Sheng Long

Sheng Long is a Ph.D. researcher at Northwestern University, affiliated with the Mu Collective research group. He is advised by Professor Matthew Kay. Previously, he studied at William & Mary, majoring in Computer Science and Economics.

His research focuses on information visualization and human-computer interaction, particularly on theoretical frameworks for understanding abstract visual data and fairness-related questions. He contributes to fields like algorithmic collusion, signal detection theory, and statistical visualization design.

Sheng's publications span top venues including CHI and BELIV, covering topics like deep-feature-based similarity metrics, y-axis truncation, and algorithmic fairness. His work often combines computer science with economics and cognitive psychology perspectives.

He has served as a teaching assistant for courses like Mathematical Foundations of Computer Science, Human-Computer Interaction, and Interactive Information Visualization at Northwestern. His academic activities also include collaboration with interdisciplinary teams at the Mu Collective and participation in workshops on visualization methodology.