{
 "type": "researcher",
 "question": "Who collaborates with Leo Schwinn?",
 "id": "A5028233502",
 "name": "Leo Schwinn",
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 "data_version": "2026.07-r1",
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 "answer": "Atlas records 30 co-authors for Leo Schwinn, who works on Adversarial Robustness in Machine Learning; the 17 strongest connections are shown. The strongest collaboration is with Stephan Günnemann (24 joint papers).",
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 ],
 "topics": [
  {
   "id": "T11689",
   "name": "Adversarial Robustness in Machine Learning",
   "works_count": 28,
   "subfield": "Artificial Intelligence",
   "field": "Computer Science"
  },
  {
   "id": "T11512",
   "name": "Anomaly Detection Techniques and Applications",
   "works_count": 11,
   "subfield": "Artificial Intelligence",
   "field": "Computer Science"
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   "subfield": "Signal Processing",
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 ],
 "topics_note": "OpenAlex exposes only this author's top 5 topics.",
 "papers": [
  {
   "title": "Exploring misclassifications of robust neural networks to enhance adversarial attacks",
   "year": 2023,
   "cited_by_count": 52
  },
  {
   "title": "Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring",
   "year": 2021,
   "cited_by_count": 31
  },
  {
   "title": "System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data",
   "year": 2021,
   "cited_by_count": 22
  },
  {
   "title": "Artificial intelligence trend analysis on healthcare podcasts using topic modeling and sentiment analysis: a data-driven approach",
   "year": 2023,
   "cited_by_count": 13
  },
  {
   "title": "Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study",
   "year": 2023,
   "cited_by_count": 13
  },
  {
   "title": "Adversarial Attacks and Defenses in Large Language Models: Old and New Threats",
   "year": 2023,
   "cited_by_count": 12
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  {
   "title": "Can you smell my stress? Influence of stress chemosignals on empathy and emotion recognition in depressed individuals and healthy controls",
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   "cited_by_count": 10
  },
  {
   "title": "Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks",
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   "cited_by_count": 5
  }
 ]
}