What is he best known for?
The fields of study Alexandre Gramfort is best known for:
- Statistics
- Functional magnetic resonance imaging
- Electroencephalography
Alexandre Gramfort connects relevant research areas such as Deep learning and Multivariate statistics in the realm of Machine learning.
The study of Multivariate statistics is intertwined with the study of Machine learning in a number of ways.
While working on this project, he studies both Artificial intelligence and Deep learning.
His Artificial intelligence research extends to the thematically linked field of Pattern recognition (psychology).
Alexandre Gramfort incorporates Electroencephalography and Local field potential in his research.
Alexandre Gramfort merges Local field potential with Electroencephalography in his research.
He conducts interdisciplinary study in the fields of Programming language and Preprocessor through his works.
His work blends Preprocessor and Programming language studies together.
He performs integrative study on Neuroscience and Functional magnetic resonance imaging in his works.
His most cited work include:
- Scikit-learn: Machine Learning in Python (8338 citations)
- MNE software for processing MEG and EEG data (1248 citations)
- Machine learning for neuroimaging with scikit-learn (1045 citations)
What are the main themes of his work throughout his whole career to date
Artificial intelligence is closely attributed to Pattern recognition (psychology) in his work.
His work on Pattern recognition (psychology) is being expanded to include thematically relevant topics such as Artificial intelligence.
His research links Brain activity and meditation with Electroencephalography.
His Electroencephalography research extends to Brain activity and meditation, which is thematically connected.
He merges many fields, such as Machine learning and Statistics, in his writings.
In his study, Alexandre Gramfort carries out multidisciplinary Statistics and Algorithm research.
By researching both Algorithm and Machine learning, Alexandre Gramfort produces research that crosses academic boundaries.
In his works, he undertakes multidisciplinary study on Neuroscience and Functional magnetic resonance imaging.
He integrates Functional magnetic resonance imaging with Neuroscience in his research.
Alexandre Gramfort most often published in these fields:
- Artificial intelligence (72.04%)
- Pattern recognition (psychology) (46.24%)
- Electroencephalography (43.01%)
What were the highlights of his more recent work (between 2020-2022)?
- Artificial intelligence (70.00%)
- Programming language (50.00%)
- Machine learning (40.00%)
In recent works Alexandre Gramfort was focusing on the following fields of study:
Alexandre Gramfort connects relevant research areas such as Python (programming language), Documentation, Set (abstract data type) and Comprehension in the realm of Programming language.
He regularly links together related areas like Programming language in his Set (abstract data type) studies.
Alexandre Gramfort is doing genetic studies as part of his Magnetoencephalography and Brain activity and meditation and Electroencephalography investigations.
He conducts interdisciplinary study in the fields of Magnetoencephalography and Electroencephalography through his works.
His Linguistics study typically links adjacent topics like Narrative, Comprehension and Feature (linguistics).
His research is interdisciplinary, bridging the disciplines of Linguistics and Feature (linguistics).
His research brings together the fields of Segmentation and Artificial intelligence.
The study of Machine learning is intertwined with the study of Labeled data in a number of ways.
His studies link Artificial intelligence with Pattern recognition (psychology).
Between 2020 and 2022, his most popular works were:
- Uncovering the structure of clinical EEG signals with self-supervised learning (56 citations)
- POT : Python Optimal Transport (37 citations)
- A unified view on beamformers for M/EEG source reconstruction (27 citations)
In his most recent research, the most cited works focused on:
- Beamforming
- Adaptive beamformer
- Artificial neural network
Python (programming language) and Documentation are all intertwined in Programming language research.
Documentation and Programming language are frequently intertwined in his study.
His research on Electroencephalography often connects related topics like Psychiatry.
Psychiatry is closely attributed to Electroencephalography in his work.
His Artificial intelligence study typically links adjacent topics like Pattern recognition (psychology).
His Pattern recognition (psychology) study frequently draws connections to other fields, such as Artificial intelligence.
In his works, he undertakes multidisciplinary study on Machine learning and Speech recognition.
Alexandre Gramfort incorporates Speech recognition and Machine learning in his studies.
He conducts interdisciplinary study in the fields of Neuroscience and Magnetoencephalography through his works.
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