John Lambert

2.2k total citations · 1 hit paper
21 papers, 1.2k citations indexed

About

John Lambert is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, John Lambert has authored 21 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Information Systems and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in John Lambert's work include Advanced Malware Detection Techniques (4 papers), Advanced Neural Network Applications (3 papers) and Autonomous Vehicle Technology and Safety (2 papers). John Lambert is often cited by papers focused on Advanced Malware Detection Techniques (4 papers), Advanced Neural Network Applications (3 papers) and Autonomous Vehicle Technology and Safety (2 papers). John Lambert collaborates with scholars based in United States, India and Australia. John Lambert's co-authors include James Hays, Andrew T. Hartnett, Jagjeet Singh, Peter Carr, Deva Ramanan, Simon Lucey, Ming-Fang Chang, Patsorn Sangkloy, Wang De and Sławomir Bąk and has published in prestigious journals such as Journal of the Franklin Institute, International Journal of Engineering Science and JCMS Journal of Common Market Studies.

In The Last Decade

John Lambert

19 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Argoverse: 3D Tracking and Forecasting With Rich Maps

2019 882 citations Ming-Fang Chang, John Lambert et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John Lambert United States	8	615	612	283	167	157	21	1.2k
Wang De United States	10	522 0.8×	575 0.9×	318 1.1×	156 0.9×	149 0.9×	42	1.2k
Sławomir Bąk United States	9	984 1.6×	577 0.9×	236 0.8×	139 0.8×	147 0.9×	14	1.4k
Jagjeet Singh United States	3	529 0.9×	600 1.0×	205 0.7×	143 0.9×	154 1.0×	4	931
Hang Zhao China	10	443 0.7×	570 0.9×	206 0.7×	101 0.6×	164 1.0×	34	1.0k
Pei Sun Australia	10	327 0.5×	251 0.4×	290 1.0×	99 0.6×	73 0.5×	26	824
Diange Yang China	20	472 0.8×	725 1.2×	193 0.7×	366 2.2×	147 0.9×	76	1.6k
Christopher Choy United States	10	770 1.3×	563 0.9×	292 1.0×	357 2.1×	161 1.0×	14	1.4k
Masao Sakauchi Japan	17	1.1k 1.8×	173 0.3×	255 0.9×	65 0.4×	145 0.9×	130	1.4k
Gabriel Agamennoni Australia	17	178 0.3×	322 0.5×	452 1.6×	216 1.3×	130 0.8×	36	1.1k
Julian F. P. Kooij Netherlands	17	468 0.8×	289 0.5×	190 0.7×	255 1.5×	79 0.5×	39	889

Countries citing papers authored by John Lambert

Since Specialization

Citations

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

Fields of papers citing papers by John Lambert

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Lambert

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Wilson, Benjamin, John Lambert, Jagjeet Singh, et al.. (2023). Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting. arXiv (Cornell University). 45 indexed citations

2.

Lambert, John, Zhuang Liu, Ozan Şener, James Hays, & Vladlen Koltun. (2020). MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation. 2876–2885. 105 indexed citations

3.

Nyström, Magnus, et al.. (2020). Adversarial Machine Learning - Industry Perspectives. SSRN Electronic Journal. 13 indexed citations

4.

Chang, Ming-Fang, John Lambert, Patsorn Sangkloy, et al.. (2019). Argoverse: 3D Tracking and Forecasting With Rich Maps. 8740–8749. 882 indexed citations breakdown →

5.

Liu, Qingyun, et al.. (2018). Latte: Large-Scale Lateral Movement Detection. 1–6. 18 indexed citations

6.

Laxman, Srivatsan, et al.. (2011). ZDVUE. 31–42. 6 indexed citations

7.

Laxman, Srivatsan, et al.. (2009). Pattern Mining for Future Attacks. 10 indexed citations

8.

Naldurg, Prasad, Stefan Schwoon, Sriram K. Rajamani, & John Lambert. (2006). NETRA :. 55–66. 19 indexed citations

9.

Lambert, John, et al.. (2005). Impact de l'abreuvement du bétail sur la qualité écologique des cours d'eau: étude de cas en région wallonne de Belgique. 10(1). 59–70. 4 indexed citations

10.

van, et al.. (2002). Bergambacht field test evaluation. Distributed Computing. 1 indexed citations

11.

Lambert, John. (2002). Using stack traces to identify failed executions in a Java distributed system. 1 indexed citations

12.

Lambert, John, et al.. (2001). CHARACTERISTICS OF FAST RESPONSE MEAN RIDE HEIGHT ANALOGUE CONTROLLED HEAVY VEHICLE AIR SPRING SUSPENSION SYSTEM. Transport Research Forum. 2 indexed citations

13.

Lambert, John, et al.. (2001). <title>xdProf: a tool for the capture and analysis of stack traces in a distributed Java system</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4521. 96–105. 4 indexed citations

14.

Lambert, John, et al.. (1999). Performance based standards for heavy vehicles in Australia: field of performance measures. 3 indexed citations

15.

Lambert, John. (1990). The Pineapple Air Force:: Pearl Harbor to Tokyo. Medical Entomology and Zoology.

16.

Lambert, John. (1971). THE CHESHIRE CAT AND THE POND: EEC AND THE MEDITERRANEAN AREA. JCMS Journal of Common Market Studies. 10(1). 37–46. 2 indexed citations

17.

Lambert, John & Shelton S. Alexander. (1971). Single station and array methods for improved surface wave spectral estimates. Defense Technical Information Center (DTIC). 2 indexed citations

18.

Lambert, John. (1969). Closed form solutions for fluid flow in a thin walled tube buckled by a uniform external pressure. International Journal of Engineering Science. 7(9). 963–971. 1 indexed citations

19.

Lambert, John. (1958). On the nonlinearities of fluid flow in nonrigid tubes. Journal of the Franklin Institute. 266(2). 83–102. 48 indexed citations

20.

Lambert, John. (1956). FLUID FLOW IN A NONRIGID TUBE. Purdue e-Pubs (Purdue University System). 7 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.

Explore authors with similar magnitude of impact