Lyudmila Grigoryeva

Lyudmila Grigoryeva is a Professor at the University of St. Gallen. Her research focuses on reservoir computing, machine learning, and nonlinear dynamical systems. She has contributed to advancing theoretical frameworks for recurrent neural networks, including memory capacity analysis, approximation bounds, and time-series forecasting. Key collaborations include work with Juan-Pablo Ortega Lahuerta and Lukas Gonon on reservoir systems and stochastic processes.

• Research Themes: Infinite-dimensional reservoirs, geometric invariant learning, and parallel-in-time solvers.
• Applications: Macroeconomic forecasting, chaotic attractor learning, and PDE solving.

Her recent work addresses foundational questions in reservoir computing, such as memory capacity evaluation and universal approximation properties. She has also explored interdisciplinary applications in econometrics and computational neuroscience.