Micah Goldblum

2.2k total citations · 1 hit paper
21 papers, 386 citations indexed

About

Micah Goldblum is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Micah Goldblum has authored 21 papers receiving a total of 386 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Micah Goldblum's work include Adversarial Robustness in Machine Learning (12 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Anomaly Detection Techniques and Applications (5 papers). Micah Goldblum is often cited by papers focused on Adversarial Robustness in Machine Learning (12 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Anomaly Detection Techniques and Applications (5 papers). Micah Goldblum collaborates with scholars based in United States, China and Germany. Micah Goldblum's co-authors include Tom Goldstein, Avi Schwarzschild, Jonas Geiping, Bo Li, Dimitris Tsipras, Chulin Xie, Aleksander Mądry, Xinyun Chen, Dawn Song and Gowthami Somepalli and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Transactions on Multimedia.

In The Last Decade

Micah Goldblum

17 papers receiving 377 citations

Hit Papers

1 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.

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

2022 134 citations Micah Goldblum, Dimitris Tsipras et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Micah Goldblum United States	6	231	147	70	38	29	21	386
Dongming Zhang China	11	131 0.6×	303 2.1×	77 1.1×	21 0.6×	29 1.0×	65	457
Marcel R. Ackermann Germany	7	213 0.9×	57 0.4×	110 1.6×	36 0.9×	32 1.1×	10	321
Jamie Hayes United Kingdom	9	502 2.2×	249 1.7×	80 1.1×	70 1.8×	38 1.3×	23	669
Ian Fischer United States	5	205 0.9×	96 0.7×	99 1.4×	34 0.9×	85 2.9×	10	351
Giorgos Bouritsas United Kingdom	7	155 0.7×	155 1.1×	21 0.3×	17 0.4×	23 0.8×	9	322
Xueqing Li China	10	132 0.6×	99 0.7×	105 1.5×	31 0.8×	44 1.5×	56	297
Manzil Zaheer United States	10	340 1.5×	82 0.6×	15 0.2×	30 0.8×	42 1.4×	23	397
Madhav Jha United States	9	150 0.6×	74 0.5×	47 0.7×	66 1.7×	18 0.6×	17	294
Yiheng Xu China	8	285 1.2×	323 2.2×	33 0.5×	22 0.6×	85 2.9×	10	560
Zhenxing Qian China	14	191 0.8×	521 3.5×	57 0.8×	6 0.2×	79 2.7×	52	697

Countries citing papers authored by Micah Goldblum

Since Specialization

Citations

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

Fields of papers citing papers by Micah Goldblum

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Micah Goldblum

This figure shows the co-authorship network connecting the top 25 collaborators of Micah Goldblum. A scholar is included among the top collaborators of Micah Goldblum 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 Micah Goldblum. Micah Goldblum 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.

Wu, Zuxuan, et al.. (2024). The Role of ViT Design and Training in Robustness to Common Corruptions. IEEE Transactions on Multimedia. 27. 1374–1385.

2.

Khorramshahi, Pirazh, et al.. (2024). Identifying Attack-Specific Signatures in Adversarial Examples. 7050–7054. 1 indexed citations

3.

Chen, Jingjing, Micah Goldblum, Zuxuan Wu, et al.. (2023). Towards Transferable Adversarial Attacks on Image and Video Transformers. IEEE Transactions on Image Processing. 32. 6346–6358. 5 indexed citations

4.

Dickerson, John P., et al.. (2023). Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition. 74366–74393.

5.

Geiping, Jonas, et al.. (2023). A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. 41956–41979.

6.

Geiping, Jonas, et al.. (2023). Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. 41259–41282.

7.

Wen, Yuxin, Jonas Geiping, Micah Goldblum, & Tom Goldstein. (2023). STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning. 1–5. 2 indexed citations

8.

Somepalli, Gowthami, et al.. (2023). Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models. 6048–6058. 65 indexed citations

9.

Bansal, Arpit, Hongmin Chu, Avi Schwarzschild, et al.. (2023). Universal Guidance for Diffusion Models. 843–852. 62 indexed citations

10.

Wei, Zhipeng, Jingjing Chen, Micah Goldblum, et al.. (2022). Towards Transferable Adversarial Attacks on Vision Transformers. Proceedings of the AAAI Conference on Artificial Intelligence. 36(3). 2668–2676. 56 indexed citations

11.

Goldblum, Micah, Dimitris Tsipras, Chulin Xie, et al.. (2022). Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(2). 1563–1580. 134 indexed citations breakdown →

12.

Schwarzschild, Avi, Micah Goldblum, Arjun K. Gupta, John P. Dickerson, & Tom Goldstein. (2021). Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. International Conference on Machine Learning. 9389–9398. 1 indexed citations

13.

Goldblum, Micah, et al.. (2021). LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. arXiv (Cornell University). 38 indexed citations

14.

Fowl, Liam, et al.. (2021). Protecting Proprietary Data: Poisoning for Secure Dataset Release. 1 indexed citations

15.

Zhu, Chen, et al.. (2021). The Intrinsic Dimension of Images and Its Impact on Learning. arXiv (Cornell University). 9 indexed citations

16.

Ni, Renkun, et al.. (2020). Data Augmentation for Meta-Learning. arXiv (Cornell University). 1 indexed citations

17.

Goldblum, Micah, Liam Fowl, & Tom Goldstein. (2020). Adversarially Robust Few-Shot Learning: A Meta-Learning Approach. Neural Information Processing Systems. 33. 17886–17895. 2 indexed citations

18.

Goldblum, Micah, Jonas Geiping, Avi Schwarzschild, Michael Moeller, & Tom Goldstein. (2020). Truth or backpropaganda? An empirical investigation of deep learning theory. International Conference on Learning Representations. 2 indexed citations

19.

Wu, Zuxuan, et al.. (2020). Preparing for the Worst: Making Networks Less Brittle with Adversarial Batch Normalization. arXiv (Cornell University). 2 indexed citations

20.

Goldblum, Micah, Liam Fowl, & Tom Goldstein. (2019). Robust Few-Shot Learning with Adversarially Queried Meta-Learners. arXiv (Cornell University). 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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