Shelly Cooper

Shelly Cooper is a Lecturer in the Department of Psychological & Brain Sciences at Washington University. She serves as a faculty statistical consultant and teaches undergraduate and graduate courses in statistics and neuroscience, including Experimental Psychology, Applied Statistical Analysis with R, The General Linear Model and Beyond, Quantitative Methods I and II, Biological Psychology, and Advanced Cognitive, Computational, and Systems Neuroscience.

Her academic training includes a B.A. in Psychology from Boston University, followed by doctoral studies at Washington University, where she completed the Cognitive, Computational, and Systems Neuroscience (CCSN) pathway and earned a certificate in Quantitative Data Analysis. Prior to her doctoral work, she spent five years at San Francisco General Hospital/UCSF.

• B.A., Psychology – Boston University
• Ph.D. – Washington University
• Certificate – Quantitative Data Analysis, Washington University
• Pathway – Cognitive, Computational, and Systems Neuroscience (CCSN)

Dr. Cooper's research and teaching interests center on quantitative methods in psychology and neuroscience, with a strong emphasis on applied statistical modeling and data science using R. Her expertise spans experimental design, statistical inference, and computational approaches to cognitive and brain sciences.

She is actively involved in the Cognitive, Computational, and Systems Neuroscience (CCSN) pathway and co-organizes the St. Louis chapter of R-Ladies, promoting inclusivity and skill development in data science.

There are no scientific awards listed in the provided text.

Dr. Cooper advises students through her teaching and mentoring in quantitative courses and research methods. She supports graduate and undergraduate training in statistical analysis and experimental design. There is no mention of external grants or funded research projects in the provided information.

She contributes to the academic community through her role in the CCSN pathway and as a co-organizer of the St. Louis R-Ladies chapter, fostering collaborative learning and professional development in data science and neuroscience.