Grant Wang

1.1k total citations
12 papers, 630 citations indexed

About

Grant Wang is a scholar working on Artificial Intelligence, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Grant Wang has authored 12 papers receiving a total of 630 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 4 papers in Computational Mechanics and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Grant Wang's work include Sparse and Compressive Sensing Techniques (3 papers), Face and Expression Recognition (3 papers) and Stochastic Gradient Optimization Techniques (3 papers). Grant Wang is often cited by papers focused on Sparse and Compressive Sensing Techniques (3 papers), Face and Expression Recognition (3 papers) and Stochastic Gradient Optimization Techniques (3 papers). Grant Wang collaborates with scholars based in United States, Canada and China. Grant Wang's co-authors include Santosh Vempala, Luis Rademacher, Amit Deshpande, Santosh Vempala, Chi Cheng, Ravi Kannan, Margrét V. Bjarnadóttir, Michael A. C. Kane, Rudra Pandey and Dimitris Bertsimas and has published in prestigious journals such as Operations Research, Journal of Computer and System Sciences and ACM Transactions on Database Systems.

In The Last Decade

Grant Wang

11 papers receiving 567 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Grant Wang United States	9	318	153	108	87	59	12	630
Madeleine Udell United States	15	194 0.6×	144 0.9×	112 1.0×	47 0.5×	12 0.2×	41	628
Yin Tat Lee United States	18	338 1.1×	99 0.6×	85 0.8×	35 0.4×	11 0.2×	46	816
Vishwa Vinay United States	12	480 1.5×	90 0.6×	180 1.7×	137 1.6×	6 0.1×	36	841
Aditya Bhaskara United States	9	156 0.5×	39 0.3×	51 0.5×	36 0.4×	12 0.2×	38	397
Noureddine El Karoui United States	14	253 0.8×	184 1.2×	43 0.4×	124 1.4×	132 2.2×	32	1.0k
Preyas Popat United States	3	292 0.9×	44 0.3×	158 1.5×	122 1.4×	10 0.2×	5	535
W. Fernandez de la Véga France	15	160 0.5×	30 0.2×	53 0.5×	83 1.0×	14 0.2×	39	897
Ilias Diakonikolas United States	18	434 1.4×	46 0.3×	45 0.4×	50 0.6×	23 0.4×	77	801
Shmuel Onn Israel	18	116 0.4×	53 0.3×	56 0.5×	41 0.5×	18 0.3×	78	811
Saeed Ghadimi United States	11	931 2.9×	700 4.6×	123 1.1×	30 0.3×	10 0.2×	21	1.3k

Countries citing papers authored by Grant Wang

Since Specialization

Citations

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

Fields of papers citing papers by Grant Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Grant Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Grant Wang. A scholar is included among the top collaborators of Grant Wang 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 Grant Wang. Grant Wang 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:

12 of 12 papers shown

1.

Luo, Jiawei, Yan Liu, Chih‐Wei Hu, et al.. (2025). Review of diffusion models and its applications in biomedical informatics. BMC Medical Informatics and Decision Making. 25(1). 390–390.

2.

Foerster, Stephen R., et al.. (2016). Are Cash Flows Better Stock Return Predictors Than Profits?. Financial Analysts Journal. 73(1). 73–99. 44 indexed citations

3.

Foerster, Stephen R., et al.. (2014). Are Cash Flows Better Stock Return Predictors than Profits?. SSRN Electronic Journal. 6 indexed citations

4.

Wang, Grant, et al.. (2013). Fire Safety Provisions for Aged Concrete Building Structures. Procedia Engineering. 62. 629–638. 13 indexed citations

5.

Bertsimas, Dimitris, Margrét V. Bjarnadóttir, Michael A. C. Kane, et al.. (2008). Algorithmic Prediction of Health-Care Costs. Operations Research. 56(6). 1382–1392. 138 indexed citations

6.

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

7.

Cheng, Chi, Ravi Kannan, Santosh Vempala, & Grant Wang. (2006). A divide-and-merge methodology for clustering. ACM Transactions on Database Systems. 31(4). 1499–1525. 72 indexed citations

8.

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

9.

Deshpande, Amit, Luis Rademacher, Santosh Vempala, & Grant Wang. (2006). Matrix approximation and projective clustering via volume sampling. 1117–1126. 52 indexed citations

10.

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

11.

Cheng, Chi, Santosh Vempala, Ravi Kannan, & Grant Wang. (2005). A divide-and-merge methodology for clustering. 196–205. 37 indexed citations

12.

Vempala, Santosh & Grant Wang. (2004). A spectral algorithm for learning mixture models. Journal of Computer and System Sciences. 68(4). 841–860. 102 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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