Jason Phang

4.1k total citations · 1 hit paper
15 papers, 671 citations indexed

About

Jason Phang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Jason Phang has authored 15 papers receiving a total of 671 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Jason Phang's work include Topic Modeling (12 papers), Natural Language Processing Techniques (10 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Jason Phang is often cited by papers focused on Topic Modeling (12 papers), Natural Language Processing Techniques (10 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Jason Phang collaborates with scholars based in United States, South Korea and Germany. Jason Phang's co-authors include Phu Mon Htut, Thibault Févry, Stella Biderman, Samuel R. Bowman, Quentin Anthony, Richard Yuanzhe Pang, Leo Gao, Michael Pieler, Samuel Weinbach and Horace He and has published in prestigious journals such as Medical Image Analysis, Lecture notes in computer science and Journal of Digital Imaging.

In The Last Decade

Jason Phang

15 papers receiving 631 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.

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

2022 235 citations Stella Biderman, Quentin Anthony et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jason Phang United States	10	556	119	89	70	41	15	671
Ximing Lu United States	10	470 0.8×	131 1.1×	20 0.2×	42 0.6×	14 0.3×	28	555
Eric Wallace United States	12	857 1.5×	287 2.4×	12 0.1×	94 1.3×	21 0.5×	20	1.1k
Teven Le Scao United States	4	722 1.3×	137 1.2×	13 0.1×	68 1.0×	35 0.9×	5	819
Zhijing Jin United States	12	807 1.5×	116 1.0×	18 0.2×	91 1.3×	25 0.6×	34	900
Juntao Li China	15	423 0.8×	120 1.0×	14 0.2×	66 0.9×	41 1.0×	74	642
Zaid Alyafeai Saudi Arabia	7	374 0.7×	65 0.5×	48 0.5×	25 0.4×	15 0.4×	13	445
Adrien Bibal Belgium	9	184 0.3×	51 0.4×	37 0.4×	33 0.5×	25 0.6×	24	329
Hengyi Cai China	9	269 0.5×	62 0.5×	16 0.2×	66 0.9×	37 0.9×	16	401
Hongshen Chen China	15	784 1.4×	142 1.2×	9 0.1×	89 1.3×	10 0.2×	48	934
Parminder Kaur India	10	204 0.4×	89 0.7×	120 1.3×	144 2.1×	3 0.1×	44	453

Countries citing papers authored by Jason Phang

Since Specialization

Citations

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

Fields of papers citing papers by Jason Phang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jason Phang

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

15 of 15 papers shown

1.

Tang, Xiangru, Jason Phang, Yilun Zhao, et al.. (2024). Struc-Bench: Are Large Language Models Good at Generating Complex Structured Tabular Data?. 12–34. 2 indexed citations

2.

Phang, Jason, Yao Zhao, & Peter Liu. (2023). Investigating Efficiently Extending Transformers for Long Input Summarization. 3946–3961. 15 indexed citations

3.

Michael, Julian, Ari Holtzman, Alicia Parrish, et al.. (2023). What Do NLP Researchers Believe? Results of the NLP Community Metasurvey. 16334–16368. 7 indexed citations

4.

Scao, Teven Le, Thomas J. Wang, Daniel Hesslow, et al.. (2022). What Language Model to Train if You Have One Million GPU Hours?. 765–782. 18 indexed citations

5.

Biderman, Stella, Quentin Anthony, Leo Gao, et al.. (2022). GPT-NeoX-20B: An Open-Source Autoregressive Language Model. 95–136. 235 indexed citations breakdown →

6.

Parrish, Alicia, Nikita Nangia, Vishakh Padmakumar, et al.. (2022). BBQ: A hand-built bias benchmark for question answering. Findings of the Association for Computational Linguistics: ACL 2022. 2086–2105. 64 indexed citations

7.

Pang, Richard Yuanzhe, Alicia Parrish, Nikita Nangia, et al.. (2022). QuALITY: Question Answering with Long Input Texts, Yes!. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5336–5358. 21 indexed citations

8.

Wang, Alex, et al.. (2022). SQuALITY: Building a Long-Document Summarization Dataset the Hard Way. Faculty Digital Archive (New York University Florence). 1139–1156. 9 indexed citations

9.

Parrish, Alicia, Harsh Trivedi, Ethan Perez, et al.. (2022). Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions. 17–28. 1 indexed citations

10.

Wu, Nan, Zhe Huang, Yiqiu Shen, et al.. (2021). Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms. Journal of Digital Imaging. 34(6). 1414–1423. 6 indexed citations

11.

Shen, Yiqiu, Nan Wu, Jason Phang, et al.. (2020). An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization. Medical Image Analysis. 68. 101908–101908. 118 indexed citations

12.

Pruksachatkun, Yada, Jason Phang, Haokun Liu, et al.. (2020). Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?. 5231–5247. 88 indexed citations

13.

Shen, Yiqiu, Nan Wu, Jason Phang, et al.. (2019). Globally-Aware Multiple Instance Classifier for Breast Cancer Screening. Lecture notes in computer science. 11861. 18–26. 10 indexed citations

14.

Warstadt, Alex, Yu Cao, Wei Peng, et al.. (2019). Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs. Faculty Digital Archive (New York University Florence). 2877–2887. 48 indexed citations

15.

Févry, Thibault & Jason Phang. (2018). Unsupervised Sentence Compression using Denoising Auto-Encoders. 413–422. 29 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