The Department of Psychiatry will feature Dr. Tammy Chung as part of its Meet the PI lecture series. Dr. Chung’s research focuses on adolescent and young adult substance use: assessment and diagnosis, screening and brief intervention, and mechanisms underlying psychotherapy change.
Dr. Chung has used a range of methods that include mobile assessment, social network data, neuroimaging, and candidate genes to examine factors influencing the course of substance use across multiple levels of analysis. She serves as the Principal Investigator or Co-Investigator for a number of ongoing research projects funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and National Institute on Drug Abuse (NIDA). Dr, Chung is an Associate Editor for the journals Alcoholism: Clinical and Experimental Research and Psychology of Addictive Behaviors. Dr. Chung is a member of the training faculty for the department’s T32 Developmental Alcohol Research Training Program.
The entirety of this program will be a lecture by the speaker(s). All individuals able to control the content of this educational activity are required to disclose all relevant financial relationships with any proprietary entity producing, marketing, re-selling, or distributing health care goods or services, used on, or consumed by, patients. Registration is not required for this event. This event is free and there will be no refunds. The University of Pittsburgh is an affirmative action, equal opportunity institution
Location. Western Psychiatric Institute and Clinic Auditorium
For More Information. Please contact Frances Patrick at firstname.lastname@example.org or by calling 412-246-6787.
Learning Objectives. At the conclusion of this lecture, attendees will be able to:
Discuss factors associated with individual differences in response (e.g., rate and magnitude of change in alcohol use) to an alcohol text message intervention.
Explain how smartphones can be used to generate “digital phenotypes” that are relevant to behavioral and mental health.
Describe which smartphone-based sensor features (e.g., travel pattern, communication logs) are most useful in detecting alcohol use events in young adults.