Organizations run on people, but how do we make evidence-based decisions about them? This course explores the dynamic intersection of psychological science and applied data analytics. Students will dive into real-world workforce datasets to solve critical organizational challenges, from predicting employee turnover and optimizing team dynamics to evaluating human-AI collaboration and auditing algorithmic fairness. By blending statistical methodology with psychological theory, this course equips students with the tools to translate complex workforce metrics into actionable, ethical, and data-driven strategies for modern leadership.

An analysis of drug-behavior interactions with emphasis on basic pharmacology, role of contingencies in drug effects, mechanisms of drug dependency, drugs as stimuli, and basic neuropharmacology.

How do organizations identify, assess, and hire top talent while navigating complex legal, ethical, and psychometric landscapes? This advanced doctoral seminar offers a rigorous exploration of the theory and practice of employee selection. Rooted in the industrial side of I-O psychology, the course critically examines the criterion problem, validation strategies, and diverse assessment modalities—from traditional cognitive tests and structured interviews to the frontiers of algorithmic and AI-driven hiring. Students will bridge the gap between academic research and applied practice, learning to design legally defensible selection systems, evaluate assessment fairness, and translate complex psychometric data into strategic, evidence-based organizational insights.

For graduate students holding a research assistantship.

Organizations run on people, but how do we make evidence-based decisions about them? This course explores the dynamic intersection of psychological science and applied data analytics. Students will dive into real-world workforce datasets to solve critical organizational challenges, from predicting employee turnover and optimizing team dynamics to evaluating human-AI collaboration and auditing algorithmic fairness. By blending statistical methodology with psychological theory, this course equips students with the tools to translate complex workforce metrics into actionable, ethical, and data-driven strategies for modern leadership.

The first of a two course sequence in which selected students conduct original work under the direction of a faculty member. The student will produce a proposal for research that will be undertaken during the second course (PSYC 4601).

Describes the characteristics of interaction between humans and computers and demonstrates techniques for the design and evaluation of user interfaces and computational systems.

Students conduct research under the direction of a faculty member on problems in cognition and brain sciences.

Students conduct research under the direction of a faculty member on problems in the general area of human-computer interaction.