Mohammad Parhizkar
Papers
3
Total Citations
16
H-Index
2
About
Mohammad Parhizkar’s research lies at the fascinating intersection of biological self-organisation and swarm robotics, drawing inspiration from the social amoeba *Dictyostelium discoideum*. His major contribution is the development of agent-based models that capture both first- and second-order emergent collective behaviours during the amoeba’s aggregation and migration phases. By simulating how individual cells signal starvation, recruit partners, and coordinate movement to form a single super-organism (fruiting body), Parhizkar provides a computational framework for understanding decentralised decision-making in nature. His most cited work, an agent-based model from 2018 (11 citations), systematically unpacks these phases, while his 2015 paper (3 citations) explicitly translates these biological principles into design inspiration for swarm robotics—envisioning robot collectives that can self-assemble, communicate, and navigate without central control. Though citation counts are modest, the novelty of bridging microbiology and robotics is notable, offering a blueprint for resilient, adaptive multi-agent systems. Parhizkar’s work is a compelling example of how observing slime moulds can inform the future of autonomous swarms.
Research Focus
Key Achievements
Top Papers
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