My research lies at the intersection of Information Visualization, Human–Computer Interaction, Machine Learning models, and perception & cognition. The overarching goal is to build perceptual, human-centered frameworks that optimize visualization design, improving decision quality and confidence while offering objective guidance for designers. I combine methods from InfoVis and perception & cognitive science with human-centered evaluation to measure, model, and theorize how people perceive data and visual design. This agenda advances practical, empirically grounded design choices and addresses core challenges such as perceptual variability, design optimization, and information accessibility—ultimately delivering tools and principles that make complex data more understandable and decisions more reliable across audiences and domains.