What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Neuroscience
The scientist’s investigation covers issues in Neuroscience, Artificial intelligence, Bayesian probability, Communication and Machine learning.
His Neuroscience research includes elements of Artificial neural network and Scalability.
His work in the fields of Artificial intelligence, such as Support vector machine, overlaps with other areas such as Accelerometer.
His work on Bayesian statistics and Bayesian inference as part of general Bayesian probability research is often related to Estimation and Tennis ball, thus linking different fields of science.
His Communication research integrates issues from Control theory, Movement, Cognitive science, Adaptation and Visual cortex.
While the research belongs to areas of Machine learning, Konrad P. Kording spends his time largely on the problem of Bayes' theorem, intersecting his research to questions surrounding Normal distribution, Bioinformatics and Prior probability.
His most cited work include:
- Bayesian integration in sensorimotor learning (1402 citations)
- Causal inference in multisensory perception. (619 citations)
- Bayesian decision theory in sensorimotor control. (574 citations)
What are the main themes of his work throughout his whole career to date?
Konrad P. Kording spends much of his time researching Artificial intelligence, Neuroscience, Machine learning, Artificial neural network and Bayesian probability.
His work is dedicated to discovering how Artificial intelligence, Movement are connected with Communication and other disciplines.
His study in Sensory system, Electrophysiology, Perception, Saccade and Stimulus is carried out as part of his studies in Neuroscience.
Many of his studies on Machine learning involve topics that are commonly interrelated, such as Kalman filter.
The Artificial neural network study combines topics in areas such as Nonlinear system and Code.
Bayes' theorem, Prior probability, Bayesian statistics and Bayesian inference are among the areas of Bayesian probability where the researcher is concentrating his efforts.
He most often published in these fields:
- Artificial intelligence (42.20%)
- Neuroscience (25.85%)
- Machine learning (19.51%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (42.20%)
- Artificial neural network (14.39%)
- Neuroscience (25.85%)
In recent papers he was focusing on the following fields of study:
Konrad P. Kording focuses on Artificial intelligence, Artificial neural network, Neuroscience, Machine learning and Algorithm.
The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Pattern recognition.
His Artificial neural network study combines topics from a wide range of disciplines, such as Set, Code, Nonlinear system and Function, Topology.
His Machine learning study combines topics in areas such as Scalability and Connectome.
His study looks at the relationship between Algorithm and fields such as Learning rule, as well as how they intersect with chemical problems.
His Perception research is multidisciplinary, incorporating elements of Infinite set, Granger causality and Causal model.
Between 2017 and 2021, his most popular works were:
- A deep learning framework for neuroscience (199 citations)
- A deep learning framework for neuroscience (199 citations)
- Tackling Climate Change with Machine Learning (112 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Machine learning, Artificial neural network, Deep learning and Neuroscience.
His work on Hybrid learning as part of general Artificial intelligence research is frequently linked to Simple, bridging the gap between disciplines.
His Machine learning research includes themes of Climate change, Emergency management, Humanity, Smart grid and Greenhouse gas.
His Artificial neural network research focuses on Code and how it relates to Neural decoding, Gradient boosting, Decoding methods, Task and Kalman filter.
Many of his research projects under Deep learning are closely connected to Systems neuroscience with Systems neuroscience, tying the diverse disciplines of science together.
When carried out as part of a general Neuroscience research project, his work on Perception and Cognition is frequently linked to work in Premotor cortex, therefore connecting diverse disciplines of study.
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.
