Lucas Janson

2.5k total citations · 2 hit papers
25 papers, 1.1k citations indexed

About

Lucas Janson is a scholar working on Statistics and Probability, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Lucas Janson has authored 25 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Statistics and Probability, 6 papers in Artificial Intelligence and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Lucas Janson's work include Statistical Methods and Inference (9 papers), Statistical Methods in Clinical Trials (5 papers) and Machine Learning and Algorithms (4 papers). Lucas Janson is often cited by papers focused on Statistical Methods and Inference (9 papers), Statistical Methods in Clinical Trials (5 papers) and Machine Learning and Algorithms (4 papers). Lucas Janson collaborates with scholars based in United States, France and Denmark. Lucas Janson's co-authors include Marco Pavone, Edward Schmerling, Emmanuel J. Candès, Ashley Clark, Jinchi Lv, Yingying Fan, Brian Ichter, Rina Foygel Barber, Weijie Su and Trevor Hastie and has published in prestigious journals such as Journal of the American Statistical Association, Annals of Surgery and Biometrika.

In The Last Decade

Lucas Janson

23 papers receiving 1.1k citations

Hit Papers

Fast marching tree: A fast marching sampling-based method... 2015 2026 2018 2022 2015 2018 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lucas Janson United States 13 422 269 245 242 202 25 1.1k
Dong Xu China 19 365 0.9× 19 0.1× 317 1.3× 81 0.3× 99 0.5× 52 1.1k
Jianguo Sun China 15 163 0.4× 72 0.3× 294 1.2× 63 0.3× 53 0.3× 64 825
Alexander Jung Finland 15 91 0.2× 68 0.3× 235 1.0× 18 0.1× 97 0.5× 73 747
Muhammad Moinuddin Saudi Arabia 16 84 0.2× 9 0.0× 153 0.6× 126 0.5× 132 0.7× 149 1.1k
Masahiro Tanaka Japan 15 61 0.1× 31 0.1× 263 1.1× 36 0.1× 125 0.6× 160 986
Zhen Xiang United States 13 158 0.4× 75 0.3× 314 1.3× 14 0.1× 37 0.2× 42 591
Rami Mangoubi United States 14 80 0.2× 50 0.2× 116 0.5× 141 0.6× 334 1.7× 54 976
Lingji Chen China 14 42 0.1× 23 0.1× 360 1.5× 117 0.5× 459 2.3× 72 1.0k
Hamid Khaloozadeh Iran 18 57 0.1× 19 0.1× 308 1.3× 193 0.8× 545 2.7× 183 1.2k

Countries citing papers authored by Lucas Janson

Since Specialization
Citations

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

Fields of papers citing papers by Lucas Janson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lucas Janson

This figure shows the co-authorship network connecting the top 25 collaborators of Lucas Janson. A scholar is included among the top collaborators of Lucas Janson 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 Lucas Janson. Lucas Janson 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.
Xu, Lily, et al.. (2025). Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits. Proceedings of the AAAI Conference on Artificial Intelligence. 39(27). 28195–28203.
2.
Janson, Lucas, et al.. (2025). Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate. SIAM/ASA Journal on Uncertainty Quantification. 13(2). 563–590.
3.
Imai, Kosuke, et al.. (2024). Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis. Political Analysis. 32(3). 329–344. 2 indexed citations
4.
Barber, Rina Foygel & Lucas Janson. (2022). Testing goodness-of-fit and conditional independence with approximate co-sufficient sampling. The Annals of Statistics. 50(5). 7 indexed citations
5.
Zhang, Kelly, Lucas Janson, & Susan A. Murphy. (2021). Statistical Inference with M-Estimators on Bandit Data.. arXiv (Cornell University). 1 indexed citations
6.
Ma, Siyuan, Boyu Ren, Himel Mallick, et al.. (2021). A statistical model for describing and simulating microbial community profiles. PLoS Computational Biology. 17(9). e1008913–e1008913. 40 indexed citations
7.
Wang, Wenshuo & Lucas Janson. (2021). A high-dimensional power analysis of the conditional randomization test and knockoffs. Biometrika. 109(3). 631–645. 6 indexed citations
8.
Liu, Molei, Eugene Katsevich, Lucas Janson, & Aaditya Ramdas. (2021). Fast and powerful conditional randomization testing via distillation. Biometrika. 109(2). 277–293. 17 indexed citations
9.
Janson, Lucas, et al.. (2020). Cross-validation Confidence Intervals for Test Error. arXiv (Cornell University). 33. 16339–16350. 2 indexed citations
10.
Bates, Stephen, et al.. (2020). Metropolized Knockoff Sampling. Figshare. 34 indexed citations
11.
Candès, Emmanuel J., Yingying Fan, Lucas Janson, & Jinchi Lv. (2018). Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection. Journal of the Royal Statistical Society Series B (Statistical Methodology). 80(3). 551–577. 302 indexed citations breakdown →
12.
Janson, Lucas, Brian Ichter, & Marco Pavone. (2017). Deterministic sampling-based motion planning: Optimality, complexity, and performance. The International Journal of Robotics Research. 37(1). 46–61. 73 indexed citations
13.
Janson, Lucas, Rina Foygel Barber, & Emmanuel J. Candès. (2016). EigenPrism: Inference for High Dimensional Signal-to-Noise Ratios. Journal of the Royal Statistical Society Series B (Statistical Methodology). 79(4). 1037–1065. 31 indexed citations
14.
Janson, Lucas & Weijie Su. (2016). Familywise error rate control via knockoffs. Electronic Journal of Statistics. 10(1). 23 indexed citations
15.
Janson, Lucas, Edward Schmerling, Ashley Clark, & Marco Pavone. (2015). Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions. The International Journal of Robotics Research. 34(7). 883–921. 325 indexed citations breakdown →
16.
Gholami, Sepideh, Lucas Janson, David J. Worhunsky, et al.. (2015). Number of Lymph Nodes Removed and Survival after Gastric Cancer Resection: An Analysis from the US Gastric Cancer Collaborative. Journal of the American College of Surgeons. 221(2). 291–299. 74 indexed citations
17.
Schmerling, Edward, Lucas Janson, & Marco Pavone. (2015). Optimal sampling-based motion planning under differential constraints: The driftless case. PubMed. 2015. 2368–2375. 40 indexed citations
18.
Janson, Lucas, William Fithian, & Trevor Hastie. (2015). Effective degrees of freedom: a flawed metaphor. Biometrika. 102(2). 479–485. 29 indexed citations
19.
Poultsides, George A., Thuy B. Tran, Eduardo Zambrano, et al.. (2015). Sarcoma Resection With and Without Vascular Reconstruction. Annals of Surgery. 262(4). 632–640. 42 indexed citations
20.
Janson, Lucas & Bala Rajaratnam. (2013). A Methodology for Robust Multiproxy Paleoclimate Reconstructions and Modeling of Temperature Conditional Quantiles. Journal of the American Statistical Association. 109(505). 63–77. 2 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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