Petia Koprinkova‐Hristova

About

Petia Koprinkova‐Hristova is a leading researcher in computational intelligence, with a primary focus on recurrent neural networks, reservoir computing, and adaptive learning systems. Her pioneering work bridges the gap between neural network theory and real-time control, most notably through her 2010 study on "Adaptive Critic Design with Echo State Network," which has garnered 23 citations. In this seminal paper, she introduced a novel framework that integrates Echo State Networks (ESNs) into reinforcement learning schemes, enabling efficient on-line training of adaptive critics—a breakthrough for dynamic system control. Her research extends to embodied cognition, as seen in her 2014 exploration of "Learning of embodied interaction dynamics with recurrent neural networks," where she investigates how intelligence emerges from the coupling of agents with their environments, earning 4 citations. Additionally, her work on "On-line Training of ESN and IP Tuning Effect" (2 citations) advances the practical tuning of reservoir parameters for improved performance. Koprinkova‐Hristova’s contributions are instrumental in developing adaptive, brain-inspired algorithms for robotics and autonomous systems, making her a key figure in the evolution of intelligent, interactive machines.

Research Focus

Key Achievements

Top Papers

1. 1
   Adaptive Critic Design with Echo State Network

   23 citations · 2010
2. 2
   Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments

   4 citations · 2014
3. 3
   On-line Training of ESN and IP Tuning Effect

   2 citations · 2014

Key Collaborators

Contact & Links