Chaosheng Dong

Papers

Total Citations

H-Index

About

Chaosheng Dong is a researcher whose work sits at the intersection of machine learning, optimization, and financial decision-making. His primary research focuses on inverse optimization—a powerful framework for inferring hidden preferences and decision-making rules from observed behavior. Dong’s most-cited paper, "Learning risk preferences from investment portfolios using inverse optimization" (2023, 14 citations), exemplifies this contribution by developing a method to reverse-engineer an investor’s risk tolerance directly from their portfolio choices. This work has practical implications for personalized financial advising and robo-advisory systems, offering a data-driven way to model human behavior under uncertainty. Beyond this flagship study, Dong’s broader research explores how optimization algorithms can learn from human actions, bridging the gap between normative models and real-world decision-making. His approach is notable for its elegance and applicability, providing tools that can be used in finance, operations research, and artificial intelligence. With a growing citation footprint, Dong is establishing himself as a key voice in the emerging field of learning from optimal behavior, making his work essential reading for students and researchers interested in the intersection of optimization, machine learning, and behavioral economics.