Tianyu Wang

Tianyu Wang works on decision sciences, focusing on mathematical topics such as optimization algorithms (including zeroth-order gradient descent methods), bandit algorithms (methods for making sequential decisions under uncertainty), and statistical/dynamical systems theory. Recent work also extends into applied and computational areas, including large language model (LLM)-based multi-agent systems and optimization tools. Prospective students would likely engage with a mix of theoretical work in optimization and probability alongside applied projects involving machine learning and AI systems.

Publication output has grown steadily over the last decade, rising from about 2 papers per year in 2017 to a sustained average of roughly 8-11 papers per year since 2020.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026