1). Brain-behavior mapping in health, multiple sclerosis, stroke and traumatic brain injury
2). Network Control Theory models to link brain structure and function
3). The effect of psychedelics in the brain
4). Sex differences in brain structure and function
5). Brain differences in individuals with substance use disorder
6). Machine learning models to map brain structure and function
7). Using AI generated visual stimuli for neuromodulation
The Computational Connectomics (CoCo) Laboratory focuses on using quantitative methods applied to MRI-based measures of brain anatomy and physiology to understand the mysteries of the human brain, including in health and disease/disorder.
Dr. Kuceyeski was awarded her PhD in Applied Mathematics in 2009 from Case Western Reserve University, after which she moved to Weill Cornell Medicine to complete her postdoctoral training in quantitative neuroimaging. Her postdoctoral training focused on development of tools to analyze MRI-based neuroimaging data and brain-behavior mapping. She has been at Weill Cornell for 14 years, where her group focuses on using quantitative approaches, including machine learning, to understand how the human brain works. Amy is also the founder and co-director of the cross-campus working group Machine Learning in Medicine, which aims to bring together ML researchers in Cornell-Ithaca/Cornell-Tech and clinicians and researchers at WCM to address medicine's toughest problems.
Distinctions:
Distinguished Investigator Award, The Academy for Radiological and Biomedical Imaging Research (2022).
Founded the Machine Learning in Medicine inter-campus working group, (2016).
