Luis Rademacher

903 total citations
26 papers, 401 citations indexed

About

Luis Rademacher is a scholar working on Computational Mechanics, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Luis Rademacher has authored 26 papers receiving a total of 401 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computational Mechanics, 9 papers in Signal Processing and 9 papers in Artificial Intelligence. Recurrent topics in Luis Rademacher's work include Sparse and Compressive Sensing Techniques (10 papers), Computational Geometry and Mesh Generation (6 papers) and Blind Source Separation Techniques (6 papers). Luis Rademacher is often cited by papers focused on Sparse and Compressive Sensing Techniques (10 papers), Computational Geometry and Mesh Generation (6 papers) and Blind Source Separation Techniques (6 papers). Luis Rademacher collaborates with scholars based in United States, United Kingdom and India. Luis Rademacher's co-authors include Amit Deshpande, Grant Wang, Santosh Vempala, Navin Goyal, Santosh Vempala, Mikhail A. Belkin, Mikhail Belkin, Chang Shu, Ravi Kannan and Alan Frieze and has published in prestigious journals such as SIAM Journal on Computing, Advances in Mathematics and Operations Research Letters.

In The Last Decade

Luis Rademacher

21 papers receiving 370 citations

Peers

Luis Rademacher

AI

CM

CTM

CVPR

SP

Bamdev Mishra Japan

Anastasios Zouzias Canada

Venkat Chandrasekaran United States

Matus Telgarsky United States

C.-T. Pan United States

Elias Tsigaridas France

Jelani Nelson United States

Michael Kapralov United States

Ali Çivril Türkiye

Cameron Musco United States

Bamdev Mishra Japan

Luis Rademacher

136 ×0.7

AI

221 ×1.2

CM

53 ×0.5

CTM

130 ×1.3

CVPR

48 ×0.7

SP

Citations per year, relative to Luis Rademacher Luis Rademacher (= 1×) peers Bamdev Mishra

Countries citing papers authored by Luis Rademacher

Since Specialization

Citations

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

Fields of papers citing papers by Luis Rademacher

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luis Rademacher

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

All Works

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20 of 20 papers shown

1.

Rademacher, Luis, et al.. (2023). Improved Bounds for the Expected Number of k-Sets. Discrete & Computational Geometry. 70(3). 790–815.

2.

Rademacher, Luis, et al.. (2023). Expansion of random 0/1 polytopes. Random Structures and Algorithms. 64(2). 309–319.

3.

Rademacher, Luis & Chang Shu. (2022). The smoothed complexity of Frank–Wolfe methods via conditioning of random matrices and polytopes. 5(3). 273–310. 4 indexed citations

4.

Belkin, Mikhail, et al.. (2018). Eigenvectors of Orthogonally Decomposable Functions. SIAM Journal on Computing. 47(2). 547–615. 5 indexed citations

5.

Goyal, Navin, et al.. (2017). Heavy-Tailed Analogues of the Covariance Matrix for ICA. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 1 indexed citations

6.

Rademacher, Luis, Alejandro Toriello, & Juan Pablo Vielma. (2016). On packing and covering polyhedra in infinite dimensions. Operations Research Letters. 44(2). 225–230. 1 indexed citations

7.

Belkin, Mikhail A., et al.. (2016). The Hidden Convexity of Spectral Clustering. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 5 indexed citations

8.

Goyal, Navin, et al.. (2014). . Theory of Computing. 10(1). 237–256. 6 indexed citations

9.

Goyal, Navin, Luis Rademacher, & Santosh Vempala. (2014). Query Complexity of Sampling and Small Geometric Partitions. Combinatorics Probability Computing. 24(5). 733–753.

10.

Belkin, Mikhail A., et al.. (2014). The Hidden Convexity of Spectral Clustering. arXiv (Cornell University). 30(1). 2108–2114. 6 indexed citations

11.

Belkin, Mikhail A., et al.. (2013). Blind Signal Separation in the Presence of Gaussian Noise. Conference on Learning Theory. 270–287. 4 indexed citations

12.

Rademacher, Luis, et al.. (2013). Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis. Neural Information Processing Systems. 26. 2544–2552. 4 indexed citations

13.

Deshpande, Ajay, Shinn‐Ying Ho, Daniel J. Kleitman, et al.. (2010). Partitioning a Planar Graph of Girth 10 into a Forest and a Matching. Studies in Applied Mathematics. 124(3). 213–228. 3 indexed citations

14.

Goyal, Navin, Luis Rademacher, & Santosh Vempala. (2009). Expanders via random spanning trees. arXiv (Cornell University). 576–585. 21 indexed citations

15.

Goyal, Navin, Luis Rademacher, & Santosh Vempala. (2009). Expanders via Random Spanning Trees. 576–585. 10 indexed citations

16.

Rademacher, Luis & S. Vempala. (2008). Testing Geometric Convexity.

17.

Rademacher, Luis. (2007). Approximating the centroid is hard. 302–302. 22 indexed citations

18.

Deshpande, Amit, Luis Rademacher, Santosh Vempala, & Grant Wang. (2006). . Theory of Computing. 2(1). 225–247. 73 indexed citations

19.

Deshpande, Amit, Luis Rademacher, Santosh Vempala, & Grant Wang. (2006). Matrix approximation and projective clustering via volume sampling. 1117–1126. 90 indexed citations

20.

Rademacher, Luis, Santosh Vempala, & Grant Wang. (2005). Matrix Approximation and Projective Clustering via Iterative Sampling. DSpace@MIT (Massachusetts Institute of Technology). 3 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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