Daniel Walter

Prof. Daniel Walter is a Junior Professor at the Institute of Mathematics, Humboldt University of Berlin, within the Faculty of Mathematics and Natural Sciences. His research focuses on non-smooth optimization, optimal control, and numerical analysis of partial differential equations (PDEs). He specializes in developing advanced methods for sensor placement in inverse problems and stabilizing control systems using computational techniques.

His work integrates theoretical analysis with practical applications, addressing challenges in feedback stabilization, convergence of optimization algorithms, and data-driven approaches. Notable contributions include studies on extremal points in optimization norms, linear convergence rates of conditional gradient methods, and semiglobal stabilization using neural networks.

Key Research Areas:

• Non-smooth optimization and sparse methods
• PDE-constrained optimization and control
• Inverse problems and sensor placement strategies
• Numerical methods for parabolic and elliptic equations

Recent publications emphasize algorithmic advancements in optimization, with a focus on acceleration and convergence guarantees. His interdisciplinary approach bridges mathematical theory with engineering applications, particularly in stabilization and parameter estimation.

Advising & Grants: While no specific grants or advisees are listed, his research indicates active involvement in training through cutting-edge projects in optimization and control.