Alexander Weigard, PhD

Alexander Weigard is a computational cognitive neuroscientist and Assistant Professor at the University of Michigan Addiction Center in the Department of Psychiatry. He is interested in using computational and network modeling methods to better characterize cognitive and neural risk factors for the development of attention problems and substance use disorders. He hopes to integrate this work with machine learning to enhance the real-world prediction of problematic substance use, psychopathology and associated outcomes.

Dr. Weigard uses computational and data science methodologies to better understand behavioral and neural risk factors for the development of attention problems and substance use disorders. His work involves a combination of formal modeling methods that allow mechanistic theories about behavioral and brain processes to be clearly specified and tested along with data-driven machine learning methods that can be used to predict clinically relevant outcomes from high-dimensional and multimodal data. His ultimate goal is to leverage this work to improve the real-world prediction and prevention of problematic substance use, psychopathology, and associated outcomes.