Riccardo Molle

Riccardo Molle is a Professor of Mathematical Analysis at the Department of Mathematics, University of Rome Tor Vergata, where he teaches Analysis 1 for Engineering students and Differential Equations for Mathematics degree students. With an extensive publication record spanning from 2013 to 2025, Professor Molle is an active researcher in the field of nonlinear partial differential equations.

Professor Molle's research focuses on nonlinear Partial Differential Equations and systems of elliptic type, employing methods from nonlinear analysis including min-max methods, concentration-compactness, Lusternik-Schnirelmann category, degree theory, barycenter maps, and minimization methods. His specific interests include prescribed mass problems, Schrödinger-Poisson systems, Choquard equations, the Bahri-Lions conjecture, existence of infinitely many solutions for nonlinear Schrödinger equations, analysis of the Fučík spectrum, critical and supercritical nonlinearities in bounded and unbounded domains, and variational inequalities.

Professor Molle's recent publications demonstrate a consistent research trajectory in nonlinear PDEs, with particular emphasis on elliptic equations with supercritical growth, normalized solutions for Schrödinger equations, and the existence of multiple solutions for various nonlinear systems. His work frequently appears in high-impact journals including Calculus of Variations and Partial Differential Equations, Journal of Differential Equations, and Proceedings of the Royal Society of Edinburgh.

Professor Molle has maintained an active teaching schedule, offering Analysis 1 courses for Engineering students across multiple academic years (2019-2025), as well as specialized courses including Differential Equations for Mathematics students and PhD-level courses on nonlinear analysis and elliptic PDEs. His teaching philosophy emphasizes deep understanding of mathematical concepts rather than mere memorization, as reflected in his course materials which stress comprehension over rote learning.