What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Artificial intelligence, Topic model, Natural language processing, Machine learning and Information retrieval.
Mark Steyvers regularly links together related areas like Causal inference in his Artificial intelligence studies.
His Topic model study frequently draws parallels with other fields, such as Probabilistic logic.
His Natural language processing research incorporates themes from Semantics and Semantic memory.
Mark Steyvers studies Information retrieval, focusing on Latent Dirichlet allocation in particular.
In the subject of general Latent Dirichlet allocation, his work in Dynamic topic model and Pachinko allocation is often linked to Structure, thereby combining diverse domains of study.
His most cited work include:
- Finding scientific topics (4035 citations)
- The author-topic model for authors and documents (1237 citations)
- The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. (964 citations)
What are the main themes of his work throughout his whole career to date?
Mark Steyvers spends much of his time researching Artificial intelligence, Machine learning, Cognitive psychology, Bayesian inference and Topic model.
His Artificial intelligence research integrates issues from Pattern recognition, Semantic memory, Set and Natural language processing.
In his study, which falls under the umbrella issue of Machine learning, Ranking is strongly linked to Cognitive model.
His studies deal with areas such as Social psychology and Perception as well as Cognitive psychology.
His biological study spans a wide range of topics, including Prior probability, Hick's law, Context effect and Model selection.
His Topic model study is concerned with Information retrieval in general.
He most often published in these fields:
- Artificial intelligence (49.34%)
- Machine learning (20.39%)
- Cognitive psychology (18.42%)
What were the highlights of his more recent work (between 2017-2020)?
- Artificial intelligence (49.34%)
- Cognitive psychology (18.42%)
- Bayesian inference (17.11%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Cognitive psychology, Bayesian inference, Machine learning and Neuroimaging.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Natural language processing and Pattern recognition.
Mark Steyvers combines subjects such as Metacognition and Covariance with his study of Cognitive psychology.
His Bayesian inference study integrates concerns from other disciplines, such as Cued speech, Leverage and Task switching.
His work carried out in the field of Machine learning brings together such families of science as Generalization, Inference and Generalizability theory.
His work deals with themes such as Latent variable model, Algorithm, Labeled data and Generative model, which intersect with Inference.
Between 2017 and 2020, his most popular works were:
- Estimating COVID-19 Antibody Seroprevalence in Santa Clara County, California. A re-analysis of Bendavid et al. (37 citations)
- A tutorial on joint models of neural and behavioral measures of cognition (22 citations)
- Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability (13 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
The scientist’s investigation covers issues in Bayesian inference, Cognitive psychology, Artificial intelligence, Cognitive model and Statistics.
Mark Steyvers interconnects Latent variable model, Bayesian framework, Labeled data and Inference in the investigation of issues within Bayesian inference.
His work on Cued speech as part of general Cognitive psychology research is often related to Age groups, thus linking different fields of science.
His research combines Machine learning and Artificial intelligence.
His Cognitive model study combines topics from a wide range of disciplines, such as Covariance, Effects of sleep deprivation on cognitive performance and Joint.
His Statistics study spans across into subjects like Seroprevalence and Coronavirus disease 2019.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
