Shanshan Hu

Comic-style chalkboard portrait representing Shanshan Hu

Doctoral Researcher
TU Berlin

Research profile

Shanshan Hu studies stochastic differential equations, mean-field systems and random dynamical systems. Her research focuses on the asymptotic behaviour of McKean–Vlasov stochastic differential equations, using probabilistic methods as well as random dynamical systems techniques, with a particular focus on synchronisation.

Research areas

Stochastic Dynamics · Sampling · Interacting Particle Systems

Five research keywords

McKean–Vlasov equations · random dynamical systems · rough paths · multiscale diffusions · sampling

Contact and links

Public email

shanshan.hu@tu-berlin.de

Institution

TU Berlin

Academic links

arXiv author profile

Curriculum vitae

DateAppointment / education
January 2025–presentDoctoral researcher, Stochastic Analysis in the Sciences, Technische Universität Berlin; member of the FluCo team.
March–September 2024Visiting student, Seoul National University, South Korea.
January 2023–January 2025Doctoral researcher, International Research Training Group 2235, Bielefeld University.

Publications

This catalogue contains all publications by the member, including work from before joining SAiS. Journal versions and preprints are maintained as one record per scientific work.

  • Weak synchronisation for McKean–Vlasov SDEs

    Synchronisation by noise for McKean–Vlasov stochastic differential equations is investigated. A transfer principle is introduced by which synchronisation by noise and diagonal mixing can be transferred from an associated limiting frozen-diffusion SDE to a genuinely law-dependent McKean–Vlasov SDE. The usefulness…

  • Asymptotics of Multi-Scale McKean–Vlasov Diffusions with Super-Linear Kernels: a Lifted Semigroup Approach

    In this work, we establish the small-noise asymptotic behaviour (namely, the functional law of large numbers and the large deviation principle) for multi-scale McKean–Vlasov diffusions with super-linear kernels. In this setting, the interaction depends on the laws of both the slow component…

  • Random dynamical systems for McKean–Vlasov SDEs via rough path theory

    The existence of random dynamical systems for McKean–Vlasov SDEs is established. This is approached by considering the joint dynamics of the corresponding nonlinear Fokker-Planck equation governing the law of the system and the underlying stochastic differential equation (SDE) as a dynamical system…

  • McKean-Vlasov SDE and SPDE with Locally Monotone Coefficients

    Published · Annals of Applied Probability 34(2), 2136–2189 (2024). In this paper we mainly investigate the strong and weak well-posedness of a class of McKean-Vlasov stochastic (partial) differential equations. The main existence and uniqueness results state that we only need to…

Current SAiS projects

FluCo
Doctoral researcher in the ERC-funded team.

CRC/TRR 388 project A11
Associated research connection; not listed as the project’s funded SAiS researcher.

Talks and posters

DateEvent
September 2026DMV Annual Meeting, Konstanz.
July 202615th AIMS Conference, Athens.
January 2026Applied Analysis, Complex Systems & Dynamics Seminar, University of Graz.
October 2025Probability Research Seminar, Tianjin University.
October 2025Probability Research Seminar, Jiangsu Normal University.
September 2025Young Researchers in Stochastic Analysis and Stochastic Geometric Analysis, EPFL.
July 2025Research Seminar Rough Analysis and Stochastic Dynamics, TU Berlin.
July 202521st Oxford–Berlin Young Researchers Meeting on Applied Stochastic Analysis, WIAS.
September 2024Probability Research Seminar, Jiangsu Normal University.
June 2024Probability Research Seminar, Seoul National University.

Teaching

TermTeaching activity
Summer 2026Teaching assistant, Measure Theory.
Winter 2025/26Teaching assistant, Stochastic Models.
Summer 2025Teaching assistant, Measure Theory.
Winter 2023/24Teaching assistant, Stochastic Partial Differential Equations.
Summer 2023Teaching assistant, Stochastic Partial Differential Equations.