Research

Vision and Expertise

My expertise is in human-behaviour learning, comprised of: machine learning, reinforcement learning, control systems, neural networks, and system modeling and identification. In particular, I design future complementary learning architectures inspired in humans' brain activity and their expertise for complex-decision making. The application areas include:

• Reinforcement Learning for Optimal Decision Making

• Complementary Learning for Experience Transference/Inference

• Encoding Temporal Difference Error

• Complementary Learning based on Semi-Supervised Learning Architectures.

I am particularly interested in designing novel drone intention prediction algorithms that combines the merits of data-driven methods and physics informed models to enhance prediction capabilities. The applications areas include:

• Trajectory Inference algorithms based on non-cooperative sensors.

• Physics informed models.

• Recurrent Neural Networks and Reservoir Computing.

• Objective function extraction of control policies.

• Complementary Learning: Data-driven methods and physics informed models.