Edward Raff

2.4k total citations · 1 hit paper
59 papers, 832 citations indexed

About

Edward Raff is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Edward Raff has authored 59 papers receiving a total of 832 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 17 papers in Signal Processing and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Edward Raff's work include Advanced Malware Detection Techniques (15 papers), Anomaly Detection Techniques and Applications (11 papers) and Adversarial Robustness in Machine Learning (10 papers). Edward Raff is often cited by papers focused on Advanced Malware Detection Techniques (15 papers), Anomaly Detection Techniques and Applications (11 papers) and Adversarial Robustness in Machine Learning (10 papers). Edward Raff collaborates with scholars based in United States, United Kingdom and United Arab Emirates. Edward Raff's co-authors include Charles Nicholas, Jared Sylvester, Mark McLean, Richard Zak, Stella Biderman, Sanjay Purushotham, Alham Fikri Aji, Niklas Muennighoff, Lintang Sutawika and Dragomir Radev and has published in prestigious journals such as Obstetrics and Gynecology, Journal of Machine Learning Research and Computers & Security.

In The Last Decade

Edward Raff

52 papers receiving 778 citations

Hit Papers

Crosslingual Generalization through Multitask Finetuning 2023 2026 2024 2025 2023 50 100 150
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.

  1. Crosslingual Generalization through Multitask Finetuning
    2023 190 citations Niklas Muennighoff, Lintang Sutawika et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Edward Raff United States 15 523 296 233 173 116 59 832
Yuanshun Yao United States 8 927 1.8× 317 1.1× 190 0.8× 126 0.7× 238 2.1× 11 1.1k
John Aycock Canada 14 421 0.8× 360 1.2× 392 1.7× 354 2.0× 71 0.6× 88 964
Shawn Shan United States 8 676 1.3× 215 0.7× 126 0.5× 66 0.4× 170 1.5× 12 824
Panagiotis Andriotis United Kingdom 12 169 0.3× 387 1.3× 284 1.2× 303 1.8× 61 0.5× 18 683
Eslam Amer Egypt 17 475 0.9× 417 1.4× 306 1.3× 326 1.9× 74 0.6× 44 952
Riivo Talviste Germany 4 468 0.9× 260 0.9× 240 1.0× 288 1.7× 49 0.4× 4 751
Md Jobair Hossain Faruk United States 10 182 0.3× 172 0.6× 163 0.7× 176 1.0× 32 0.3× 35 430
Zhijing Jin United States 12 807 1.5× 140 0.5× 35 0.2× 91 0.5× 116 1.0× 34 900
Alon Zakai Israel 9 298 0.6× 108 0.4× 164 0.7× 144 0.8× 116 1.0× 12 678
Sanaa Ghouzali Saudi Arabia 13 117 0.2× 220 0.7× 123 0.5× 253 1.5× 324 2.8× 53 648

Countries citing papers authored by Edward Raff

Since Specialization
Citations

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

Fields of papers citing papers by Edward Raff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Edward Raff

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

All Works

20 of 20 papers shown
1.
Raff, Edward, et al.. (2025). You Don’t Need Robust Machine Learning to Manage Adversarial Attack Risks. Proceedings of the AAAI/ACM Conference on AI Ethics and Society. 8(3). 2094–2106.
2.
Raff, Edward, et al.. (2024). WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions. Maryland Shared Open Access Repository (USMAI Consortium). 364–388.
3.
Roy, Saptarshi, et al.. (2024). Feature Selection from Differentially Private Correlations. 12–23. 1 indexed citations
4.
5.
Lu, Fred, et al.. (2024). High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. arXiv (Cornell University). 2037–2047. 1 indexed citations
6.
Lu, Fred, et al.. (2023). Differentially Private Logistic Regression with Sparse Solutions. 1–9. 4 indexed citations
7.
Lu, Fred, et al.. (2023). A Coreset Learning Reality Check. Proceedings of the AAAI Conference on Artificial Intelligence. 37(7). 8940–8948. 1 indexed citations
8.
Raff, Edward, et al.. (2023). When Visible-to-Thermal Facial GAN Beats Conditional Diffusion. 33. 181–185.
9.
Yong, Zheng, Niklas Muennighoff, Alham Fikri Aji, et al.. (2023). BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 11682–11703. 9 indexed citations
10.
Raff, Edward, et al.. (2022). Machine Learning for Malware Detection. WORLD SCIENTIFIC eBooks. 1 indexed citations
11.
Raff, Edward, et al.. (2022). Cross-Sectional Survey of High-Risk Pregnant Women's Opinions on COVID-19 Vaccination. Women s Health Reports. 3(1). 608–616. 10 indexed citations
12.
Raff, Edward, et al.. (2021). Intelligent Sight and Sound: A Chronic Cancer Facial Pain Dataset. Neural Information Processing Systems. 1 indexed citations
13.
Nguyen, André T., et al.. (2021). Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning. Maryland Shared Open Access Repository (USMAI Consortium). 1613–1622. 2 indexed citations
14.
Zhang, Wenbin, Mingli Zhang, Ji Zhang, et al.. (2020). Flexible and Adaptive Fairness-aware Learning in Non-stationary Data Streams. BOA (University of Milano-Bicocca). 399–406. 7 indexed citations
15.
Raff, Edward, et al.. (2020). The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data. arXiv (Cornell University). 9 indexed citations
16.
Raff, Edward. (2020). Research Reproducibility as a Survival Analysis. arXiv (Cornell University). 35(1). 469–478. 1 indexed citations
17.
Jauregui, Julio J., et al.. (2019). Early outcomes and complications of obese patients undergoing shoulder arthroplasty: A meta-analysis. Journal of Clinical Orthopaedics and Trauma. 11(Suppl 2). S260–S264. 12 indexed citations
18.
19.
Raff, Edward & Charles Nicholas. (2018). Hash-Grams. Maryland Shared Open Access Repository (USMAI Consortium). 1–4. 13 indexed citations
20.
Raff, Edward. (2017). JSAT: Java statistical analysis tool, a library for machine learning. Journal of Machine Learning Research. 18(1). 792–796. 30 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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