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Ashwinee Panda

Ashwinee Panda has 2 papers in our library, in 2023–2024, most of them on Bias, fairness & safety, Language models & pretraining; often with Peter Henderson, Prateek Mittal, Xiangyu Qi.

2 papers in our library · 2023–2024

OpenAlex · h-index 5 · 35 works · 266 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 194 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5263

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Maryland, College ParkCollege Park · United States2025–2026 · 6 paperscurrent🎓Princeton UniversityPrinceton · United States2021–2025 · 8 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 workSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Bias, fairness & safety · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Visual Adversarial Examples Jailbreak Aligned Large Language Models (2023 · 240 citations)
    Adding image inputs to large language models creates new security risks. The authors argue that images are continuous and high-dimensional, which makes them much easier to manipulate adversarially than discrete text. Because these models can do so many things, a successful attack can do more than cause a…
  2. Safety Alignment Should Be Made More Than Just a Few Tokens Deep (2024 · 97 citations)
    The paper asks why safety-aligned LLMs are so easy to break, whether through adversarial suffixes, prefilling, tweaked decoding parameters, or a few steps of fine-tuning.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Visual Adversarial Examples Jailbreak Aligned Large Language ModelsProceedings of the AAAI Conference on Artificial IntelligenceXiangyu Qi, Kaixuan Huang, Ashwinee Panda, et al.2023 · 240 citations
  2. Safety Alignment Should Be Made More Than Just a Few Tokens DeeparXiv:2406.05946Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, et al.2024 · 97 citations
  3. FetchSGD: Communication-Efficient Federated Learning with Sketching ↗arXiv (Cornell University) · no summary yetDaniel Rothchild et al.2020 · 68 citations
  4. Beyond the Haystack: Sensitivity to Context in Legal Reference Recall ↗Proceedings of the Natural Legal Language Processing Workshop 2025 · no summary yetEric Xia et al.2025 · 2 citations

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 194 times (of the 149,439 papers whose bibliographies we read); 71 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/ashwinee-panda/