About

Dr. Dallas Card is an Assistant Professor of Information in the School of Information at the University of Michigan, Ann Arbor, where he works at the intersection of machine learning, natural language processing (NLP), and data science with a strong focus on understanding society through text data and improving the reliability and responsibility of machine learning systems. Prior to joining Michigan, he was a postdoctoral scholar in the Stanford Natural Language Processing Group and Stanford Data Science Institute, and he holds a Ph.D. in Machine Learning from Carnegie Mellon University, where he was advised by Noah A. Smith. Dr. Card’s research explores computational methods for textual data analysis, including projects on historic text mining to study social and political phenomena, bias in machine learning systems (such as racial bias in hate speech detection), methods for low‑resource text classification, and broader questions about reproducibility and interpretability in machine learning research. His work combines technical innovations in NLP with applications in computational social science, enabling insights into historical discourse, framing, and how models behave on real‑world text corpora. At the University of Michigan, Card contributes to interdisciplinary research and teaching that bridges computing with social inquiry, and he participates in events and collaborations across AI, data science, and information studies. His scholarship continues to advance both methodological rigor and societal relevance in machine learning and NLP.

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