Dana Angluin

Dana Angluin is affiliated with Yale University in the United States and has an active research profile primarily in computer science, with a focus on artificial intelligence, computational theory, and electrical engineering. Their work intersects multiple subfields, ranging from automata theory to machine learning and natural language processing techniques.

Their research topics include:

• Semigroups and automata theory
• Machine learning and algorithms
• Ferroelectric and negative capacitance devices
• Natural language processing techniques
• Topic modeling
• DNA and biological computing
• Formal methods in verification

Several recent papers authored or coauthored by Dana Angluin span prominent publication venues. These include:

• "Regular ω-languages with an informative right congruence" (2020), published in Information and Computation
• "Strongly Unambiguous Büchi Automata Are Polynomially Predictable With Membership Queries" (2020), published by Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
• "Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity" (2022), published in Transactions of the Association for Computational Linguistics
• "What Formal Languages Can Transformers Express? A Survey" (2023), pre-published on arXiv (Cornell University)
• "What Formal Languages Can Transformers Express? A Survey" (2024), published in Transactions of the Association for Computational Linguistics

Their frequent coauthors include Dana Fisman, Lena Strobl, David Chiang, Robert Frank, and Yiding Hao. The collaboration network shows repeated partnerships, notably with Dana Fisman and Lena Strobl, indicating ongoing research in shared topics.

Dana Angluin's publications frequently appear in the following venues:

• arXiv (Cornell University)
• Transactions of the Association for Computational Linguistics
• Information and Computation
• Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
• Logical Methods in Computer Science

Their broader scholarly work spans 30 publications in computer science, with significant contributions to artificial intelligence and computational theory. Their interdisciplinary approach also connects with molecular biology and computer vision, reflecting a diverse research portfolio.