Fei Lu

Associate Professor, Department of Mathematics, Johns Hopkins University

Note: The links are for the slides.

Learning from ensemble data

• 2025/3, IMSI Self-test loss functions for learning weak-form operators and gradient flows

Learning/inverse problems with nonlocal dependence

• 2024/12, U Chicago Data-adaptive RKHS regularization for learning kernels in operators
• 2024/10, U Houston Data-adaptive RKHS regularization for learning kernels in operators
• 2024/9, ECE@JHU Data-adaptive RKHS regularization for learning kernels in operators
• 2024/5, Caltech Data-adaptive RKHS regularization for learning kernels in operators
• 2024/3, University of Maryland Data-adaptive RKHS regularization for learning kernels in operators
• 2023/7, Great Bay Univ. mini-course: Introduction to Nonparametric Learning of Kernels in Operators
• 2022/8, ORNL DARTR for learning kernels in operators
• 2022/8, MSML3 DARTR for learning kernels in operators
• 2022/3, UMass Amherst Learning interacting kernels in mean-field equations and DARTR

Nonparametric inference and statistical learning

• 2023/9, IIT Statistical learning and inverse problems from interacting particle systems
• 2023/7, ICMSEC, CAS Inverse problems for mean-field equations of interacting particles
• 2023/6, HUST Statistical learning and inverse problems from interacting particle systems
• 2023/1, CAM colloquium, PSU Statistical learning and inverse problems from interacting particle systems
• 2022/11, AMS JHU Statistical learning and inverse problems from interacting particle systems
• 2022/9, Fields Institute, Toronto Nonparametric learning of interaction kernels in interacting particle systems
• 2022/8, BIMCR mini-course: Mathematical foundations of learning kernels in Operators
• 2022/3, UMass Amherst Learning interacting kernels in mean-field equations and DARTR
• 2021/8, Europe Dynamics Day Learning interacting kernels in mean-field equations
• 2021/7, SIAM AN21 Clustering prediction of opinion dynamics
• 2021/5, SIAM DS21 Learning interacting kernels of mean-field equations of 1st-order particle systems
• 2019/11, UPenn Nonparametric inference of interaction laws in particles/agent systems
• 2019/2, UMD Nonparametric inference of interaction laws in particles/agent systems

Stochastic model reduction and data assimilation

• 2022/7, SIAM AN22, Pittsburg Shock trace prediction by reduced models for stochastic Burgers equation
• 2022/7, Virginia Tech Stochastic ROM closure Accurate ROMs for Industrial Applications at Virginia Tech
• 2022/6, CAS-AMSS A statistical learning perspective of data-driven model reduction
• 2021/3, Virginia Tech and Univ. of Kansas Stochastic model reduction of nonlinear dynamics by inference
• 2020/9, 2nd Symposium on Machine Learning and Dynamical Systems Data-driven model reduction for stochastic Burgers equations
• 2019/5, SIAM-DS, Snowbird,Utah SMR: from nonlinear Galerkin to parametric inference
• 2019/5, SIAM Snowbird, Utah Data assimilation with stochastic model reduction
• 2018/4, SIAM UQ, Grove Garden, CA Data assimilation with stochastic reduced models
• 2017/11, Banff, Canada Data assimilation with stochastic model reduction

SPDE in Climate modeling

• 2019/5, SIAM Snowbird, Utah Joint parameter-state estimation for a nonlinear SPDE
• 2019/1, JMM, Baltimore, MD Joint parameter-state estimation for a stochastic energy balance model