{
 "type": "researcher",
 "question": "Who collaborates with Jonas Geiping?",
 "id": "A5049400969",
 "name": "Jonas Geiping",
 "url": "https://researcher-atlas.com/researcher/jonas-geiping-a5049400969/",
 "total_coauthors": 100,
 "displayed_coauthors": 20,
 "display_limit": 20,
 "data_version": "2026.07-r1",
 "updated": "2026-07-28",
 "source": "OpenAlex (CC0)",
 "answer": "Atlas records 100 co-authors for Jonas Geiping, who works on Adversarial Robustness in Machine Learning; the 20 strongest connections are shown. The strongest collaboration is with Tom Goldstein (61 joint papers).",
 "metrics": {
  "cited_by_count": 1999,
  "works_count": 107
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 "affiliation_latest": "ELLIS Institute &amp;  Max-Planck Institute for Intelligent Systems, Tübingen AI Center",
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   "name": "Guinan Su",
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   "name": "Alexander Panfilov",
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   "name": "Yanwu Yang",
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   "name": "Hamid Kazemi",
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   "name": "Avi Schwarzschild",
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   "url": "https://researcher-atlas.com/researcher/avi-schwarzschild-a5026712341/"
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  {
   "name": "Mrinmaya Sachan",
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   "url": "https://researcher-atlas.com/researcher/mrinmaya-sachan-a5002316432/"
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  {
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 ],
 "topics": [
  {
   "id": "T11689",
   "name": "Adversarial Robustness in Machine Learning",
   "works_count": 35,
   "subfield": "Artificial Intelligence",
   "field": "Computer Science"
  },
  {
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   "name": "Topic Modeling",
   "works_count": 18,
   "subfield": "Artificial Intelligence",
   "field": "Computer Science"
  },
  {
   "id": "T10764",
   "name": "Privacy-Preserving Technologies in Data",
   "works_count": 18,
   "subfield": "Artificial Intelligence",
   "field": "Computer Science"
  },
  {
   "id": "T10181",
   "name": "Natural Language Processing Techniques",
   "works_count": 16,
   "subfield": "Artificial Intelligence",
   "field": "Computer Science"
  },
  {
   "id": "T10036",
   "name": "Advanced Neural Network Applications",
   "works_count": 14,
   "subfield": "Computer Vision and Pattern Recognition",
   "field": "Computer Science"
  }
 ],
 "topics_note": "OpenAlex exposes only this author's top 5 topics.",
 "papers": [
  {
   "title": "Inverting Gradients -- How easy is it to break privacy in federated\n learning?",
   "year": 2020,
   "cited_by_count": 390
  },
  {
   "title": "A Cookbook of Self-Supervised Learning",
   "year": 2023,
   "cited_by_count": 163
  },
  {
   "title": "Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models",
   "year": 2023,
   "cited_by_count": 145
  },
  {
   "title": "Universal Guidance for Diffusion Models",
   "year": 2024,
   "cited_by_count": 128
  },
  {
   "title": "A Watermark for Large Language Models",
   "year": 2023,
   "cited_by_count": 113
  },
  {
   "title": "Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise",
   "year": 2023,
   "cited_by_count": 105
  },
  {
   "title": "MetaPoison: Practical General-purpose Clean-label Data Poisoning",
   "year": 2020,
   "cited_by_count": 81
  },
  {
   "title": "Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery",
   "year": 2023,
   "cited_by_count": 40
  }
 ]
}