What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Katherine A. Heller focuses on Artificial intelligence, Data mining, Bayesian probability, Pattern recognition and Support vector machine. Her Artificial intelligence research integrates issues from Machine learning and Theoretical computer science. In the subject of general Machine learning, her work in Reinforcement learning is often linked to Weight-balanced tree, thereby combining diverse domains of study.
Katherine A. Heller works mostly in the field of Bayesian probability, limiting it down to concerns involving Exponential family and, occasionally, Principal component analysis, Hybrid Monte Carlo, Sparse PCA and Dimensionality reduction. The various areas that she examines in her Pattern recognition study include Latent Dirichlet allocation and Dirichlet process. In her study, Algorithm is strongly linked to Anomaly detection, which falls under the umbrella field of Support vector machine.
Her most cited work include:
- Bayesian hierarchical clustering (295 citations)
- One Class Support Vector Machines for Detecting Anomalous Windows Registry Accesses (147 citations)
- Bayesian Sets (146 citations)
What are the main themes of her work throughout her whole career to date?
Katherine A. Heller spends much of her time researching Artificial intelligence, Machine learning, Bayesian probability, Data mining and Algorithm. Her Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition. Her research investigates the link between Machine learning and topics such as Hidden Markov model that cross with problems in Covariate and Sigmoid function.
Her Bayesian inference, Bayes' theorem and Marginal likelihood study, which is part of a larger body of work in Bayesian probability, is frequently linked to Dynamics, bridging the gap between disciplines. Her work in Data mining covers topics such as Inference which are related to areas like Estimator and Personalized medicine. Her biological study spans a wide range of topics, including Mathematical optimization, Statistical model and Markov chain Monte Carlo.
She most often published in these fields:
- Artificial intelligence (62.39%)
- Machine learning (40.17%)
- Bayesian probability (28.21%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (62.39%)
- Machine learning (40.17%)
- Artificial neural network (11.11%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Artificial intelligence, Machine learning, Artificial neural network, Deep learning and Clinical decision support system. Her Artificial intelligence research is multidisciplinary, relying on both Stochastic process and Linear regression. Her research ties Bayesian probability and Machine learning together.
Her work in the fields of Bayesian probability, such as Bayes' theorem, overlaps with other areas such as Meta learning. Her research in Artificial neural network focuses on subjects like Parametric statistics, which are connected to Mathematical model, Algorithm, Probability density function and Stochastic differential equation. Her biological study deals with issues like Sepsis, which deal with fields such as Translational medicine.
Between 2018 and 2021, her most popular works were:
- Do no harm: a roadmap for responsible machine learning for health care. (111 citations)
- Do no harm: a roadmap for responsible machine learning for health care. (111 citations)
- Underspecification Presents Challenges for Credibility in Modern Machine Learning. (54 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Machine learning, Artificial intelligence, Bayesian probability, Artificial neural network and Software deployment. Her study on Deep learning is often connected to Domain as part of broader study in Machine learning. Her work deals with themes such as Ambiguity and Underspecification, which intersect with Deep learning.
Her work on Bayes' theorem as part of general Bayesian probability research is often related to Meta learning, thus linking different fields of science. Her Software deployment research spans across into fields like Psychological intervention, Context, MEDLINE, In patient and Medical education. Her Uncertainty quantification research incorporates themes from Subspace topology and Robustness.
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