erik B. Sudderth. (2012). Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. Neural Information Processing Systems. 25. 1295–1303. 22 indexed citations

18.

Hughes, Michael C. & Erik B. Sudderth. (2012). Nonparametric discovery of activity patterns from video collections. 24. 25–32. 9 indexed citations

19.

Hughes, Michael C., et al.. (1990). Characterization of sulfuric acid proton-exchanged lithium niobate. Journal of Applied Physics. 67(2). 627–633. 17 indexed citations

20.

Priemer, Roland & Michael C. Hughes. (1974). On sub-optimal fixed point and fixed lag smoothing in non-linear systems. International Journal of Control. 19(6). 1117–1127. 1 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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