Mehrdad Saeidi

Research Overview

My research focuses on artificial intelligence for autonomous agents, with specific interest in reinforcement learning, wireless sensing, localisation, and multi-sensor fusion for navigation in complex and GPS-challenged environments. I also investigate human-inspired behavioural models through computational Pavlovian and instrumental conditioning, studying how agents develop cue–outcome associations and adapt their decision-making policies.

I am currently involved in the PNRR–PRIN 2022 project on AI-driven sensing and navigation for networks of autonomous agents. My work includes the design of learning-based sensing and localisation algorithms, the development of simulation frameworks in MATLAB, and contributions to experimental validation and scientific publications in collaboration with academic and industrial partners.

• Artificial Intelligence

• Machine Learning

• Reinforcement Learning

• Autonomous Agents

• Multi-Agent Systems

• Wireless Sensing

• Sensor Fusion

• State Estimation

• Multi-Object Tracking

• UAV Navigation

• GPS-Denied Localisation

Under Review
M. Saeidi et al., "Pavlovian-Inspired Cue-Outcome Association for Autonomous Agent Navigation," submitted to ICASSP, 2025.