Qin Li

associate professor

University of Wisconsin-Madison

United States

Research Interests

Explore related searches

Professors in United States Professors in Fluid Mechanics Fluid Mechanics professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Qin Li is an associate professor at the University of Wisconsin-Madison, USA. His research spans several areas including inverse problem-solving techniques, Vlasov-Poisson systems, and kinetic chemotaxis. Recent publications highlight his work on diffusion models, stochastic optimization, and interaction kernels in mean-field opinion dynamics.

Articles (25)

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Collaborators (6)

Stephen Wright

Professor

University of Wisconsin-Madison

UNITED STATES

View details

Borong Zhang

University of Wisconsin-Madison

UNITED STATES

View details

Yunan Yang

Cornell University

UNITED STATES

View details

Alex Townsend

Associate Professor

Cornell University

UNITED STATES

View details

Christian Klingenberg

professor

Julius-Maximilians-Universität Würzburg

GERMANY

View details

Yukun Yue

Van Vleck Visiting Assistant Professor

University of Wisconsin-Madison

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Qin Li

Research Interests

Explore related searches

Contact this professor

About

Articles (25)

Control of instability in a Vlasov-Poisson system through an external electric field

Reconstructing the Kinetic Chemotaxis Kernel Using Macroscopic Data: Well-Posedness and Ill-Posedness

Back-Projection Diffusion: Solving the wideband inverse scattering problem with diffusion models

Unique reconstruction for discretized inverse problems: a random sketching approach via subsampling

Kinetic Chemotaxis Tumbling Kernel Determined from Macroscopic Quantities

Beyond expectations: residual dynamic mode decomposition and variance for stochastic dynamical systems

Reconstruction of the doping profile in Vlasov-Poisson system

Differential Equation–Constrained Optimization with Stochasticity

Inference of Interaction Kernels in Mean-Field Models of Opinion Dynamics

Suppressing instability in a Vlasov–Poisson system by an external electric field through constrained optimization

Swarm-Based Gradient Descent Meets Simulated Annealing

Estimating the time-evolving refractivity of a turbulent medium using optical beam measurements: a data assimilation approach

High-Frequency Limit of the Inverse Scattering Problem: Asymptotic Convergence from Inverse Helmholtz to Inverse Liouville

Monte Carlo Gradient in Optimization Constrained by Radiative Transport Equation

Scintillation minimization versus intensity maximization in optimal beams

Manifold Learning and Nonlinear Homogenization

Unique Reconstruction of the Heat-Reflection Indices at Solid Interfaces

Second-order diffusion limit for the phonon transport equation: asymptotics and numerics

Reconstructing the Thermal Phonon Transmission Coefficient at Solid Interfaces in the Phonon Transport Equation

Bayesian Instability of Optical Imaging: Ill Conditioning of Inverse Linear and Nonlinear Radiative Transfer Equation in the Fluid Regime

Bridging and Improving Theoretical and Computational Electrical Impedance Tomography via Data Completion

Constrained Ensemble Langevin Monte Carlo

Computation of optimal beams in weak turbulence

Multiscale Convergence of the Inverse Problem for Chemotaxis in the Bayesian Setting

Semiclassical limit of an inverse problem for the Schrödinger equation

Collaborators (6)

Stephen Wright

Borong Zhang

Yunan Yang

Alex Townsend

Christian Klingenberg

Yukun Yue

Make your next move.