Daniel M. Kane

3.6k total citations
89 papers, 841 citations indexed

About

Daniel M. Kane is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computational Mechanics. According to data from OpenAlex, Daniel M. Kane has authored 89 papers receiving a total of 841 indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Artificial Intelligence, 30 papers in Computational Theory and Mathematics and 12 papers in Computational Mechanics. Recurrent topics in Daniel M. Kane's work include Machine Learning and Algorithms (30 papers), Complexity and Algorithms in Graphs (22 papers) and Sparse and Compressive Sensing Techniques (10 papers). Daniel M. Kane is often cited by papers focused on Machine Learning and Algorithms (30 papers), Complexity and Algorithms in Graphs (22 papers) and Sparse and Compressive Sensing Techniques (10 papers). Daniel M. Kane collaborates with scholars based in United States, United Kingdom and Canada. Daniel M. Kane's co-authors include Jelani Nelson, Ilias Diakonikolas, David P. Woodruff, Alistair Stewart, Paul Valiant, Mihir Bellare, Joseph Jaeger, Raghu Meka, Ely Porat and Ryan Williams and has published in prestigious journals such as Journal of Fluid Mechanics, Communications of the ACM and Journal of the ACM.

In The Last Decade

Daniel M. Kane

77 papers receiving 761 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel M. Kane United States 15 460 271 145 125 110 89 841
Ilias Diakonikolas United States 18 434 0.9× 316 1.2× 140 1.0× 46 0.4× 112 1.0× 77 801
Yin Tat Lee United States 18 338 0.7× 373 1.4× 211 1.5× 99 0.8× 99 0.9× 46 816
Ravindran Kannan United States 14 330 0.7× 336 1.2× 93 0.6× 119 1.0× 55 0.5× 28 848
Raghu Meka United States 17 332 0.7× 305 1.1× 79 0.5× 270 2.2× 83 0.8× 48 792
David Steurer United States 21 383 0.8× 707 2.6× 264 1.8× 126 1.0× 121 1.1× 47 1.1k
Jelani Nelson United States 14 540 1.2× 236 0.9× 266 1.8× 237 1.9× 47 0.4× 38 854
Yuri Rabinovich Israel 14 265 0.6× 631 2.3× 325 2.2× 102 0.8× 50 0.5× 38 1.2k
Hans Ulrich Simon Germany 16 620 1.3× 275 1.0× 215 1.5× 43 0.3× 40 0.4× 76 1.0k
Konstantin Makarychev United States 18 349 0.8× 553 2.0× 427 2.9× 53 0.4× 33 0.3× 68 1.2k
S. Vempala United States 10 531 1.2× 172 0.6× 63 0.4× 125 1.0× 44 0.4× 15 865

Countries citing papers authored by Daniel M. Kane

Since Specialization
Citations

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

Fields of papers citing papers by Daniel M. Kane

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel M. Kane. 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 Daniel M. Kane. The network helps show where Daniel M. Kane may publish in the future.

Co-authorship network of co-authors of Daniel M. Kane

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel M. Kane. A scholar is included among the top collaborators of Daniel M. Kane 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 Daniel M. Kane. Daniel M. Kane 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.
Kane, Daniel M., et al.. (2024). A Qualitative Study of Spatial Strategies in Blind and Low Vision Individuals. Papers on Engineering Education Repository (American Society for Engineering Education).
2.
Goodridge, Wade, et al.. (2024). Work in Progress: The Development of a Tactile Spatial Ability Instrument for Assessing Spatial Ability in Blind and Low-vision Populations. 2021 ASEE Virtual Annual Conference Content Access Proceedings. 2 indexed citations
3.
Goodridge, Wade, et al.. (2023). Spatial Strategies Employed by Blind and Low-Vision (BLV) Individuals on the Tactile Mental Cutting Test (TMCT). International Journal of Engineering Pedagogy (iJEP). 13(5). 42–57. 1 indexed citations
4.
Diakonikolas, Ilias, et al.. (2021). The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model.. eScholarship (California Digital Library). 1552–1584. 1 indexed citations
5.
Diakonikolas, Ilias, et al.. (2020). Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks. eScholarship (California Digital Library). 1514–1539. 1 indexed citations
6.
Bousquet, Olivier, Daniel M. Kane, & Shay Moran. (2019). The Optimal Approximation Factor in Density Estimation.. eScholarship (California Digital Library). 318–341.
7.
Diakonikolas, Ilias, et al.. (2019). Private Testing of Distributions via Sample Permutations. DSpace@MIT (Massachusetts Institute of Technology). 32. 10877–10888. 3 indexed citations
8.
Diakonikolas, Ilias, Gautam Kamath, Daniel M. Kane, et al.. (2018). Sever: A Robust Meta-Algorithm for Stochastic Optimization. eScholarship (California Digital Library). 1596–1606. 17 indexed citations
9.
Cheng, Yu, Ilias Diakonikolas, Daniel M. Kane, & Alistair Stewart. (2018). Robust Learning of Fixed-Structure Bayesian Networks. eScholarship (California Digital Library). 31. 10283–10295. 7 indexed citations
10.
Diakonikolas, Ilias, Daniel M. Kane, & John Peebles. (2018). Testing Identity of Multidimensional Histograms. eScholarship (California Digital Library). 1107–1131. 1 indexed citations
11.
Kane, Daniel M., Julie R. Palmer, & Álvaro Pelayo. (2018). Minimal models of compact symplectic semitoric manifolds. Journal of Geometry and Physics. 125. 49–74. 4 indexed citations
12.
Canonne, Clément L., Ilias Diakonikolas, Daniel M. Kane, & Alistair Stewart. (2018). Testing Conditional Independence of Discrete Distributions. eScholarship (California Digital Library). 1–57. 2 indexed citations
13.
Kane, Daniel M., Roi Livni, Shay Moran, & Amir Yehudayoff. (2017). On Communication Complexity of Classification Problems. eScholarship (California Digital Library). 24. 177–1943. 1 indexed citations
14.
Kane, Daniel M., et al.. (2017). Labeling the complete bipartite graph with no zero cycles. arXiv (Cornell University). 24. 33. 1 indexed citations
15.
Kang, Ilgweon, et al.. (2016). 3D floorplan representations: Corner links and partial order. eScholarship (California Digital Library). 1–5. 1 indexed citations
16.
Kane, Daniel M. & Scott Duke Kominers. (2014). Asymptotic Improvements of Lower Bounds for the Least Common Multiples of Arithmetic Progressions. Canadian Mathematical Bulletin. 57(3). 551–561. 5 indexed citations
17.
Kane, Daniel M. & Osamu Watanabe. (2013). A Short Implicant of CNFs with Relatively Many Satisfying Assignments.. Electronic colloquium on computational complexity. 20. 176.
18.
Kane, Daniel M., Adam R. Klivans, & Raghu Meka. (2013). Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. Conference on Learning Theory. 522–545. 4 indexed citations
19.
Kane, Daniel M. & Jelani Nelson. (2012). Sparser Johnson-Lindenstrauss transforms. Symposium on Discrete Algorithms. 1195–1206. 11 indexed citations
20.
Kane, Daniel M.. (2006). Generalized base representations. Journal of Number Theory. 120(1). 92–100. 5 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

Breakdown of academic impact, for papers by Ilias Diakonikolas Breakdown of academic impact, for papers by Yin Tat Lee Breakdown of academic impact, for papers by Ravindran Kannan Breakdown of academic impact, for papers by Raghu Meka Breakdown of academic impact, for papers by David Steurer Breakdown of academic impact, for papers by Jelani Nelson Breakdown of academic impact, for papers by Yuri Rabinovich Breakdown of academic impact, for papers by Hans Ulrich Simon Breakdown of academic impact, for papers by Konstantin Makarychev Breakdown of academic impact, for papers by S. Vempala
Rankless by CCL
2026