Projects
Research Directions
My research spans mathematical foundations, scientific machine learning, dynamical systems, and learning-based control.
Core Research Areas
Scientific Machine Learning
Mathematical foundations and computational methods combining scientific models with machine learning.
Neural Differential Equations
Continuous-depth neural networks, neural ODEs, and learning-based dynamical systems.
Physics-Informed Learning
Physics-informed and theory-informed learning methods for differential equations.
Control and Dynamical Systems
Learning-based control, nonlinear systems, stability, and systems under uncertainty.
Model Predictive Control
Robust MPC, Tube MPC, and learning-based control policies.
Fractional-Order Systems
Fractional calculus, fractional differential equations, and fractional dynamics.
Operator Learning
Neural operators and learning mappings between infinite-dimensional function spaces.
Scientific Computing
Numerical analysis, stochastic differential equations, optimization, and computational mathematics.
Research Collaboration
Researchers interested in collaboration in any of the above areas are welcome to contact me.