Prerequisites
Graduate standing or an undergraduate course on probability and/or statistics. Typical courses include, but are not limited to, ME EN 2550, CS3130, ECE3530, or MATH 3070
Student Learning Objectives
Upon successful completion of this course, students shall be able to:
- Identify the basic anatomical brain regions and neurotransmitters that have been correlated with intellectual and emotional pathways.
- Explain key neuroscience concepts relevant to engineering leadership, including how the brain processes information, emotion, and social interaction.
- Identify cognitive shortcuts and biases (such as heuristics, confirmation bias, and threat responses) that influence decision-making and behavior in technical and organizational contexts.
- Assess their own emotional style using neuroscience-informed frameworks, recognizing both strengths and areas for improvement.
- Apply neuroscience principles to enhance self-awareness, emotional regulation, and stress management as a leader.
- Develop strategies to optimize brain performance of multidisciplinary teams, including methods for improving focus, memory, creativity, and motivation.
- Design personal leadership development plans that incorporate neuroscientific insights to support long-term growth, career development, and skills to influence without authority.
- Analyze the approaches and career decisions taken by current senior industry leaders in the realm of personal leadership and team development.
Course Description
This course explores the neuroscience, psychology, and communication dynamics that shape how leaders think, decide, and influence others. Drawing from cutting-edge research and practical frameworks, students will examine how cognitive processes, emotional regulation, and conversational strategies impact leadership effectiveness and organizational outcomes in engineering enterprises. By integrating theory with real-world applications, students will develop insight into “mental drivers” behind their personal management style along with tools to recognize biases, manage emotions, improve decision quality, and foster constructive dialogue in STEM industries.