Jin Feng

Jin Feng is a Professor in the Department of Mathematics at the University of Kansas. His research focuses on rigorous derivations of hydrodynamic equations through Hamilton-Jacobi theory and stochastic analysis, with applications to continuum mechanics and singular stochastic PDEs. He holds a joint appointment in the College of Liberal Arts and Sciences.

Education details are not explicitly listed, but his work bridges mathematical physics and probability. Research interests include large deviation principles for Markov processes, metric space Hamilton-Jacobi equations, and renormalized solution frameworks for stochastic conservation laws.

Key contributions include a 2006 monograph on large deviations for stochastic processes with Kurtz, and foundational work on viscosity solutions in non-standard spaces. Recent efforts emphasize hydrodynamic limit derivations using geodesic metric space methods outlined in Oberwolfach Reports (2020).

Publications span Communications in Mathematical Physics, Archive for Rational Mechanics and Analysis, and Annals of Probability. His methods integrate optimal transport theory, weak KAM theory, and mass transport principles to address complex fluid dynamics problems.