Quan Mai Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
unknown
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Quan Mai's research focuses on the application of advanced machine learning techniques to analyze complex biological and medical data. His recent work includes the development of a multi-module recurrent convolutional neural network with a Transformer encoder for classifying ECG arrhythmias, presented in 2021. In 2022, he contributed to the creation of BrainVGAE, an end-to-end graph neural network designed for processing noisy fMRI datasets. Mai has collaborated with researchers from the University of Arkansas at Little Rock and within the University of Arkansas at Fayetteville, contributing to shared publications in these areas. His scholarship is marked by a growing body of work in computational approaches to health data analysis, indicated by his recent activity and publication record.
Metrics
- h-index: 2
- Publications: 2
- Citations: 43
Selected Publications
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BrainVGAE: End-to-End Graph Neural Networks for Noisy fMRI Dataset (2022)
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Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification (2021)
Collaboration Network
Top Collaborators
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
- BrainVGAE: End-to-End Graph Neural Networks for Noisy fMRI Dataset
- BrainVGAE: End-to-End Graph Neural Networks for Noisy fMRI Dataset
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