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Dongyeop Kang

Dongyeop Kang

University of Minnesota – CS & Engineering

Builds human-centric NLP systems through cognitively-aligned models and interactive AI. Research bridges language and cognition to support reading, writing, and reasoning at scale.

Selected works

  • Mary, the Cheeseburger-Eating Vegetarian: Do LLMs Recognize Incoherence? – EACL 2026
  • Strong Memory, Weak Control: Executive Functioning in LLMs – EACL 2026
  • How LLMs Comprehend Temporal Meaning in Narratives – ACL 2025
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Varun Athilat

Varun Athilat

University of Minnesota – Educational Psychology

Studies the interplay between text and emotion — how language shapes how we feel, and how feelings shape how we read. Research spans multiple-document comprehension, persuasion, and typographical cues as proxies for prosody.

Sashank Varma

Sashank Varma

Georgia Tech – School of Interactive Computing

Cognitive scientist investigating the alignment between how humans and Large Language Models understand language and perform cognitive tasks. Uses computational modeling and behavioral experiments.

Selected works

  • Modeling story-based analogy understanding using LLMs – CogSci 2025
  • Incremental comprehension of garden-path sentences by LLMs – CogSci 2024
  • Development of cognitive intelligence in pre-trained language models – EMNLP 2024
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Evelyn Milburn

Evelyn Milburn

North Dakota State University – Psychology

Investigates how we use knowledge beyond words to flexibly comprehend real-life language, including figurative language, language learning, and cognitive aging. Runs the Experimental Cognition and Language Lab at NDSU.

Selected works

  • Native speakers kick buckets but learners kick doors – Memory & Cognition, 2026
  • Online processing of anomalous learner syntax – Applied Psycholinguistics, 2023
  • Idioms show effects of meaning relatedness and dominance – Psychonomic Bulletin, 2019
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