Jordan Matelsky

543 total citations
14 papers, 92 citations indexed

About

Jordan Matelsky is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biophysics. According to data from OpenAlex, Jordan Matelsky has authored 14 papers receiving a total of 92 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 5 papers in Biophysics. Recurrent topics in Jordan Matelsky's work include Cell Image Analysis Techniques (5 papers), Scientific Computing and Data Management (4 papers) and Machine Learning in Healthcare (2 papers). Jordan Matelsky is often cited by papers focused on Cell Image Analysis Techniques (5 papers), Scientific Computing and Data Management (4 papers) and Machine Learning in Healthcare (2 papers). Jordan Matelsky collaborates with scholars based in United States, Canada and Spain. Jordan Matelsky's co-authors include William Gray-Roncal, Brock A. Wester, Erik C. Johnson, Christopher G. Chute, Xiaohan Zhang, Ilya Shpitser, Casey Overby Taylor, Gregory Kiar, Luis M. Rodríguez and Jennifer Stiso and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Nature Methods.

In The Last Decade

Jordan Matelsky

12 papers receiving 90 citations

Peers

Jordan Matelsky
William Gray-Roncal United States
Dean M. Kleissas United States
Derek Houghton United Kingdom
Kunal Lillaney United States
Aditeya Pandey United States
Jakob Troidl United States
Pedro L. López-Cruz Spain
Pierre Rioux Canada
Tiago Azevedo United Kingdom
Andrei Kapishnikov United States
William Gray-Roncal United States
Jordan Matelsky
Citations per year, relative to Jordan Matelsky Jordan Matelsky (= 1×) peers William Gray-Roncal

Countries citing papers authored by Jordan Matelsky

Since Specialization
Citations

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

Fields of papers citing papers by Jordan Matelsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jordan Matelsky. 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 Jordan Matelsky. The network helps show where Jordan Matelsky may publish in the future.

Co-authorship network of co-authors of Jordan Matelsky

This figure shows the co-authorship network connecting the top 25 collaborators of Jordan Matelsky. A scholar is included among the top collaborators of Jordan Matelsky 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 Jordan Matelsky. Jordan Matelsky is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
Joshi, Saurabh, Kyu Sang Han, Florin M. Selaru, et al.. (2025). InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping. Nature Methods. 22(7). 1556–1567. 1 indexed citations
2.
Martínez, H., et al.. (2025). Using BossDB Tools to Access, Visualize, and Share Volumetric Neuroscience Data. Current Protocols. 5(10). e70247–e70247.
3.
Matelsky, Jordan, et al.. (2024). Computational kinematics of dance: distinguishing hip hop genres. Frontiers in Robotics and AI. 11. 1295308–1295308. 4 indexed citations
4.
5.
Matelsky, Jordan, et al.. (2023). Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients. Scientific Reports. 13(1). 4 indexed citations
6.
Troidl, Jakob, Jinhan Choi, Jordan Matelsky, et al.. (2023). ViMO - Visual Analysis of Neuronal Connectivity Motifs. IEEE Transactions on Visualization and Computer Graphics. 30(1). 748–758. 5 indexed citations
7.
Kleissas, Dean M., Jordan Matelsky, Luis M. Rodríguez, et al.. (2022). The Brain Observatory Storage Service and Database (BossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery. Frontiers in Neuroinformatics. 16. 828787–828787. 13 indexed citations
8.
Matelsky, Jordan, Erik C. Johnson, Jennifer Stiso, et al.. (2021). DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries. Scientific Reports. 11(1). 13045–13045. 14 indexed citations
9.
Matelsky, Jordan, et al.. (2021). An Integrated Toolkit for Extensible and Reproducible Neuroscience. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. 2413–2418. 3 indexed citations
10.
Zhang, Xiaohan, et al.. (2020). Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance. PLoS ONE. 15(4). e0231300–e0231300. 27 indexed citations
11.
Matelsky, Jordan, et al.. (2020). A substrate for modular, extensible data-visualization. SHILAP Revista de lepidopterología. 5(1). 5 indexed citations
12.
Johnson, Erik C., Luis M. Rodríguez, Corban G. Rivera, et al.. (2020). Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets. GigaScience. 9(12). 3 indexed citations
13.
Matelsky, Jordan, et al.. (2018). Container-Based Clinical Solutions for Portable and Reproducible Image Analysis. Journal of Digital Imaging. 31(3). 315–320. 10 indexed citations
14.

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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2026