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Daniel McDuff

18 papers hereh-index 111.3k citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author13
  • last author1

Across the 16 of 18 papers where every author was matched, so the position is known.

fields
  • cs.AI4
  • cs.CL4
  • cs.CV3
  • cs.CY2
  • cs.LG2
  • cs.SE1
same name
  • Daniel McDuff — 5 papers, h 6
  • Daniel McDuff — 2 papers, h 2
  • Daniel McDuff — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedCapabilities of Gemini Models in Medicine

96 citations · 284 across the 18 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026★ 3 cited

Gemma 4 Technical Report

Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…

cs.CL2024★ 1 cited

A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making

Yubin Kim, Chanwoo Park, Hyewon Jeong +7

Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively. Large Language Models (…

cs.CL2024★ 9 cited

MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making

Yubin Kim, Chanwoo Park, Hyewon Jeong +7

Foundation models are becoming valuable tools in medicine. Yet despite their promise, the best way to leverage Large Language Models (LLMs) in complex medical tasks remains an open…

cs.CL2024★ 30 cited

Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data

Yubin Kim, Xuhai Xu, Daniel McDuff +2

Large language models (LLMs) are capable of many natural language tasks, yet they are far from perfect. In health applications, grounding and interpreting domain-specific and non-l…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.