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Adeyemi Akintonde
Affiliation
Medical Wireless Sensing Ltd., London, U.K.
Meta Materials Inc., London, U.K.
Meta Materials Inc., London, U.K.
Topic
Respiratory Motion,18F-FDG,Blood Glucose,Blood Glucose Readings,Blood Glucose Values,Clinical Data,Continuous Glucose Monitoring,Data Frame,Feature Matrix,Forward Projection,Glucose Detection,Glucose Monitoring,Glucose Tolerance Test,Glucose Values,Healthy Subjects,Image Reconstruction,Image Registration,Intravenous Glucose Tolerance Test,Machine Learning Models,Machine Learning Models For Prediction,Mean Absolute Percentage Error,Minimally Invasive,Mixed Dataset,Motion Correction,Motion Model,Multi-sensor System,Near-infrared Data,Non-invasive Glucose,Positron Emission Tomography Data,Prediction Model,Pressure Data,Project Data,Radio Frequency Data,Random Forest,Random Forest Model,Random Forest Regression Model,Raw Data,Respiratory Signals,Root Mean Square Error,Rotational Motion,Simulated Phantom,Skin Temperature,Sum Of Squared Differences,Training Set,Tumor Regions,Variance In The Data,mmWave Signals,
Biography
Adeyemi Akintonde received the Ph.D. degree from University College London, London, U.K., in 2020.
He is a data scientist/machine learning engineer with a strong academic background in biomedical engineering and medical imaging computing. He excels in implementing state-of-the-art machine learning and deep learning techniques. With a passion for pushing the boundaries of data science and machine learning, he is dedicated to leveraging advanced methodologies to tackle complex challenges and drive meaningful insights in the field.
He is a data scientist/machine learning engineer with a strong academic background in biomedical engineering and medical imaging computing. He excels in implementing state-of-the-art machine learning and deep learning techniques. With a passion for pushing the boundaries of data science and machine learning, he is dedicated to leveraging advanced methodologies to tackle complex challenges and drive meaningful insights in the field.