Jonathan DeCastro

749 total citations
40 papers, 470 citations indexed

About

Jonathan DeCastro is a scholar working on Automotive Engineering, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Jonathan DeCastro has authored 40 papers receiving a total of 470 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Automotive Engineering, 13 papers in Artificial Intelligence and 12 papers in Control and Systems Engineering. Recurrent topics in Jonathan DeCastro's work include Autonomous Vehicle Technology and Safety (13 papers), Formal Methods in Verification (9 papers) and Robotic Path Planning Algorithms (6 papers). Jonathan DeCastro is often cited by papers focused on Autonomous Vehicle Technology and Safety (13 papers), Formal Methods in Verification (9 papers) and Robotic Path Planning Algorithms (6 papers). Jonathan DeCastro collaborates with scholars based in United States, Switzerland and Canada. Jonathan DeCastro's co-authors include Hadas Kress‐Gazit, Liang Tang, Xiaodong Zhang, Kai Goebel, Daniela Rus, Guy Rosman, Liang Tang, Vasumathi Raman, Bin Zhang and Javier Alonso–Mora and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Industrial Electronics and The International Journal of Robotics Research.

In The Last Decade

Jonathan DeCastro

38 papers receiving 462 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonathan DeCastro United States 12 202 122 111 98 75 40 470
Jeremy Gillula United States 9 310 1.5× 176 1.4× 111 1.0× 151 1.5× 102 1.4× 12 546
Kim P. Wabersich Switzerland 10 593 2.9× 74 0.6× 109 1.0× 134 1.4× 52 0.7× 22 775
Gennaro Notomista United States 13 254 1.3× 169 1.4× 82 0.7× 111 1.1× 71 0.9× 35 643
Jaime F. Fisac United States 15 325 1.6× 204 1.7× 150 1.4× 247 2.5× 139 1.9× 30 740
Chen Shen China 9 262 1.3× 102 0.8× 168 1.5× 19 0.2× 30 0.4× 31 560
Guihe Qin China 13 96 0.5× 142 1.2× 100 0.9× 78 0.8× 14 0.2× 90 509
Bo Mi China 13 182 0.9× 71 0.6× 31 0.3× 134 1.4× 30 0.4× 50 496
Vishnu R. Desaraju United States 9 160 0.8× 213 1.7× 262 2.4× 83 0.8× 18 0.2× 22 545
Melissa Greeff Canada 6 300 1.5× 89 0.7× 72 0.6× 188 1.9× 55 0.7× 15 540
Minghao Han China 13 350 1.7× 104 0.9× 107 1.0× 161 1.6× 66 0.9× 42 629

Countries citing papers authored by Jonathan DeCastro

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan DeCastro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonathan DeCastro

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan DeCastro. A scholar is included among the top collaborators of Jonathan DeCastro 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 Jonathan DeCastro. Jonathan DeCastro 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.
Hu, Haimin, Jaime Fernández Fisac, Naomi Ehrich Leonard, et al.. (2025). Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactions. 16678–16684.
2.
DeCastro, Jonathan, Jean Costa, Deepak Gopinath, et al.. (2024). Personalizing driver safety interfaces via driver cognitive factors inference. Scientific Reports. 14(1). 18058–18058. 2 indexed citations
3.
Li, Dan, et al.. (2022). Outlier-robust Inverse Reinforcement Learning and Reward-based Detection of Anomalous Driving Behaviors. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 4175–4182. 2 indexed citations
4.
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
5.
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
6.
DeCastro, Jonathan, et al.. (2021). Learning A Risk-Aware Trajectory Planner From Demonstrations Using Logic Monitor. 3 indexed citations
7.
Araki, Brandon, et al.. (2021). The Logical Options Framework. International Conference on Machine Learning. 307–317.
8.
Li, Xiao, Guy Rosman, Igor Gilitschenski, et al.. (2021). Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features. DSpace@MIT (Massachusetts Institute of Technology). 1 indexed citations
9.
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
10.
Li, Xiao, Guy Rosman, Igor Gilitschenski, et al.. (2020). Differentiable Logic Layer for Rule Guided Trajectory Prediction. 2178–2194. 2 indexed citations
11.
Alonso–Mora, Javier, Jonathan DeCastro, Vasumathi Raman, Daniela Rus, & Hadas Kress‐Gazit. (2017). Reactive mission and motion planning with deadlock resolution avoiding dynamic obstacles. Autonomous Robots. 42(4). 801–824. 47 indexed citations
12.
DeCastro, Jonathan & Hadas Kress‐Gazit. (2016). Nonlinear Controller Synthesis and Automatic Workspace Partitioning for Reactive High-Level Behaviors. 225–234. 8 indexed citations
13.
DeCastro, Jonathan & Hadas Kress‐Gazit. (2013). Guaranteeing reactive high-level behaviors for robots with complex dynamics. 749–756. 11 indexed citations
14.
Tang, Liang, et al.. (2011). A Testbed for Real-Time Autonomous Vehicle PHM and Contingency Management Applications. Annual Conference of the PHM Society. 3(1). 20 indexed citations
15.
Zhang, Bin, Liang Tang, Jonathan DeCastro, & Kai Goebel. (2011). Prognostics-enhanced Receding Horizon Mission Planning for Field Unmanned Vehicles. AIAA Guidance, Navigation, and Control Conference. 5 indexed citations
16.
Tang, Liang, et al.. (2010). Filtering and prediction techniques for model-based prognosis and uncertainty management. 1–10. 23 indexed citations
17.
Zhang, Xiaodong, et al.. (2010). A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults. NASA STI Repository (National Aeronautics and Space Administration). 143–153. 13 indexed citations
18.
DeCastro, Jonathan. (2007). Rate-Based Model Predictive Control of Turbofan Engine Clearance. Journal of Propulsion and Power. 23(4). 804–813. 41 indexed citations
19.
DeCastro, Jonathan. (2006). Rate-Based Model Predictive Control of Turbofan Engine Clearance. 2 indexed citations
20.
Steinetz, Bruce M., et al.. (2006). Test Rig for Active Turbine Blade Tip Clearance Control Concepts: An Update. NASA Technical Reports Server (NASA). 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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