Andreas Stuhlmüller
- Artificial Intelligence top 5%
- Language and Linguistics top 5%
- Cognitive Neuroscience
- Developmental and Educational Psychology
- Experimental and Cognitive Psychology
- Co-authors
- Noah D. GoodmanDavid WingateJoshua B. TenenbaumTomer UllmanWilliam S. SaundersOwain EvansJudith DegenGirish Sastry
- Topics
- Language and cultural evolution (4 papers)Speech and dialogue systems (3 papers)Bayesian Modeling and Causal Inference (3 papers)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Andreas Stuhlmüller
11 papers receiving 340 citations
Peers
Comparison fields: 5 of 62
- Artificial Intelligence 220
- Language and Linguistics 74
- Cognitive Neuroscience 70
- Developmental and Educational Psychology 61
- Experimental and Cognitive Psychology 53
Countries citing papers authored by Andreas Stuhlmüller
This map shows the geographic impact of Andreas Stuhlmüller's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Andreas Stuhlmüller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Stuhlmüller more than expected).
Fields of papers citing papers by Andreas Stuhlmüller
This network shows the impact of papers produced by Andreas Stuhlmüller. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Andreas Stuhlmüller. The network helps show where Andreas Stuhlmüller may publish in the future.
Co-authorship network of co-authors of Andreas Stuhlmüller
This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Stuhlmüller. A scholar is included among the top collaborators of Andreas Stuhlmüller based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Andreas Stuhlmüller. Andreas Stuhlmüller is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 17 | |
| 2 | 22 | |
| 3 | C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching | 8 |
| 4 | Why do you ask? Good questions provoke informative answers. | 15 |
| 5 | Learning physical theories from dynamical scenes | 1 |
| 6 | Learning Stochastic Inverses | 32 |
| 7 | 15 | |
| 8 | 214 | |
| 9 | Knowledge and implicature: Modeling language understanding as social cognition | 3 |
| 10 | Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation | 43 |
| 11 | Learning Structured Generative Concepts | 7 |
About Andreas Stuhlmüller
Andreas Stuhlmüller is a scholar working on Cultural Studies, Artificial Intelligence and Developmental and Educational Psychology, having authored 11 papers that have together received 377 indexed citations. Recurring topics across this work include Language and cultural evolution (4 papers), Speech and dialogue systems (3 papers) and Bayesian Modeling and Causal Inference (3 papers). The work is most often cited by research in Language and Linguistics (74 citations), Artificial Intelligence (220 citations) and General Decision Sciences (12 citations). Andreas Stuhlmüller has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Noah D. Goodman, David Wingate, Joshua B. Tenenbaum, Tomer Ullman, William S. Saunders, Owain Evans, Judith Degen, Girish Sastry, Robert D. Hawkins and Daniel Ritchie. Their work appears in journals such as Cognitive Psychology, Cognitive Science and Topics in Cognitive Science.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.