Eric C. Larson

2.7k total citations
81 papers, 1.8k citations indexed

About

Eric C. Larson is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Eric C. Larson has authored 81 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 14 papers in Computer Vision and Pattern Recognition and 13 papers in Cognitive Neuroscience. Recurrent topics in Eric C. Larson's work include Network Security and Intrusion Detection (6 papers), Tactile and Sensory Interactions (6 papers) and Gaze Tracking and Assistive Technology (5 papers). Eric C. Larson is often cited by papers focused on Network Security and Intrusion Detection (6 papers), Tactile and Sensory Interactions (6 papers) and Gaze Tracking and Assistive Technology (5 papers). Eric C. Larson collaborates with scholars based in United States, Slovakia and Italy. Eric C. Larson's co-authors include Shwetak Patel, Mayank Goel, Margaret Rosenfeld, Damon M. Chandler, Jon E. Froehlich, Xinyi Ding, James Fogarty, Sean Liu, Sidhant Gupta and Sonya L. Heltshe and has published in prestigious journals such as The Journal of Chemical Physics, SHILAP Revista de lepidopterología and PEDIATRICS.

In The Last Decade

Eric C. Larson

71 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Eric C. Larson United States	24	414	292	266	227	217	81	1.8k
Yu Guan China	20	620 1.5×	134 0.5×	66 0.2×	250 1.1×	368 1.7×	81	1.8k
Javier Andreu-Pérez United Kingdom	18	359 0.9×	203 0.7×	97 0.4×	153 0.7×	458 2.1×	77	2.9k
Luís Paulo Reis Portugal	20	389 0.9×	132 0.5×	343 1.3×	41 0.2×	205 0.9×	291	2.1k
Dewar Finlay United Kingdom	22	359 0.9×	188 0.6×	72 0.3×	125 0.6×	486 2.2×	217	2.2k
Andreas Weber⋆ Germany	30	900 2.2×	440 1.5×	148 0.6×	57 0.3×	333 1.5×	144	3.3k
Charence Wong United Kingdom	10	597 1.4×	122 0.4×	70 0.3×	90 0.4×	511 2.4×	20	2.1k
P. W. C. Prasad Australia	23	590 1.4×	160 0.5×	142 0.5×	151 0.7×	235 1.1×	196	2.3k
Michael A. Riegler Norway	30	1.4k 3.4×	88 0.3×	94 0.4×	302 1.3×	263 1.2×	233	4.4k
AKM Azad Australia	19	272 0.7×	192 0.7×	45 0.2×	72 0.3×	122 0.6×	86	1.4k
Pieter Jonker Netherlands	22	446 1.1×	402 1.4×	143 0.5×	21 0.1×	166 0.8×	114	1.7k

Countries citing papers authored by Eric C. Larson

Since Specialization

Citations

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

Fields of papers citing papers by Eric C. Larson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eric C. Larson

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

Ketterlin–Geller, Leanne R., et al.. (2024). Enhancing middle school students’ computational thinking competency through game-based learning. Educational Technology Research and Development. 72(6). 3391–3419.

Shahoei, Hiva, et al.. (2024). Designing a photonic physically unclonable function having resilience to machine learning attacks. 22–22. 1 indexed citations

Shahoei, Hiva, et al.. (2024). A Photonic Physically Unclonable Function's Resilience to Multiple-Valued Machine Learning Attacks. 161–166. 1 indexed citations

Thornton, Mitchell A., et al.. (2024). Learnable Statistical Moments Pooling for Automatic Modulation Classification. 8981–8985. 1 indexed citations

Larson, Eric C., et al.. (2023). Towards Scalable Vocabulary Acquisition Assessment with BERT. 272–276.

Thornton, Mitchell A., et al.. (2023). Automatic Modulation Classification with Deep Neural Networks. Electronics. 12(18). 3962–3962. 8 indexed citations

Stothoff, S., et al.. (2023). Exploring Convolutional Neural Networks for Predicting Sentinel-C Backscatter Between Image Acquisitions. IEEE Transactions on Geoscience and Remote Sensing. 61. 1–16. 1 indexed citations

Larson, Eric C., et al.. (2023). A programmable true random number generator using commercial quantum computers. 7–7. 3 indexed citations

Ding, Xinyi, et al.. (2022). Smartphone camera oximetry in an induced hypoxemia study. npj Digital Medicine. 5(1). 146–146. 15 indexed citations

10.

Dai, Jessica, et al.. (2021). Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks. Journal of Robotic Surgery. 16(4). 917–925. 10 indexed citations

11.

Verma, Niraj, et al.. (2021). Generative adversarial networks for transition state geometry prediction. The Journal of Chemical Physics. 155(2). 24116–24116. 36 indexed citations

12.

Verma, Niraj, Francesco Trozzi, Peng Tao, et al.. (2021). SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction. International Journal of Molecular Sciences. 22(3). 1392–1392. 30 indexed citations

13.

Thornton, Mitchell A., et al.. (2021). SNR-Boosted Automatic Modulation Classification. 2021 55th Asilomar Conference on Signals, Systems, and Computers. 372–375. 3 indexed citations

14.

Taylor, Michael A., et al.. (2021). Industrial Control System Anomaly Detection Using Convolutional Neural Network Consensus. 693–700. 1 indexed citations

15.

Ding, Xinyi & Eric C. Larson. (2019). Why Deep Knowledge Tracing Has Less Depth than Anticipated.. Educational Data Mining. 8 indexed citations

16.

Clark, Karen, et al.. (2018). Open Cycle: Forecasting Ovulation for Family Planning. SMU Scholar (Southern Methodist University). 1(1). 2. 1 indexed citations

17.

Wheeler, Jessica, et al.. (2018). Bipolar Mania Eye Image Classification. SMU Scholar (Southern Methodist University). 1(1). 1. 6 indexed citations

18.

Abbott, Andrew, et al.. (2018). WalkNet: A Deep Learning Approach to Improving Sidewalk Quality and Accessibility. SMU Scholar (Southern Methodist University). 1(1). 7. 5 indexed citations

19.

Gupta, Sidhant, et al.. (2013). DopLink. 583–586. 77 indexed citations

20.

Jordan, David & Eric C. Larson. (2009). On the classification of certain fusion categories. Journal of Noncommutative Geometry. 3(3). 481–499. 14 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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