Guy Rosman

4.5k total citations · 1 hit paper
85 papers, 2.5k citations indexed

About

Guy Rosman is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Guy Rosman has authored 85 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computer Vision and Pattern Recognition, 22 papers in Artificial Intelligence and 21 papers in Automotive Engineering. Recurrent topics in Guy Rosman's work include Autonomous Vehicle Technology and Safety (21 papers), Advanced Vision and Imaging (14 papers) and Surgical Simulation and Training (11 papers). Guy Rosman is often cited by papers focused on Autonomous Vehicle Technology and Safety (21 papers), Advanced Vision and Imaging (14 papers) and Surgical Simulation and Training (11 papers). Guy Rosman collaborates with scholars based in United States, Israel and Canada. Guy Rosman's co-authors include Ozanan R. Meireles, Daniel A. Hashimoto, Daniela Rus, Elan R. Witkowski, Ron Kimmel, Lei Gao, Thomas M. Ward, Yutong Ban, John J. Leonard and John W. Fisher and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Annals of Surgery.

In The Last Decade

Guy Rosman

79 papers receiving 2.4k citations

Hit Papers

Artificial Intelligence in Surgery: Promises and Perils 2018 2026 2020 2023 2018 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guy Rosman United States 22 888 609 543 456 363 85 2.5k
Stefanie Speidel Germany 26 948 1.1× 173 0.3× 894 1.6× 783 1.7× 281 0.8× 126 2.4k
Petia Radeva Spain 37 692 0.8× 88 0.1× 1.9k 3.5× 678 1.5× 1.1k 3.1× 270 4.7k
Chee‐Kong Chui Singapore 31 625 0.7× 110 0.2× 1.2k 2.1× 1.2k 2.7× 390 1.1× 202 3.9k
Santu Rana Australia 20 184 0.2× 140 0.2× 131 0.2× 262 0.6× 549 1.5× 85 2.1k
Nicolas Padoy France 29 1.8k 2.1× 444 0.7× 790 1.5× 1.1k 2.4× 345 1.0× 124 3.1k
Serena Yeung United States 18 272 0.3× 242 0.4× 1.4k 2.6× 501 1.1× 1.0k 2.8× 47 2.8k
Mozziyar Etemadi United States 26 927 1.0× 315 0.5× 131 0.2× 2.0k 4.4× 494 1.4× 73 3.7k
Roohallah Alizadehsani Australia 42 113 0.1× 273 0.4× 576 1.1× 418 0.9× 1.9k 5.3× 169 5.5k
Leo Joskowicz Israel 32 1.2k 1.3× 67 0.1× 1.0k 1.9× 1.2k 2.5× 431 1.2× 225 3.9k
Salvatore Vitabile Italy 27 192 0.2× 66 0.1× 794 1.5× 174 0.4× 450 1.2× 154 3.4k

Countries citing papers authored by Guy Rosman

Since Specialization
Citations

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

Fields of papers citing papers by Guy Rosman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guy Rosman

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

All Works

20 of 20 papers shown
1.
Ban, Yutong, et al.. (2024). Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models. 6687–6694. 15 indexed citations
2.
Drews, Paul, et al.. (2024). Online Adaptation of Learned Vehicle Dynamics Model with Meta-Learning Approach. arXiv (Cornell University). 802–809. 1 indexed citations
3.
Li, Xiao, Igor Gilitschenski, Guy Rosman, Sertaç Karaman, & Daniela Rus. (2023). Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving. IEEE Robotics and Automation Letters. 8(8). 4737–4744.
4.
Ban, Yutong, Guy Rosman, Dolores T. Müller, et al.. (2023). TEsoNet: knowledge transfer in surgical phase recognition from laparoscopic sleeve gastrectomy to the laparoscopic part of Ivor–Lewis esophagectomy. Surgical Endoscopy. 37(5). 4040–4053. 12 indexed citations
5.
Cao, Zhangjie, Erdem Bıyık, Guy Rosman, & Dorsa Sadigh. (2022). Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction. 2022 International Conference on Robotics and Automation (ICRA). 10723–10730. 5 indexed citations
6.
Li, Xiao, Guy Rosman, Igor Gilitschenski, et al.. (2021). Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features. IEEE Robotics and Automation Letters. 6(2). 3459–3466. 40 indexed citations
7.
Huang, Xin, Stephen G. McGill, Jonathan DeCastro, et al.. (2021). CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy. IEEE Robotics and Automation Letters. 6(3). 4433–4440. 5 indexed citations
8.
Li, Xiao, Guy Rosman, Igor Gilitschenski, et al.. (2021). Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN). IEEE Robotics and Automation Letters. 7(2). 984–991. 5 indexed citations
9.
Meireles, Ozanan R., Guy Rosman, Maria S. Altieri, et al.. (2021). SAGES consensus recommendations on an annotation framework for surgical video. Surgical Endoscopy. 35(9). 4918–4929. 60 indexed citations
10.
Gilitschenski, Igor, et al.. (2020). Deep Context Maps: Agent Trajectory Prediction Using Location-Specific Latent Maps. IEEE Robotics and Automation Letters. 5(4). 5097–5104. 10 indexed citations
11.
Huang, Xin, Stephen G. McGill, Jonathan DeCastro, et al.. (2020). DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling. IEEE Robotics and Automation Letters. 5(4). 5089–5096. 49 indexed citations
12.
Li, Xiao, Guy Rosman, Igor Gilitschenski, et al.. (2020). Differentiable Logic Layer for Rule Guided Trajectory Prediction. 2178–2194. 2 indexed citations
13.
Hashimoto, Daniel A., Elan R. Witkowski, Lei Gao, Ozanan R. Meireles, & Guy Rosman. (2019). Artificial Intelligence in Anesthesiology. Anesthesiology. 132(2). 379–394. 290 indexed citations
14.
McGill, Stephen G., Guy Rosman, Alyssa Pierson, et al.. (2019). Probabilistic Risk Metrics for Navigating Occluded Intersections. IEEE Robotics and Automation Letters. 4(4). 4322–4329. 24 indexed citations
15.
Huang, Xin, Stephen G. McGill, Brian Williams, Luke Fletcher, & Guy Rosman. (2019). Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections. 9718–9724. 49 indexed citations
16.
Rosman, Guy, Daniela Rus, & John W. Fisher. (2016). Information-Driven Adaptive Structured-Light Scanners. 874–883. 8 indexed citations
17.
Rosman, Guy, et al.. (2015). Coresets for visual summarization with applications to loop closure. 3638–3645. 14 indexed citations
18.
Rosman, Guy, et al.. (2014). Coresets for k-Segmentation of Streaming Data. DSpace@MIT (Massachusetts Institute of Technology). 27. 559–567. 24 indexed citations
19.
Rosman, Guy, et al.. (2009). On reconstruction of non-rigid shapes with intrinsic regularization. 272–279. 6 indexed citations
20.
Hullett, J.L., Hung Q. Doan, & Guy Rosman. (1975). A Modified Receiver for Optical Transmission Systems. IRE Transactions on Communications Systems. 23(12). 1514–1518. 10 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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