Gregory Kiar

Biography
Gregory Kiar, PhD, is a research scientist at the Child Mind Institute. Throughout his degrees in Biomedical Engineering, Greg has developed techniques to study biosignal data, a turn-key tool that allows researchers to generate maps of brain connectivity from diffusion MRI data, and techniques to assess and improve the stability of research in neuroscience.
Greg’s research bridges the fields of numerical analysis and brain imaging to evaluate and improve the trustworthiness of techniques used to study the brain. He has developed infrastructures that support the reliable evaluation of neuroimaging experiments, studied the role that unavoidable computing errors play in results, and developed techniques to consider such errors when building robust models of the brain. His work ultimately seeks to inform decision-making surrounding robust data collection, image processing and biomarker discovery. Greg has considerable experience in computational statistics, uncertainty quantification, pipeline development and machine learning.
In addition to his research aims, Greg has participated in the organization of over a dozen hackathon-style events focused on training and collaboration, and taught several full-length courses at the university level. Greg regularly takes on mentorship opportunities, which are often geared towards teaching academic writing, fundamental computational skills or research best practices.
Greg’s research bridges the fields of numerical analysis and brain imaging to evaluate and improve the trustworthiness of techniques used to study the brain. He has developed infrastructures that support the reliable evaluation of neuroimaging experiments, studied the role that unavoidable computing errors play in results, and developed techniques to consider such errors when building robust models of the brain. His work ultimately seeks to inform decision-making surrounding robust data collection, image processing and biomarker discovery. Greg has considerable experience in computational statistics, uncertainty quantification, pipeline development and machine learning.
In addition to his research aims, Greg has participated in the organization of over a dozen hackathon-style events focused on training and collaboration, and taught several full-length courses at the university level. Greg regularly takes on mentorship opportunities, which are often geared towards teaching academic writing, fundamental computational skills or research best practices.
Presentations
Workshop
Online Only
Cloud and Distributed Computing
Scientific Computing
Workflows
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