PhD-BPD Dissertation Defense Presentation: Niloofar Nikookar

Title: Affective Atmospheres: An Interactive Lighting System to Enhance Emotional Experience in Indoor Spaces

Name: Niloofar Nikookar, Ph.D. candidate in Building Performance and Diagnostics (PhD-BPD)

Date: Tuesday, April 28, 2026
Time: 10:30am-12:30pm ET
Location: Intelligent Workplace (IW) Conference Room, MMCH 415 & Zoom

Advisory Committee:

Azadeh Sawyer, Ph.D. (Chair)
Assistant Professor in Building Technology
School of Architecture
Carnegie Mellon University

Mayank Goel, Ph.D.
Associate Professor
Software and Societal Systems Department (S3D) & Human-Computer Interaction Institute
Carnegie Mellon University

Motahhare Eslami, Ph.D.
Assistant Professor
Human-Computer Interaction Institute
Carnegie Mellon University

Siobhan Rockcastle, Ph.D.
Associate Professor
School of Architecture & Environment
University of Oregon

Abstract:
Indoor lighting significantly influences human affective state, satisfaction, and well-being in office environments. Most lighting systems prioritize task performance and energy efficiency, often neglecting users’ psychological and emotional needs. While the effects of lighting on human experience are well-documented, current lighting solutions fall short of addressing the complex, real-world conditions that shape how users perceive and respond to indoor environments. This dissertation identifies key research gaps: (1) indoor lighting remains static despite dynamic variations caused by environmental factors and user behavior; (2) adaptive systems rarely integrate established knowledge on lighting metrics and their impact on the affective experience; and (3) daylight and electric lighting are typically studied in isolation, limiting applicability to conditions where both are present.

To address these gaps, the study investigates how lighting compositions shaped by daylight, blinds position, and correlated color temperature (CCT) influence users’ affective state and spatial perception. It proposes an interactive lighting system that adapts in real time to environmental conditions and user feedback. Through a four-phase mixed-methods study involving perceptual assessments, experimental evaluations, and system prototyping in real-world settings, the research identifies lighting strategies that support positive affective outcomes and enhance user experience. The final system integrates environmental sensing and reinforcement learning to deliver adaptive, user-centered lighting tailored to dynamic office environments.

This dissertation contributes to the field by advancing our understanding of how lighting influences affective and perceptual experience in offices and by demonstrating the potential of adaptive, sensor-driven systems to create emotionally responsive indoor environments.