Jay Whang

2.9k total citations · 1 hit paper
8 papers, 205 citations indexed

About

Jay Whang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Jay Whang has authored 8 papers receiving a total of 205 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Statistical and Nonlinear Physics. Recurrent topics in Jay Whang's work include Gaussian Processes and Bayesian Inference (3 papers), Model Reduction and Neural Networks (3 papers) and Image and Signal Denoising Methods (2 papers). Jay Whang is often cited by papers focused on Gaussian Processes and Bayesian Inference (3 papers), Model Reduction and Neural Networks (3 papers) and Image and Signal Denoising Methods (2 papers). Jay Whang collaborates with scholars based in United States and Israel. Jay Whang's co-authors include Alexandros G. Dimakis, Yonina C. Eldar, Nir Shlezinger, Hyeji Kim, Emma Brunskill, Jonathan Ho, Chitwan Saharia, David J. Fleet, Saurabh Saxena and Raphael Gontijo Lopes and has published in prestigious journals such as Proceedings of the IEEE, arXiv (Cornell University) and IEEE Journal on Selected Areas in Information Theory.

In The Last Decade

Jay Whang

8 papers receiving 203 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.

Model-Based Deep Learning

2023 167 citations Nir Shlezinger, Jay Whang et al. Proceedings of the IEEE profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jay Whang United States	4	62	59	41	39	33	8	205
Khawla A. Alnajjar United Arab Emirates	9	62 1.0×	121 2.1×	17 0.4×	31 0.8×	29 0.9×	57	232
Lantu Guo China	8	215 3.5×	94 1.6×	49 1.2×	59 1.5×	56 1.7×	33	338
Zhilin Lu China	7	138 2.2×	196 3.3×	34 0.8×	25 0.6×	43 1.3×	17	299
Farzan Haddadi Iran	9	32 0.5×	81 1.4×	55 1.3×	103 2.6×	59 1.8×	28	284
Shuhong Jiao China	10	28 0.5×	68 1.2×	87 2.1×	58 1.5×	52 1.6×	35	278
Youwen Zhang China	11	76 1.2×	191 3.2×	22 0.5×	145 3.7×	37 1.1×	53	445
Yue Jiang China	10	35 0.6×	109 1.8×	113 2.8×	12 0.3×	48 1.5×	48	312
Lipeng Gao China	11	194 3.1×	55 0.9×	53 1.3×	47 1.2×	188 5.7×	27	336
Hongyi Pan United States	10	30 0.5×	45 0.8×	137 3.3×	23 0.6×	8 0.2×	25	260
Farrukh A. Bhatti Pakistan	8	50 0.8×	156 2.6×	24 0.6×	24 0.6×	67 2.0×	24	300

Countries citing papers authored by Jay Whang

Since Specialization

Citations

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

Fields of papers citing papers by Jay Whang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jay Whang

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

8 of 8 papers shown

1.

Whang, Jay, et al.. (2024). Neural Distributed Source Coding. IEEE Journal on Selected Areas in Information Theory. 5. 493–508. 3 indexed citations

2.

Shlezinger, Nir, Jay Whang, Yonina C. Eldar, & Alexandros G. Dimakis. (2023). Model-Based Deep Learning. Proceedings of the IEEE. 111(5). 465–499. 167 indexed citations breakdown →

3.

Denton, Emily, David J. Fleet, Raphael Gontijo Lopes, et al.. (2022). Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding. 36479–36494. 2 indexed citations

4.

Shlezinger, Nir, Jay Whang, Yonina C. Eldar, & Alexandros G. Dimakis. (2021). Model-Based Deep Learning: Key Approaches and Design Guidelines. 1–6. 25 indexed citations

5.

Whang, Jay, Qi Lei, & Alexandros G. Dimakis. (2020). Solving Inverse Problems with a Flow-based Noise Model. arXiv (Cornell University). 11146–11157. 1 indexed citations

6.

Whang, Jay, et al.. (2020). Compressed Sensing with Invertible Generative Models and Dependent Noise. 2 indexed citations

7.

Whang, Jay, et al.. (2020). Composing Normalizing Flows for Inverse Problems. arXiv (Cornell University). 11158–11169. 2 indexed citations

8.

Whang, Jay, et al.. (2018). Strategic Object Oriented Reinforcement Learning.. 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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