Erin Calipari.

Erin Calipari

Director, Vanderbilt Center for Addiction Research
Associate professor of pharmacology, Vanderbilt University

Erin Calipari is director of the Vanderbilt Center for Addiction Research and associate professor of pharmacology at Vanderbilt University. She is a neuroscientist whose work focuses on understanding how the brain’s reward and motivation systems adapt to experience, and how these processes become dysregulated in addiction.

As director of the Vanderbilt Center for Addiction Research, Calipari leads a multidisciplinary group of faculty, trainees and staff working to identify the biological, environmental and developmental factors that confer risk for addiction. Under her leadership, the center also engages in targeted outreach to inform and empower communities through evidence-based education on the science of addiction.

Calipari earned her Ph.D. in neuroscience at Wake Forest University School of Medicine, where she studied how drugs of abuse alter dopaminergic signaling to shape addictive behaviors. She then completed her postdoctoral training at the Icahn School of Medicine at Mount Sinai, using advanced genetic and molecular approaches to investigate how drugs remodel brain circuits and influence behavior. Across her career, she has integrated behavioral, circuit-level, microcircuit and molecular techniques to uncover the mechanisms that govern adaptive and maladaptive learning. Her work has led to more than 100 publications and numerous honors, including the Presidential Early Career Award for Scientists and Engineers from the White House.

Explore more from The Transmitter

Illustration of an open journal featuring lines of text and small illustrations of eyes and mouths.

Autism-linked genes alter sleep behavior, and more

Here is a roundup of autism-related news and research spotted around the web for the week of 13 April.

By Jill Adams
14 April 2026 | 2 min read
Illustration of a sheet of paper with a topography map-like pattern on it.

Why neural foundation models work, and what they might—and might not—teach us about the brain

These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?

By Juan Gallego
13 April 2026 | 8 min read
A fragmenting cube hovers over a person reading a book.

Error equation predicts brain’s ability to generalize

Four statistical measurements of neural network geometry capture how well brains and artificial networks use what they already know to solve new problems, a study suggests.

By Natalia Mesa
10 April 2026 | 5 min read