Dorsa Sadigh

79 papers receiving 1.9k citations

Hit Papers

SpatialVLM: Endowing Vision-Language Models with Spatial ...20242026202520241020304050

Peers

Dorsa Sadigh
Comparison fields: 5 of 93
  • Artificial Intelligence 868
  • Control and Systems Engineering 673
  • Automotive Engineering 473
  • Computer Vision and Pattern Recognition 382
  • Computational Theory and Mathematics 363
Replace Fernando Fernández with:
Fernando Fernández Spain
Karl Koscher United States
Philip Koopman United States
Jonathan Sprinkle United States
Markus Maurer Germany
BaekGyu Kim United States
Vinny Cahill Ireland
Mohammad Abdullah Al Faruque United States
Alexei Czeskis United States
Matthijs T. J. Spaan Netherlands
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Citations per year

Countries citing papers authored by Dorsa Sadigh

Since Specialization
Citations

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

Fields of papers citing papers by Dorsa Sadigh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dorsa Sadigh

This figure shows the co-authorship network connecting the top 25 collaborators of Dorsa Sadigh. A scholar is included among the top collaborators of Dorsa Sadigh 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 Dorsa Sadigh. Dorsa Sadigh 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
#WorkIndexed citations
1 0
2 1
3 38
4 7
5 9
6 6
7 29
8 10
9 34
10 54
11 5
12
24
13 12
14
Learning Visually Guided Latent Actions for Assistive Teleoperation
2
15
Learning from My Partner’s Actions: Roles in Decentralized Robot Teams
4
16
Influencing Interactions between Human Drivers and Autonomous Vehicles
3
17
Multi-Agent Generative Adversarial Imitation Learning
25
18
Fast Safe Mission Plans for Autonomous Vehicles
5
19
Data-Driven Probabilistic Modeling and Verification of Human Driver Behavior
38
20 13

About Dorsa Sadigh

Dorsa Sadigh is a scholar working on Computational Mathematics, Artificial Intelligence and Control and Systems Engineering, having authored 85 papers that have together received 1.9k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (22 papers), Robot Manipulation and Learning (17 papers) and Formal Methods in Verification (14 papers). The work is most often cited by research in Software (151 citations), Automotive Engineering (473 citations) and Control and Systems Engineering (673 citations). Dorsa Sadigh has collaborated with scholars based in United States, United Kingdom and France. Frequent co-authors include Sanjit A. Seshia, S. Shankar Sastry, Anca D. Dragan, Vasumathi Raman, Alexandre Donzé, Ashish Kapoor, Richard M. Murray, Erdem Bıyık, Shankar Sastry and Ramtin Pedarsani. Their work appears in journals such as Communications of the ACM, The International Journal of Robotics Research and IEEE Transactions on Intelligent Transportation Systems.

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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