Chin Tseng

I'm Chin Tseng, a final-year Ph.D. researcher in Computer Science at the University of North Carolina at Chapel Hill, advised by Prof. Danielle Szafir. My research lies at the intersection of human–computer interaction (HCI), human perception, and information visualization, studying how people interpret and reason about complex information through interactive interfaces. Through large-scale empirical studies, I investigate how perceptual and cognitive mechanisms shape user performance, and how widely used design heuristics hold up under realistic complexity. Specific threads include how color, shape, and redundant encodings support analysis of categorical data — and how those findings translate into design tools and recommendations practitioners can act on. Beyond controlled experiments, I work across the full HCI research lifecycle — designing user studies, running mixed-methods research at scale, conducting analyses that surface actionable insights, and translating those findings into recommendations, design tools, and interactive systems. I've shipped data-driven design tools that help practitioners make informed encoding decisions, and built interactive components for real-world analytics workflows. More broadly, I am motivated by HCI questions around how interfaces can better align with human perception, cognition, and decision-making. My goal is to contribute empirically grounded methods and systems that bridge theory, design, and practice in human-centered computing. Looking forward, my research extends to human-centered interface design for AI systems, focusing on how interaction and visualization can help people interpret, verify, and reason with model-generated outputs. Before my Ph.D., I earned my M.S. in Computer Science from NYU and my B.S. from National Tsing Hua University in Taiwan, with an exchange year at TU Dresden. I also enjoy building interactive systems and tools in practice, having worked as a research intern at Epsilon and as a software engineer in industry. A few things that show up in everything I do: I love building, I'm curious about why people behave the way they do, and I find real joy in digging into mechanisms — what causes what, what predicts what. My favorite part of the work is the back-and-forth between empirical rigor and creative tool-building, and getting them to feed each other.