Hamed Zamani

All four engines identify Hamed Zamani as a faculty member at the University of Massachusetts Amherst working on information retrieval and retrieval-augmented generation. Copilot adds specific methods such as Stochastic RAG, Unified RAG, and Plan-and-Refine, while Google AI Mode adds personalized retrieval-augmented reasoning and multi-step context discovery. Gemini adds agentic search and document ranking. All four engines name him. The engines cite groups.cs.umass.edu most often, including research topics and publications pages, and arxiv.org for papers such as Stochastic RAG.

• ChatGPTNamed in 1 answers

  Hamed Zamani is an Associate Professor at the University of Massachusetts Amherst and Associate Director of CIIR, researching information retrieval and RAG context engineering.

  Known for

  • information retrieval
  • conversational search
  • retrieval-enhanced machine learning
• GeminiNamed in 2 answers

  Hamed Zamani is an Associate Professor at the Manning College of Information and Computer Sciences at UMass Amherst, focusing on information retrieval, search architectures, RAG, and personalized language models.

  Known for

  • personalized RAG
  • agentic search
  • document ranking
• Google AI ModeNot named

  Hamed Zamani is an Associate Professor at UMass Amherst's Manning College of Information and Computer Sciences and Associate Director of CIIR, researching RAG and context management.

  Known for

  • personalized retrieval-augmented reasoning
  • multi-step context discovery
  • neural information retrieval
• PerplexityNot named

  No description yet.

• Bing AI ModeNot named

  No description yet.

• CopilotNot named

  Hamed Zamani is a leading researcher in retrieval-augmented generation and context engineering known for methods that optimize retrieval, compression, and integration of external knowledge.

  Known for

  • Stochastic RAG
  • Unified RAG
  • Plan-and-Refine

• #60RAGof 64
  Named in 5 answers

1. 1
   Top Research Papers on Retrieval Augmented Generation

   paperguide.ai2 citations
2. 2
   https://aclanthology.org/2026.findings-acl.78/

   aclanthology.org1 citations
3. 3
   https://arxiv.org/abs/2603.09891

   arxiv.org1 citations