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Michael J Kleiman

Michael J Kleiman

Michael is a Research Assistant Professor at the University of Miami Miller School of Medicine's Comprehensive Center for Brain Health. He specializes in using artificial intelligence and machine learning techniques to investigate the cognitive behavior and medical records of patients with Alzheimer's disease and other forms of dementia, enabling the disorders to be more accurately and thoroughly researched.

Michael received his doctorate from Florida Atlantic University by exploring the ability to streamline Alzheimer's detection in clinical settings using electronic medical records. He has since received awards from the Alzheimer's Association, the American Academy of Neurology, the McKnight Brain Research Foundation, and the Florida Department of Health.

 

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What I Do

Behavior Analysis

Michael specializes in using behavior (gaze, speech, physical movements and computer mouse movements) to investigate how and what people are currently thinking and how that thinking compares to others, enabling the measurement of cognitive functioning and the detection of psychological disorders and personality types.

Presenting and Teaching

Michael is highly proficient in presenting complex subjects to the general public, having presented to investors and judges as Founder of his company SciKey, disseminated research at scientific conferences, taught neuroscience, psychology, statistics, and scientific writing to undergraduates, tutored high school and undergraduate students in psychology, statistics, and jazz, and proposed/defended his master's and doctoral thesis.

Data Science

Using Python and R, Michael is able to quickly analyze and transform data, identify trends using statistical expertise, develop machine learning algorithms and pipelines, and prepare professional presentations and memos.

Computer Vision

Michael has experience using convolutional neural networks, including capsule-based derivatives, to classify image and video data. Examples include detecting Alzheimer's disease from volumetric MRI scans and identifying a person's current task through translating time series eye movement behavior into CNN-readable sequential image formats.

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